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Publisher: Elsevier   (Total: 3161 journals)

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Showing 1 - 200 of 3161 Journals sorted alphabetically
A Practical Logic of Cognitive Systems     Full-text available via subscription   (Followers: 9)
AASRI Procedia     Open Access   (Followers: 15)
Academic Pediatrics     Hybrid Journal   (Followers: 35, SJR: 1.655, CiteScore: 2)
Academic Radiology     Hybrid Journal   (Followers: 24, SJR: 1.015, CiteScore: 2)
Accident Analysis & Prevention     Partially Free   (Followers: 97, SJR: 1.462, CiteScore: 3)
Accounting Forum     Hybrid Journal   (Followers: 27, SJR: 0.932, CiteScore: 2)
Accounting, Organizations and Society     Hybrid Journal   (Followers: 37, SJR: 1.771, CiteScore: 3)
Achievements in the Life Sciences     Open Access   (Followers: 5)
Acta Anaesthesiologica Taiwanica     Open Access   (Followers: 7)
Acta Astronautica     Hybrid Journal   (Followers: 418, SJR: 0.758, CiteScore: 2)
Acta Automatica Sinica     Full-text available via subscription   (Followers: 2)
Acta Biomaterialia     Hybrid Journal   (Followers: 28, SJR: 1.967, CiteScore: 7)
Acta Colombiana de Cuidado Intensivo     Full-text available via subscription   (Followers: 2)
Acta de Investigación Psicológica     Open Access   (Followers: 3)
Acta Ecologica Sinica     Open Access   (Followers: 10, SJR: 0.18, CiteScore: 1)
Acta Haematologica Polonica     Free   (Followers: 1, SJR: 0.128, CiteScore: 0)
Acta Histochemica     Hybrid Journal   (Followers: 3, SJR: 0.661, CiteScore: 2)
Acta Materialia     Hybrid Journal   (Followers: 266, SJR: 3.263, CiteScore: 6)
Acta Mathematica Scientia     Full-text available via subscription   (Followers: 5, SJR: 0.504, CiteScore: 1)
Acta Mechanica Solida Sinica     Full-text available via subscription   (Followers: 9, SJR: 0.542, CiteScore: 1)
Acta Oecologica     Hybrid Journal   (Followers: 12, SJR: 0.834, CiteScore: 2)
Acta Otorrinolaringologica (English Edition)     Full-text available via subscription  
Acta Otorrinolaringológica Española     Full-text available via subscription   (Followers: 3, SJR: 0.307, CiteScore: 0)
Acta Pharmaceutica Sinica B     Open Access   (Followers: 1, SJR: 1.793, CiteScore: 6)
Acta Poética     Open Access   (Followers: 4, SJR: 0.101, CiteScore: 0)
Acta Psychologica     Hybrid Journal   (Followers: 27, SJR: 1.331, CiteScore: 2)
Acta Sociológica     Open Access   (Followers: 1)
Acta Tropica     Hybrid Journal   (Followers: 6, SJR: 1.052, CiteScore: 2)
Acta Urológica Portuguesa     Open Access  
Actas Dermo-Sifiliograficas     Full-text available via subscription   (Followers: 3, SJR: 0.374, CiteScore: 1)
Actas Dermo-Sifiliográficas (English Edition)     Full-text available via subscription   (Followers: 2)
Actas Urológicas Españolas     Full-text available via subscription   (Followers: 3, SJR: 0.344, CiteScore: 1)
Actas Urológicas Españolas (English Edition)     Full-text available via subscription   (Followers: 1)
Actualites Pharmaceutiques     Full-text available via subscription   (Followers: 6, SJR: 0.19, CiteScore: 0)
Actualites Pharmaceutiques Hospitalieres     Full-text available via subscription   (Followers: 3)
Acupuncture and Related Therapies     Hybrid Journal   (Followers: 8)
Acute Pain     Full-text available via subscription   (Followers: 14, SJR: 2.671, CiteScore: 5)
Ad Hoc Networks     Hybrid Journal   (Followers: 11, SJR: 0.53, CiteScore: 4)
Addictive Behaviors     Hybrid Journal   (Followers: 17, SJR: 1.29, CiteScore: 3)
Addictive Behaviors Reports     Open Access   (Followers: 8, SJR: 0.755, CiteScore: 2)
Additive Manufacturing     Hybrid Journal   (Followers: 11, SJR: 2.611, CiteScore: 8)
Additives for Polymers     Full-text available via subscription   (Followers: 23)
Advanced Drug Delivery Reviews     Hybrid Journal   (Followers: 163, SJR: 4.09, CiteScore: 13)
Advanced Engineering Informatics     Hybrid Journal   (Followers: 12, SJR: 1.167, CiteScore: 4)
Advanced Powder Technology     Hybrid Journal   (Followers: 17, SJR: 0.694, CiteScore: 3)
Advances in Accounting     Hybrid Journal   (Followers: 8, SJR: 0.277, CiteScore: 1)
Advances in Agronomy     Full-text available via subscription   (Followers: 15, SJR: 2.384, CiteScore: 5)
Advances in Anesthesia     Full-text available via subscription   (Followers: 28, SJR: 0.126, CiteScore: 0)
Advances in Antiviral Drug Design     Full-text available via subscription   (Followers: 2)
Advances in Applied Mathematics     Full-text available via subscription   (Followers: 10, SJR: 0.992, CiteScore: 1)
Advances in Applied Mechanics     Full-text available via subscription   (Followers: 11, SJR: 1.551, CiteScore: 4)
Advances in Applied Microbiology     Full-text available via subscription   (Followers: 24, SJR: 2.089, CiteScore: 5)
Advances In Atomic, Molecular, and Optical Physics     Full-text available via subscription   (Followers: 14, SJR: 0.572, CiteScore: 2)
Advances in Biological Regulation     Hybrid Journal   (Followers: 4, SJR: 2.61, CiteScore: 7)
Advances in Botanical Research     Full-text available via subscription   (Followers: 2, SJR: 0.686, CiteScore: 2)
Advances in Cancer Research     Full-text available via subscription   (Followers: 33, SJR: 3.043, CiteScore: 6)
Advances in Carbohydrate Chemistry and Biochemistry     Full-text available via subscription   (Followers: 9, SJR: 1.453, CiteScore: 2)
Advances in Catalysis     Full-text available via subscription   (Followers: 5, SJR: 1.992, CiteScore: 5)
Advances in Cell Aging and Gerontology     Full-text available via subscription   (Followers: 4)
Advances in Cellular and Molecular Biology of Membranes and Organelles     Full-text available via subscription   (Followers: 13)
Advances in Chemical Engineering     Full-text available via subscription   (Followers: 27, SJR: 0.156, CiteScore: 1)
Advances in Child Development and Behavior     Full-text available via subscription   (Followers: 10, SJR: 0.713, CiteScore: 1)
Advances in Chronic Kidney Disease     Full-text available via subscription   (Followers: 10, SJR: 1.316, CiteScore: 2)
Advances in Clinical Chemistry     Full-text available via subscription   (Followers: 26, SJR: 1.562, CiteScore: 3)
Advances in Colloid and Interface Science     Full-text available via subscription   (Followers: 19, SJR: 1.977, CiteScore: 8)
Advances in Computers     Full-text available via subscription   (Followers: 14, SJR: 0.205, CiteScore: 1)
Advances in Dermatology     Full-text available via subscription   (Followers: 15)
Advances in Developmental Biology     Full-text available via subscription   (Followers: 12)
Advances in Digestive Medicine     Open Access   (Followers: 9)
Advances in DNA Sequence-Specific Agents     Full-text available via subscription   (Followers: 7)
Advances in Drug Research     Full-text available via subscription   (Followers: 25)
Advances in Ecological Research     Full-text available via subscription   (Followers: 44, SJR: 2.524, CiteScore: 4)
Advances in Engineering Software     Hybrid Journal   (Followers: 28, SJR: 1.159, CiteScore: 4)
Advances in Experimental Biology     Full-text available via subscription   (Followers: 8)
Advances in Experimental Social Psychology     Full-text available via subscription   (Followers: 46, SJR: 5.39, CiteScore: 8)
Advances in Exploration Geophysics     Full-text available via subscription   (Followers: 1)
Advances in Fluorine Science     Full-text available via subscription   (Followers: 9)
Advances in Food and Nutrition Research     Full-text available via subscription   (Followers: 60, SJR: 0.591, CiteScore: 2)
Advances in Fuel Cells     Full-text available via subscription   (Followers: 16)
Advances in Genetics     Full-text available via subscription   (Followers: 18, SJR: 1.354, CiteScore: 4)
Advances in Genome Biology     Full-text available via subscription   (Followers: 10, SJR: 12.74, CiteScore: 13)
Advances in Geophysics     Full-text available via subscription   (Followers: 6, SJR: 1.193, CiteScore: 3)
Advances in Heat Transfer     Full-text available via subscription   (Followers: 24, SJR: 0.368, CiteScore: 1)
Advances in Heterocyclic Chemistry     Full-text available via subscription   (Followers: 12, SJR: 0.749, CiteScore: 3)
Advances in Human Factors/Ergonomics     Full-text available via subscription   (Followers: 23)
Advances in Imaging and Electron Physics     Full-text available via subscription   (Followers: 2, SJR: 0.193, CiteScore: 0)
Advances in Immunology     Full-text available via subscription   (Followers: 36, SJR: 4.433, CiteScore: 6)
Advances in Inorganic Chemistry     Full-text available via subscription   (Followers: 8, SJR: 1.163, CiteScore: 2)
Advances in Insect Physiology     Full-text available via subscription   (Followers: 2, SJR: 1.938, CiteScore: 3)
Advances in Integrative Medicine     Hybrid Journal   (Followers: 6, SJR: 0.176, CiteScore: 0)
Advances in Intl. Accounting     Full-text available via subscription   (Followers: 3)
Advances in Life Course Research     Hybrid Journal   (Followers: 8, SJR: 0.682, CiteScore: 2)
Advances in Lipobiology     Full-text available via subscription   (Followers: 1)
Advances in Magnetic and Optical Resonance     Full-text available via subscription   (Followers: 8)
Advances in Marine Biology     Full-text available via subscription   (Followers: 18, SJR: 0.88, CiteScore: 2)
Advances in Mathematics     Full-text available via subscription   (Followers: 11, SJR: 3.027, CiteScore: 2)
Advances in Medical Sciences     Hybrid Journal   (Followers: 7, SJR: 0.694, CiteScore: 2)
Advances in Medicinal Chemistry     Full-text available via subscription   (Followers: 5)
Advances in Microbial Physiology     Full-text available via subscription   (Followers: 4, SJR: 1.158, CiteScore: 3)
Advances in Molecular and Cell Biology     Full-text available via subscription   (Followers: 23)
Advances in Molecular and Cellular Endocrinology     Full-text available via subscription   (Followers: 8)
Advances in Molecular Toxicology     Full-text available via subscription   (Followers: 7, SJR: 0.182, CiteScore: 0)
Advances in Nanoporous Materials     Full-text available via subscription   (Followers: 3)
Advances in Oncobiology     Full-text available via subscription   (Followers: 2)
Advances in Organ Biology     Full-text available via subscription   (Followers: 2)
Advances in Organometallic Chemistry     Full-text available via subscription   (Followers: 17, SJR: 1.875, CiteScore: 4)
Advances in Parallel Computing     Full-text available via subscription   (Followers: 7, SJR: 0.174, CiteScore: 0)
Advances in Parasitology     Full-text available via subscription   (Followers: 5, SJR: 1.579, CiteScore: 4)
Advances in Pediatrics     Full-text available via subscription   (Followers: 24, SJR: 0.461, CiteScore: 1)
Advances in Pharmaceutical Sciences     Full-text available via subscription   (Followers: 12)
Advances in Pharmacology     Full-text available via subscription   (Followers: 16, SJR: 1.536, CiteScore: 3)
Advances in Physical Organic Chemistry     Full-text available via subscription   (Followers: 8, SJR: 0.574, CiteScore: 1)
Advances in Phytomedicine     Full-text available via subscription  
Advances in Planar Lipid Bilayers and Liposomes     Full-text available via subscription   (Followers: 3, SJR: 0.109, CiteScore: 1)
Advances in Plant Biochemistry and Molecular Biology     Full-text available via subscription   (Followers: 10)
Advances in Plant Pathology     Full-text available via subscription   (Followers: 5)
Advances in Porous Media     Full-text available via subscription   (Followers: 5)
Advances in Protein Chemistry     Full-text available via subscription   (Followers: 19)
Advances in Protein Chemistry and Structural Biology     Full-text available via subscription   (Followers: 20, SJR: 0.791, CiteScore: 2)
Advances in Psychology     Full-text available via subscription   (Followers: 65)
Advances in Quantum Chemistry     Full-text available via subscription   (Followers: 6, SJR: 0.371, CiteScore: 1)
Advances in Radiation Oncology     Open Access   (Followers: 1, SJR: 0.263, CiteScore: 1)
Advances in Small Animal Medicine and Surgery     Hybrid Journal   (Followers: 3, SJR: 0.101, CiteScore: 0)
Advances in Space Biology and Medicine     Full-text available via subscription   (Followers: 6)
Advances in Space Research     Full-text available via subscription   (Followers: 406, SJR: 0.569, CiteScore: 2)
Advances in Structural Biology     Full-text available via subscription   (Followers: 5)
Advances in Surgery     Full-text available via subscription   (Followers: 12, SJR: 0.555, CiteScore: 2)
Advances in the Study of Behavior     Full-text available via subscription   (Followers: 34, SJR: 2.208, CiteScore: 4)
Advances in Veterinary Medicine     Full-text available via subscription   (Followers: 18)
Advances in Veterinary Science and Comparative Medicine     Full-text available via subscription   (Followers: 14)
Advances in Virus Research     Full-text available via subscription   (Followers: 5, SJR: 2.262, CiteScore: 5)
Advances in Water Resources     Hybrid Journal   (Followers: 47, SJR: 1.551, CiteScore: 3)
Aeolian Research     Hybrid Journal   (Followers: 6, SJR: 1.117, CiteScore: 3)
Aerospace Science and Technology     Hybrid Journal   (Followers: 351, SJR: 0.796, CiteScore: 3)
AEU - Intl. J. of Electronics and Communications     Hybrid Journal   (Followers: 8, SJR: 0.42, CiteScore: 2)
African J. of Emergency Medicine     Open Access   (Followers: 6, SJR: 0.296, CiteScore: 0)
Ageing Research Reviews     Hybrid Journal   (Followers: 11, SJR: 3.671, CiteScore: 9)
Aggression and Violent Behavior     Hybrid Journal   (Followers: 465, SJR: 1.238, CiteScore: 3)
Agri Gene     Hybrid Journal   (Followers: 1, SJR: 0.13, CiteScore: 0)
Agricultural and Forest Meteorology     Hybrid Journal   (Followers: 17, SJR: 1.818, CiteScore: 5)
Agricultural Systems     Hybrid Journal   (Followers: 31, SJR: 1.156, CiteScore: 4)
Agricultural Water Management     Hybrid Journal   (Followers: 42, SJR: 1.272, CiteScore: 3)
Agriculture and Agricultural Science Procedia     Open Access   (Followers: 4)
Agriculture and Natural Resources     Open Access   (Followers: 3)
Agriculture, Ecosystems & Environment     Hybrid Journal   (Followers: 57, SJR: 1.747, CiteScore: 4)
Ain Shams Engineering J.     Open Access   (Followers: 5, SJR: 0.589, CiteScore: 3)
Air Medical J.     Hybrid Journal   (Followers: 6, SJR: 0.26, CiteScore: 0)
AKCE Intl. J. of Graphs and Combinatorics     Open Access   (SJR: 0.19, CiteScore: 0)
Alcohol     Hybrid Journal   (Followers: 12, SJR: 1.153, CiteScore: 3)
Alcoholism and Drug Addiction     Open Access   (Followers: 11)
Alergologia Polska : Polish J. of Allergology     Full-text available via subscription   (Followers: 1)
Alexandria Engineering J.     Open Access   (Followers: 1, SJR: 0.604, CiteScore: 3)
Alexandria J. of Medicine     Open Access   (Followers: 1, SJR: 0.191, CiteScore: 1)
Algal Research     Partially Free   (Followers: 10, SJR: 1.142, CiteScore: 4)
Alkaloids: Chemical and Biological Perspectives     Full-text available via subscription   (Followers: 2)
Allergologia et Immunopathologia     Full-text available via subscription   (Followers: 1, SJR: 0.504, CiteScore: 1)
Allergology Intl.     Open Access   (Followers: 5, SJR: 1.148, CiteScore: 2)
Alpha Omegan     Full-text available via subscription   (SJR: 3.521, CiteScore: 6)
ALTER - European J. of Disability Research / Revue Européenne de Recherche sur le Handicap     Full-text available via subscription   (Followers: 10, SJR: 0.201, CiteScore: 1)
Alzheimer's & Dementia     Hybrid Journal   (Followers: 52, SJR: 4.66, CiteScore: 10)
Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring     Open Access   (Followers: 4, SJR: 1.796, CiteScore: 4)
Alzheimer's & Dementia: Translational Research & Clinical Interventions     Open Access   (Followers: 4, SJR: 1.108, CiteScore: 3)
Ambulatory Pediatrics     Hybrid Journal   (Followers: 6)
American Heart J.     Hybrid Journal   (Followers: 53, SJR: 3.267, CiteScore: 4)
American J. of Cardiology     Hybrid Journal   (Followers: 58, SJR: 1.93, CiteScore: 3)
American J. of Emergency Medicine     Hybrid Journal   (Followers: 44, SJR: 0.604, CiteScore: 1)
American J. of Geriatric Pharmacotherapy     Full-text available via subscription   (Followers: 10)
American J. of Geriatric Psychiatry     Hybrid Journal   (Followers: 13, SJR: 1.524, CiteScore: 3)
American J. of Human Genetics     Hybrid Journal   (Followers: 34, SJR: 7.45, CiteScore: 8)
American J. of Infection Control     Hybrid Journal   (Followers: 29, SJR: 1.062, CiteScore: 2)
American J. of Kidney Diseases     Hybrid Journal   (Followers: 35, SJR: 2.973, CiteScore: 4)
American J. of Medicine     Hybrid Journal   (Followers: 48)
American J. of Medicine Supplements     Full-text available via subscription   (Followers: 3, SJR: 1.967, CiteScore: 2)
American J. of Obstetrics and Gynecology     Hybrid Journal   (Followers: 221, SJR: 2.7, CiteScore: 4)
American J. of Ophthalmology     Hybrid Journal   (Followers: 66, SJR: 3.184, CiteScore: 4)
American J. of Ophthalmology Case Reports     Open Access   (Followers: 5, SJR: 0.265, CiteScore: 0)
American J. of Orthodontics and Dentofacial Orthopedics     Full-text available via subscription   (Followers: 6, SJR: 1.289, CiteScore: 1)
American J. of Otolaryngology     Hybrid Journal   (Followers: 25, SJR: 0.59, CiteScore: 1)
American J. of Pathology     Hybrid Journal   (Followers: 28, SJR: 2.139, CiteScore: 4)
American J. of Preventive Medicine     Hybrid Journal   (Followers: 29, SJR: 2.164, CiteScore: 4)
American J. of Surgery     Hybrid Journal   (Followers: 38, SJR: 1.141, CiteScore: 2)
American J. of the Medical Sciences     Hybrid Journal   (Followers: 12, SJR: 0.767, CiteScore: 1)
Ampersand : An Intl. J. of General and Applied Linguistics     Open Access   (Followers: 7)
Anaerobe     Hybrid Journal   (Followers: 4, SJR: 1.144, CiteScore: 3)
Anaesthesia & Intensive Care Medicine     Full-text available via subscription   (Followers: 63, SJR: 0.138, CiteScore: 0)
Anaesthesia Critical Care & Pain Medicine     Full-text available via subscription   (Followers: 18, SJR: 0.411, CiteScore: 1)
Anales de Cirugia Vascular     Full-text available via subscription   (Followers: 1)
Anales de Pediatría     Full-text available via subscription   (Followers: 3, SJR: 0.277, CiteScore: 0)
Anales de Pediatría (English Edition)     Full-text available via subscription  
Anales de Pediatría Continuada     Full-text available via subscription  
Analytic Methods in Accident Research     Hybrid Journal   (Followers: 5, SJR: 4.849, CiteScore: 10)
Analytica Chimica Acta     Hybrid Journal   (Followers: 42, SJR: 1.512, CiteScore: 5)
Analytical Biochemistry     Hybrid Journal   (Followers: 186, SJR: 0.633, CiteScore: 2)
Analytical Chemistry Research     Open Access   (Followers: 12, SJR: 0.411, CiteScore: 2)
Analytical Spectroscopy Library     Full-text available via subscription   (Followers: 12)
Anesthésie & Réanimation     Full-text available via subscription   (Followers: 2)
Anesthesiology Clinics     Full-text available via subscription   (Followers: 23, SJR: 0.683, CiteScore: 2)
Angiología     Full-text available via subscription   (SJR: 0.121, CiteScore: 0)
Angiologia e Cirurgia Vascular     Open Access   (Followers: 1, SJR: 0.111, CiteScore: 0)
Animal Behaviour     Hybrid Journal   (Followers: 204, SJR: 1.58, CiteScore: 3)

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Journal Cover
Advanced Engineering Informatics
Journal Prestige (SJR): 1.167
Citation Impact (citeScore): 4
Number of Followers: 12  
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1474-0346
Published by Elsevier Homepage  [3161 journals]
  • On the role of generating textual description for design intent
           communication in feature-based 3D collaborative design
    • Abstract: Publication date: January 2019Source: Advanced Engineering Informatics, Volume 39Author(s): Yuan Cheng, Fazhi He, Xiao Lv, Weiwei Cai Modern manufacturing firms are more inclining to promote the product quality, save costs and reduce times of product design by both collaborative designing and model reuse. If CAD components constructed collaboratively have information representing their developers’ design intents embedded in the model, people’s understanding over the product should be improved and the product model should be best reused. Until now, capturing, recording and presenting design intents still remains a challenge. It has been shown by empirical studies that textual summarisations can lead to improved decision making. In this paper, we propose an approach to generation the natural language description about design intents of collaboratively developed product. The approach brings together techniques from different areas of collaborative designing, ontology and semantic network, and natural language generation. The language generation process is guided by an information model we established to give a structured description about design intents of collaboratively products. In order to record information related to the design intents, we build a common CAD model ontology and then generate a semantic network to describe dependencies, component structures and design history which are components of the design intent information model. The techniques of natural language generation, namely discourse planning and sentence planning, are adopted for the eventual linguistic generation of design intents. Finally, we use several case studies to prove the advantages of natural language in helping people better understanding the design intents.
  • Healthcare process modularization using design structure matrix
    • Abstract: Publication date: January 2019Source: Advanced Engineering Informatics, Volume 39Author(s): Xiaojin Zhang, Shuang Ma, Songlin Chen The healthcare industry is confronted with the challenge of offering customized services while in the meantime to control increasing healthcare costs. Modularization is an important approach to reduce healthcare costs and improve patient-centered services via decreasing process complexity and enhancing flexibility through configuring pre-identified service modules. Recognizing the importance of modularity for healthcare services, this paper introduces Design Structure Matrix (DSM) as a technique for healthcare process modularization. A DSM-based modularization and sequencing algorithm is developed to allocate healthcare activities to service modules using Genetic Algorithm (GA) and arrange sequences of services both within and across service modules to support modular clinical pathway design. The proposed algorithm is implemented with a case study, the results of which have demonstrated the feasibility and applicability of the proposed DSM-based modularization method for healthcare process design.
  • Applications of 3D point cloud data in the construction industry: A
           fifteen-year review from 2004 to 2018
    • Abstract: Publication date: January 2019Source: Advanced Engineering Informatics, Volume 39Author(s): Qian Wang, Min-Koo Kim 3D point cloud data obtained from laser scans, images, and videos are able to provide accurate and fast records of the 3D geometries of construction-related objects. Thus, the construction industry has been using point cloud data for a variety of purposes including 3D model reconstruction, geometry quality inspection, construction progress tracking, etc. Although a number of studies have been reported on applying point cloud data for the construction industry in the recent decades, there has not been any systematic review that summaries these applications and points out the research gaps and future research directions. This paper, therefore, aims to provide a thorough review on the applications of 3D point cloud data in the construction industry and to provide recommendations on future research directions in this area. A total of 197 research papers were collected in this study through a two-fold literature search, which were published within a fifteen-year period from 2004 to 2018. Based on the collected papers, applications of 3D point cloud data in the construction industry are reviewed according to three categories including (1) 3D model reconstruction, (2) geometry quality inspection, and (3) other applications. Following the literature review, this paper discusses on the acquisition and processing of point cloud data, particularly focusing on how to properly perform data acquisition and processing to fulfill the needs of the intended construction applications. Specifically, the determination of required point cloud data quality and the determination of data acquisition parameters are discussed with regard to data acquisition, and the extraction and utilization of semantic information and the platforms for data visualization and processing are discussed with regard to data processing. Based on the review of applications and the following discussions, research gaps and future research directions are recommended including (1) application-oriented data acquisition, (2) semantic enrichment for as-is BIM, (3) geometry quality inspection in fabrication phase, and (4) real-time visualization and processing.
  • Sensitivity analysis of artificial neural networks for just-suspension
           speed prediction in solid-liquid mixing systems: Performance comparison of
           MLPNN and RBFNN
    • Abstract: Publication date: January 2019Source: Advanced Engineering Informatics, Volume 39Author(s): Shaliza Ibrahim, Choe Earn Choong, Ahmed El-Shafie Just-suspension speed (Njs) is an important parameter for stirred tank design using a solid-liquid mixing system in the chemical process industry. However, current correlations for Njs suffer from uncertainty from limited experimental databases and limitations due to many parameters that play an important role in Njs determination. A comprehensive computation of the radial basis function neural network (RBFNN) was developed based on solid-liquid mixing experiments, which contain 935 datasets for the prediction of Njs. The Njs values were obtained experimentally using Zwietering correlation with different solid loading percentages, solid particle density, solid particle diameter, mixing solvent density, number of impeller blades, impeller diameter, impeller blade hub angle, impeller blade tip angle, the width of the impeller blade and the ratio of the clearance between the impeller and the bottom of the tank with the tank diameter. The RBFNN proved to have a much better ability to accurately predict the desired Njs compared to MLPNN even after decreasing the number of input variables from 11 to 8. Thus, the computational RBFNN model results will be useful for extending the application of a solid-liquid mixing system for estimating the just-suspension speed for stirred tank design.
  • A framework for data-driven informatization of the construction company
    • Abstract: Publication date: January 2019Source: Advanced Engineering Informatics, Volume 39Author(s): Zhijia You, Chen Wu With the advent of big data era, the construction industry has focused on processing large quantities of engineering data and extracting their value. However, inaccurate manual entries and delayed data collection have created difficulties in making full use of information. Meanwhile, difficulty sharing data and weak interoperability of data among business information systems also leaves company headquarters without the resource integration that can facilitate decision making. To overcome these challenges, we proposed a big data infrastructure called the enterprise integrated data platform (EIDP) for use by construction companies. We discuss a case study, and offer a framework for future business improvement that contributes to closed-loop construction supply chain management, cost management and control, knowledge discovery, and decision making. The proposed informatization solution provides a theoretical basis for realizing data sharing and interoperability between business management and project management. On this basis, it will help construction companies to improve the efficiency of both company operations and project delivery by optimizing the business process and supporting decision making.
  • Ontology-based approach for the provision of simulation knowledge acquired
           by Data and Text Mining processes
    • Abstract: Publication date: January 2019Source: Advanced Engineering Informatics, Volume 39Author(s): Philipp Kestel, Patricia Kügler, Christoph Zirngibl, Benjamin Schleich, Sandro Wartzack Numerical simulation techniques such as Finite Element Analyses are essential in today's engineering design practices. However, comprehensive knowledge is required for the setup of reliable simulations to verify strength and further product properties. Due to limited capacities, design-accompanying simulations are performed too rarely by experienced simulation engineers. Therefore, product models are not sufficiently verified or the simulations lead to wrong design decisions, if they are applied by less experienced users. This results in belated redesigns of already detailed product models and to highly cost- and time-intensive iterations in product development.Thus, in order to support less experienced simulation users in setting up reliable Finite Element Analyses, a novel ontology-based approach is presented. The knowledge management tools developed on the basis of this approach allow an automated acquisition and target-oriented provision of necessary simulation knowledge. This knowledge is acquired from existing simulation models and text-based documentations from previous product developments by Text and Data Mining. By offering support to less experienced simulation users, the presented approach may finally lead to a more efficient and extensive application of reliable FEA in product development.
  • Degradation evaluation of lateral story stiffness using HLA-based deep
           learning networks
    • Abstract: Publication date: January 2019Source: Advanced Engineering Informatics, Volume 39Author(s): Cong Zhou, J. Geoffrey Chase, Geoffrey W. Rodgers Hysteresis loop analysis (HLA) has proven an effective indicator of damage detection in civil engineering structural health monitoring (SHM). In this paper, the histogram of stiffness (HOS) features are extracted from segregated half cycles of hysteresis loops reconstructed from measured response. A deep learning network (DLN) is proposed with the use of the HOS to classify the damage index (DI) based on stiffness degradation for damage identification. Training data are obtained using numerical simulations of 30,000 realistic, randomly created hysteresis loops, including a wide range of typical linear and nonlinear structural behaviours. Performance of the trained DLN model is assessed using both 1800 additional simulated 3-story “virtual” buildings and experimental data from a 3-story full-scale real building. Results are compared to the validated HLA method.Validation on simulated virtual building data yields prediction accuracy for 97.2% and 91.6% samples without and with 10% added noise, respectively. The comparison shows a good match of trend and percentage stiffness drop between DLN and HLA identification with the average difference for all cases within 1.1–4.6%, indicating a good accuracy of the proposed DLN prediction model for real structures. The overall results show its potential to provide a rapid, and real-time alarm or other notice on damage states and mitigation to emergency response using DLN and thus without detailed engineering analysis.
  • Optimal decisions for a two-echelon supply chain with capacity and demand
    • Abstract: Publication date: January 2019Source: Advanced Engineering Informatics, Volume 39Author(s): Meimei Zheng, Kan Wu, Cunwu Sun, Ershun Pan Due to the applications of Internet of Things and big data in the Industry 4.0 context, more information in and out of a smart factory can be collected and shared between manufacturers and retailers. In this study, we consider two types of information that can be available in a supply chain consisting of a manufacturer and a retailer in Industry 4.0: the capacity information for the later rush production and the demand information shared between the retailer and manufacturer. In the supply chain, the manufacturer provides two orders with maximum limits by using a capacitated normal production and two capacitated rush production modes. To study the effects of the information, we investigate the optimal decisions and profits for the supply chain with and without the capacity information and demand information sharing. In addition, we propose a coordination mechanism for the supply chain with both the capacity information and demand information sharing. The coordination mechanism does not only rely on cost parameters, but also on the capacity and demand information. The numerical examples show that the supply chain profit can be improved by as large as 16.76% in the coordinated system, compared with the original system without the capacity information and demand information sharing.
  • BIM-enabled facilities operation and maintenance: A review
    • Abstract: Publication date: January 2019Source: Advanced Engineering Informatics, Volume 39Author(s): Xinghua Gao, Pardis Pishdad-Bozorgi Building Information modeling (BIM) has the potential to advance and transform facilities Operation and Maintenance (O&M) by providing a platform for facility managers to retrieve, analyze, and process building information in a digitalized 3D environment. Currently, because of rapid developments in BIM, researchers and industry professionals need a state-of-the-art overview of BIM implementation and research in facility O&M. This paper presents a review of recent publications on the topic. It aims to evaluate and summarize the current BIM-O&M research and application developments from a facility manager's point of view, analyze research trends, and identify research gaps and promising future research directions. The scope of this research includes the academic articles, industry reports and guidelines pertaining to using BIM to improve selected facility O&M activities, including maintenance and repair, emergency management, energy management, change/relocation management, and security. The content analysis results show that research on BIM for O&M is still in its early stage and most of the current research has focused on energy management. We have identified that the interoperability in the BIM-O&M context is still a challenge and adopting the National Institute of Standards and Technology (NIST) Cyber-Physical Systems (CPS) Framework is a potential starting point to address this issue. More studies involving surveys are needed to understand the underlying O&M principles for BIM implementation – data requirements, areas of inefficiencies, the process changes. In addition, more studies on the return on investment of the innovative systems are required to justify the value of BIM-O&M applications and an improved Life Cycle Cost Analysis method is critical for such justifications.
  • Using blockmodeling for capturing knowledge: The case of energy analysis
           in the construction phase of oil and gas facilities
    • Abstract: Publication date: January 2019Source: Advanced Engineering Informatics, Volume 39Author(s): Rodrigo Rodrigues Aragao, E. El-Diraby Tamer In this paper, blockmodeling, a network analysis clustering approach, is used to study and capture clusters of concepts. The semantics networks are a former stage of the extracted, formalized knowledge from unstructured data (text). As a sample domain of the application of the proposed approach, the networks are focused on concepts related to planning for energy management during the construction phase. Text describing the status or lessons learned from projects is transferred to semantic networks. A benchmark concept network was generated based on surveying experts. Blockmodeling algorithms were used to create a set of concept blocks: clusters of related concepts that capture some of the knowledge of a generic scenario. Through interviews with staff from three projects, we developed 3 case studies (text) to capture the conditions and knowledge gained in these projects. A concept network of main concepts was extracted for each case. Also, blockmodels from these networks were also extracted. To facilitate the comparison, an average block analogy index k¯ was introduced. The smaller k¯ is, the more dissimilar the studied blocks are, and the more unique and unusual are the characteristics of the project at hand. By contrasting the blocks of the case projects against each other and against the benchmark network, we identified unique knowledge constructs (concept clusters) in the three projects. This can be beneficial in capturing project-specific knowledge; contrasting project conditions and knowledge concepts; and supporting a frequent upgrade of the benchmark concept network or a formal ontology.
  • Towards an automatic engineering change management in smart
           product-service systems – A DSM-based learning approach
    • Abstract: Publication date: January 2019Source: Advanced Engineering Informatics, Volume 39Author(s): Pai Zheng, Chun-Hsien Chen, Suiyue Shang The rapid development and implementation of smart, connected products (SCPs) in the engineering field has triggered a promising manufacturing paradigm of servitization, i.e. smart product-service systems (Smart PSS). As a complex solution bundle in both system and product level, its engineering change management differs from the existing ones mainly in two aspects. Firstly, massive in-context stakeholder-generated/product-sensed data during usage stage can be leveraged to enable its success in a data-driven manner. Secondly, the digitalized services, consisting of both hardware and software solutions, can also be changed in a more flexible way other than the physical components alone. Nevertheless, scarcely any work reports on how to conduct engineering change in such context, let alone a systematic approach to support the automatic generation of its change prediction or recommendation. Aiming to fill these gaps, this work proposes an occurrence-based design structure matrix (DSM) approach together with a three-way based cost-sensitive learning approach for automatic engineering change management in the Smart PSS environment. This informatics-based research, as an explorative study, overcomes the subjectivity and tedious assessment of the experts in the conventional approaches, and can offer useful guidelines to the manufacturing companies for managing their engineering changes for product-service innovation process.
  • A multi-criteria decision framework to support measurement-system design
           for bridge load testing
    • Abstract: Publication date: January 2019Source: Advanced Engineering Informatics, Volume 39Author(s): Numa J. Bertola, Marco Cinelli, Simon Casset, Salvatore Corrente, Ian F.C. Smith Due to conservative design models and safe construction practices, infrastructure usually has unknown amounts of reserve capacity that exceed code requirements. Quantification of this reserve capacity has the potential to lead to better asset-management decisions by avoiding unnecessary replacement and by lowering maintenance expenses. However, such quantification is challenging due to systematic uncertainties that are present in typical structural models. Field measurements, collected during load tests, combined with good structural-identification methodologies may improve the accuracy of model predictions. In most structural-identification tasks, engineers usually select and place sensors based on experience and high signal-to-noise estimations. Since the success of structural identification depends on the measurement system, research into measurement system design has been carried out over several decades. Despite the multi-criteria nature of the problem, most researchers have focused only on the information gained by the measurement system. This study presents a framework to evaluate and rank possible measurement-system designs based on a tiered multi-criteria strategy. Performance criteria for the design of measurement systems include monitoring costs, information gain, ability to detect outliers and impact of loss of information in case of sensor failure. Through including conflicting criteria, such as cost of monitoring and information gain, the optimal measuring system becomes a Pareto-like choice that ultimately depends on asset-manager preference hierarchies. Several potential preference scenarios are generated and results are compared using a full-scale test study, the Exeter Bascule Bridge. The framework successfully supports an informed design of measurement systems by providing an extensive set of alternatives, including the best solution defined probabilistically and for specific conditions when other near-optimal solutions might be preferred.
  • BA-PNN-based methods for power transformer fault diagnosis
    • Abstract: Publication date: January 2019Source: Advanced Engineering Informatics, Volume 39Author(s): Xiaohui Yang, Wenkai Chen, Anyi Li, Chunsheng Yang, Zihao Xie, Huanyu Dong This paper presents a machine learning-based approach to power transformer fault diagnosis based on dissolved gas analysis (DGA), a bat algorithm (BA), optimizing the probabilistic neural network (PNN). PNN is a radial basis function feedforward neural network based on Bayesian decision theory, which has a strong fault tolerance and significant advantages in pattern classification. However, one challenge still remains: the performance of PNN is greatly affected by its hidden layer element smooth factor which impacts the classification performance. The proposed approach addresses this challenge by deploying the BA algorithm, a kind of bio-inspired algorithm to optimize PNN. Using the real data collected from a transformer system, we conducted the experiments for validating the performance of the developed method. The experimental results demonstrated that BA is an effective algorithm for optimizing PNN smooth factor and BA-PNN can improve the fault diagnosis performance; in turn, and the machine learning-based model (BA-PNN) can significantly enhance the accuracies of power transformer fault diagnosis.
  • A deep learning-based approach for mitigating falls from height with
           computer vision: Convolutional neural network
    • Abstract: Publication date: January 2019Source: Advanced Engineering Informatics, Volume 39Author(s): Weili Fang, Botao Zhong, Neng Zhao, Peter E.D. Love, Hanbin Luo, Jiayue Xue, Shuangjie Xu Structural supports (e.g., concrete and steel) provide engineering structures with stability by transferring loads. During the construction of an engineering structure, individuals are often prone to taking short take-cuts by traversing supports to perform their daily activities and save time. Thus, the likelihood of an individual being subjected to an injury or even killing themselves significantly increases when performing such unsafe behavior. To address this problem, we have developed an automatic computer-vision approach that utilizes a Mask Region Based Convolutional Neural Network (R-CNN) to detect individuals traversing structural supports during the construction of a project. The algorithms developed are used to: (1) automatically identify the presence of people; and (2) recognize the relationship between people and concrete/steel supports to determine their presence of them. To validate our approach, we created an extensive database of photographs of people who had traversed structural supports from a number of different constructions project to train and test the developed Mask R-CNN. The recall and precision rates for overlapping detection were found to be 90% and 75%. The results demonstrate that the developed Mask R-CNN can accurately detect people that traverse concrete/steel supports during construction. We suggest that proposed computer-vision approach that we have developed can be used by site management to automatically identify unsafe behavior and provide feedback to individuals about their likelihood of falls from heights. By recognizing unsafe behavior in real-time, appropriate actions (e.g. education) can be instantly put in place to prevent their re-occurrence.
  • Framework for prioritizing geospatial data processing tasks during extreme
           weather events
    • Abstract: Publication date: January 2019Source: Advanced Engineering Informatics, Volume 39Author(s): Xuan Hu, Jie Gong In recent years, advanced geospatial technologies have been playing an increasingly important role in supporting critical decision makings in disaster response. One rising challenge to effectively use the growing volume of geospatial data sets is to rapidly process the data and to extract useful information. Unprocessed data are intangible and non-consumable, and often create the so-called “data-rich-but-information-poor” situation. To address this issue, this study proposed a Data Envelopment Analysis (DEA) based information salience framework to prioritize the sequence of the information processing tasks. The proposed model integrates the DEA efficiency score with a linguistic group decision process. For the input variables, computational complexity and intensity are selected to measure the difficulty in information processing. For the outputs, the performance of each processing tasks is evaluated based on the experts’ judgment on how the processing tasks satisfy the needs of decision makers. These needs are characterized by four classic disaster functions. A unique element of our proposed framework is that cone constraints are added to the DEA model based on the experts’ evaluation of the importance of the four disaster functions to model the dynamic information need. The proposed model was validated with a Hurricane Sandy based case study. The results indicate that the proposed framework is capable of prioritizing geospatial data processing tasks in a systematic manner and accelerating information extraction from disaster related geospatial data sets.
  • Multi-view feature modeling for design-for-additive manufacturing
    • Abstract: Publication date: January 2019Source: Advanced Engineering Informatics, Volume 39Author(s): Lei Li, Jikai Liu, Yongsheng Ma, Rafiq Ahmad, Ahmed Qureshi This paper presents a design-for-additive manufacturing (DfAM) methodology based on multi-view feature modeling. Multi-view feature model is a unified information carrier which contains the data of a product related to different lifecycle stages. The information can be selectively extracted, interpreted and clustered to provide a specific view of the product and thus to facilitate the design-for-X, e.g. manufacturability design with the manufacturing view and mechanical property enhancement with the analysis view. Feature conversion provides a mechanism to translate the lifecycle stage-related descriptions of the product, which therefore reveals the underlying dependencies and addresses the complexities in associative modeling. For instance, manufacturing information has to be extracted to support the construction of the analysis model, because process planning of additive manufacturing (AM) has a direct impact on the material properties. The main benefit of multi-view feature modeling for AM is that integrated product development can be realized to simultaneously take several engineering aspects into account, e.g. concurrent design of the structural mechanical properties and manufacturability. Apparently, performing the integrated design can enhance the overall design quality and significantly improve the product development efficiency. Specifically in this paper, the design, manufacturing, and analysis views of an AM product will be modeled. Level set will be adopted as the basic mathematical tool for multi-view feature modeling because of its compatibility with all the concerned design activities. The effectiveness of the integrated product design will be demonstrated by numerical case studies.
  • Virtual engineering of cyber-physical automation systems: The case of
           control logic
    • Abstract: Publication date: January 2019Source: Advanced Engineering Informatics, Volume 39Author(s): Georg Ferdinand Schneider, Hendro Wicaksono, Jivka Ovtcharova Mastering the fusion of information and communication technologies with physical systems to cyber-physical automation systems is of main concern to engineers in the industrial automation domain. The engineering of these systems is challenging as their distributed nature and the heterogeneity of stakeholders and tools involved in their engineering contradict the need for the simultaneous engineering of their cyber and physical parts over their life cycle. This paper presents a novel approach based on the virtual engineering method, which provides support for the simultaneous engineering of the cyber and physical parts of automation systems. The approach extends and integrates the life cycle centered view mandated by current conceptual architectures and the digital twin paradigm with an integrated, iterative engineering method. The benefits of the approach are highlighted in a case study related to the engineering of the control logic of a cyber physical automation system originating from the process engineering domain. We describe for the first time a modular domain ontology, which formally describes the cyber and physical part of the system. We present cyber services built on top of the ontology layer, which allow to automatically verify different control logic types and simultaneously verify cyber and physical parts of the system in an incremental manner.
  • Seismic loss assessment for buildings with various-LOD BIM data
    • Abstract: Publication date: January 2019Source: Advanced Engineering Informatics, Volume 39Author(s): Zhen Xu, Xinzheng Lu, Xiang Zeng, Yongjia Xu, Yi Li Earthquake-induced loss of buildings is a fundamental concern for earthquake-resilient cities. The FEMA P-58 method is a state-of-the-art seismic loss assessment method for buildings. Nevertheless, because the FEMA P-58 method is a refined component-level loss assessment method, it requires highly detailed data as the input. Consequently, the knowledge of building details will affect the seismic loss assessment. In this study, a seismic loss assessment method for buildings combining building information modeling (BIM) with the FEMA P-58 method is proposed. The detailed building data are automatically obtained from the building information model in which the building components may have different levels of development (LODs). The determination of component type and the development of the component vulnerability function when the information is incomplete are proposed. The modeling rules and the information extraction from BIM through the Autodesk Revit application programming interface (API) are also proposed. Finally, to demonstrate the rationality of the proposed method, an office building that is available online is selected, and the seismic loss assessments with various-LOD BIM data are performed as case studies. The results show that, on the one hand, even if the available building information is limited, the proposed method can still produce an acceptable loss assessment; on the other hand, given more information, the accuracy of the assessment can be improved and the uncertainty can be reduced using the proposed method. Consequently, this study provides a useful reference for the automation of the refined seismic loss assessment of buildings.
  • A novel approach for capturing and evaluating dynamic consumer
           requirements in open design
    • Abstract: Publication date: January 2019Source: Advanced Engineering Informatics, Volume 39Author(s): Shipei Li, Dunbing Tang, Jun Yang, Qi Wang, Inayat Ullah, Haihua Zhu In the product design, understanding consumer requirements (CRs) is essential due to the fact that it influences the success of the product. Currently, it has been recognized that consumer involvement in an open design (OD) environment is an effective way to reduce the gap between what is required by consumers and what companies can provide. In the process of OD, the initial CRs may shift as the consumers can influence each other, and it is a challenge to capture and evaluate dynamic CRs in OD. In this study, the dynamic relationship between consumers who participate in OD is discussed based on Nash equilibrium. Corresponding to the dynamic relationship, a novel approach is proposed to explore the crucial CRs from consumers’ perspective. Initially, the platform of OD is constructed on the Internet to collect the CRs and its evaluations. Thereafter, a similarity-based filtering method (SFM) is presented to filter out the most differentiated evaluations. Furthermore, to get the weight of the CR evaluated by consumers, an improved fuzzy Delphi method is introduced. Meanwhile, to solve the problem of excessive number of CRs, a dual-index evaluation method is put forward to control the opening degree of OD. Finally, to demonstrate the applicability of the suggested approach, a product of smartphone is examined as a case study. The results confirm that the proposed approach is appropriate for capturing and evaluating dynamic CRs in OD.
  • A non-centralized adaptive method for dynamic planning of construction
           components storage areas
    • Abstract: Publication date: January 2019Source: Advanced Engineering Informatics, Volume 39Author(s): Kaiman Li, Hanbin Luo, Mirosław J. Skibniewski Rehandling of construction components, such as pipes, structural steel elements, and curtain walls, may increase the handling cost and reduce the construction efficiency, which is a critical issue for storage area plans of a project. Moreover, on some construction sites where space is limited, there are not adequate storage areas for centralized stacking of components and frequent changes in spatial state. Existing studies have investigated site layout planning for temporary facilities including arranging a storage area for the same type of material, which still have limitations in solving the above problems. This study proposes a novel and flexible arrangement method for incoming components in limited site space. This method is non-centralized and adaptive to the dynamic change of the actual component requirements based on construction activities and the real-time storage area availability. Therefore, a construction components storage areas planning (CCSAP) model is developed for dynamic allocation of construction components storage areas. Building information modeling (BIM) can be used to generate the material requirements planning before construction according to the actual construction activities. Real-time spatial recognition is a critical step for dynamic allocation of construction components storage areas because no such research has been done. This paper firstly presents an imaging technology with a low-rank matrix to identify on-site unoccupied locations automatically in real time. In addition, genetic algorithms (GA) consider two types of decision variables: actual components supply and real-time space availability. Finally, a dynamic visualization platform is built for planning construction components storage areas. An implementation example is demonstrated to validate principles and this model and shows a 21.9% reduction in the handling cost and a 19.4% increase in the construction efficiency compared with conventional methods.
  • A design for disassembly tool oriented to mechatronic product
           de-manufacturing and recycling
    • Abstract: Publication date: January 2019Source: Advanced Engineering Informatics, Volume 39Author(s): Claudio Favi, Marco Marconi, Michele Germani, Marco Mandolini The easy disassembly of certain product components is a prerequisite to guarantee an efficient recovery of parts and materials. This is one of the first step in the implementation of circular economy business models. Design for Disassembly (DfD) is a particular target design methodology supporting engineers in developing industrial products that can be easily disassembled into single components.The paper presents a method and a software tool for quantitatively assessing the disassemblability and recyclability of mechatronic products. The time-based method has been implemented in a software tool, called LeanDfD, which calculates the best disassembly sequences of target components considering disassembly precedencies, liaisons among components, and specific properties to model the real condition of the product at its End-of-Life (EoL). A dedicated repository has been developed to store and classify standard times and corrective factors of each disassembly liaison and operation. This knowledge feeds the two LeanDfD tool modules: (i) product disassemblability module, which allows to carry out the time-based analysis and to improve the disassemblability performance of target components, and (ii) product recyclability module, which estimates the quantities of materials that could be potentially recycled at the product EoL. The LeanDfD tool functionalities have been defined starting from the means of the user stories and the developed tool framework, data structure, databases and use scenarios are described.A group of designers/engineers used the tool during a re-design project of a washing machine, considering the disassemblability as the main driver. The case study highlights how the proposed DfD method and tool are able to support the implementation of re-design actions for improving product de-manufacturability and EoL performance. The LeanDfD features aid engineers in making a quick and robust assessment of their design choices by considering quantitative disassemblability and recyclability metrics.
  • Assessing the influence of repeated exposures and mental stress on human
           wayfinding performance in indoor environments using virtual reality
    • Abstract: Publication date: January 2019Source: Advanced Engineering Informatics, Volume 39Author(s): Jing Lin, Lijun Cao, Nan Li This study aimed to examine the effect of repeated exposures to indoor environments on people’s indoor wayfinding performance, both under normal condition and during fire emergency which could induce significant mental stress. Indoor wayfinding experiments were conducted in an immersive virtual museum developed using virtual reality technologies. Participants of the experiments were divided into three groups, who participated in one, two and three trials, respectively. Those who participated in more than one trial were given an interval of two weeks between two consecutive trials. Each trial of the experiment included a treasure hunting task and an egress task. Participants were presented with a virtual fire emergency during the egress task of their last trial. Data of wayfinding performance measures of the participants, as well as their physiological and emotional responses, sense of direction, wayfinding anxiety and simulator sickness were collected and analyzed. The results revealed significant positive impact of repeated exposure on participants’ wayfinding performance, which resulted in a decrease in the time needed to complete the treasure hunting task. The results also revealed significant negative impact of mental stressed caused by the fire emergency on participants’ wayfinding performance, which led to increased travel time and distance during egress. Such negative impact of stress, however, could be noticeably diminished by the repeated exposures, showing significant interaction effect between these two factors.
  • A new distributed time series evolution prediction model for dam
           deformation based on constituent elements
    • Abstract: Publication date: January 2019Source: Advanced Engineering Informatics, Volume 39Author(s): Mingchao Li, Yang Shen, Qiubing Ren, Heng Li The construction of a mathematical model to predict dam deformation can provide an important basis for judging its operating condition. Due to several time-varying factors, such as water level, temperature and aging, the dam prototype monitoring data series shows non-linear and non-stationary features, which increase the difficulty of dam deformation prediction and analysis. For this reason, a novel distributed deformation prediction model (DDPM), which combines transformation ideology with structured methodology, is proposed to improve the reliability of deformation prediction. DDPM starts by considering three constituent elements of dam deformation series using time series decomposition, and a multi-model fusion strategy is adopted. The trend, periodic and remainder components are separately predicted through constructing the optimal fitting, weight window and remainder generation sub-models. The three predicted components are aggregated as the final predicted output based on an underlying data model. The accuracy and validity of DDPM are verified and evaluated by taking a concrete dam in China as an example and comparing prediction performance with well-established models. The simulation results indicate that DDPM can not only extract more potential data features to obtain good deformation prediction effect, it can also reduce the complexity of mathematical modeling. Furthermore, two other functions of DDPM, including missing value handling and anomaly detection, are also discussed, which ultimately realize the integrated configuration of deformation prediction and data cleaning. The new model provides an alternative method for prediction and analysis of dam deformation and other structural behavior.
  • A knowledge discovery and reuse method for time estimation in ship block
           manufacturing planning using DEA
    • Abstract: Publication date: January 2019Source: Advanced Engineering Informatics, Volume 39Author(s): Jinghua Li, Miaomiao Sun, Duanfeng Han, Jiaxuan Wang, Xuezhang Mao, Xiaoyuan Wu Rational and precise time estimation in a manufacturing plan is critical to the success of a shipbuilding project. However, due to the large number of various ship blocks, existing means are somehow inadequate to make the expected estimation. This paper proposes a novel three-stage method to discover and reuse the knowledge about how the duration and the slack time is essential while manufacturing a specific ship block. An efficient arrangement of the duration and the slack time means that the activity is more likely to be finished within the allocated duration, or if not, the extra consumed time does not exceed the given slack time which is at its lowest level. With such knowledge, planners can rapidly estimate the time allocation of all the manufacturing activities in the planning stage, which raises the possibility of successful execution within the limited budget. Different from previous studies, this research utilizes the execution data to find efficiency frontiers of the planned time arrangement (the duration and the slack time). For the sake of the evaluation validity, ship blocks are primarily clustered according to their features using the K-Means algorithm. In the second stage, an adapted data envelopment analysis (DEA) model is presented to evaluate the planned time arrangement. By processing the results, efficient time arrangements for manufacturing all the blocks can be obtained, hence, forming a data basis to boost the time estimation accuracy. In the last stage, genetic algorithm-backpropagation neural network (GA-BPNN) models are trained to capture the knowledge for further reuse by planners. Verified through experiments, this research almost outperforms several peer methods in terms of precision.
  • Modeling and simulation of complex manufacturing phenomena using sensor
           signals from the perspective of Industry 4.0
    • Abstract: Publication date: January 2019Source: Advanced Engineering Informatics, Volume 39Author(s): AMM Sharif Ullah This article presents a methodology defined as semantic modeling to create computable virtual abstractions of complex manufacturing phenomena denoted as phenomena twins from the perspective of the fourth industrial revolution (also known as Smart Manufacturing, Connected Factory, Industry 4.0, and so forth). The twins are created such that they become friendly to both sensor signals and new-generation web technology (i.e., the semantic web). The efficacy of the proposed modeling approach is demonstrated by creating a phenomenon twin of cutting force (a highly complex and stochastic phenomenon associated with all material removal processes) and also by representing it using the semantic web. The relevant epistemological and systemological issues (e.g., those of meta-models, ontology, classification/trustworthiness/provenance of knowledge associated with the webized phenomenon twin) are also discussed. This article will help developers of embedded (e.g., cyber-physical) systems needed for functionalizing Industry 4.0.
  • Yard crane scheduling problem in a container terminal considering risk
           caused by uncertainty
    • Abstract: Publication date: January 2019Source: Advanced Engineering Informatics, Volume 39Author(s): Junliang He, Caimao Tan, Yuting Zhang In a container terminal, the arriving times and handling volumes of the vessels are uncertain. The arriving times of the external trucks and the number of containers which are needed to be brought into or retrieved from a container terminal by external trucks within a period are also uncertain. Yard crane (YC) scheduling is under uncertainty. This paper addresses a YC scheduling problem with uncertainty of the task groups' arriving times and handling volumes. We do not only optimize the efficiency of YC operations, but also optimize the extra loss caused by uncertainty for reducing risk of adjusting schedule as the result of the task groups' arriving times and handling volumes deviating from their plan. A mathematical model is proposed for optimizing the total delay to the estimated ending time of all task groups without uncertainty and the extra loss under all uncertain scenarios. Furthermore, a GA-based framework combined with three-stage algorithm is proposed to solve the problem. Finally, the proposed mathematical model and approach are validated by numerical experiments.
  • Multi-source data analytics for AM energy consumption prediction
    • Abstract: Publication date: October 2018Source: Advanced Engineering Informatics, Volume 38Author(s): Jian Qin, Ying Liu, Roger Grosvenor The issue of Additive Manufacturing (AM) system energy consumption attracts increasing attention when many AM systems are applied in digital manufacturing systems. Prediction and reduction of the AM energy consumption have been established as one of the most crucial research targets. However, the energy consumption is related to many attributes in different components of an AM system, which are represented as multiple source data. These multi-source data are difficult to integrate and to model for AM energy consumption due to its complexity. The purpose of this study is to establish an energy value predictive model through a data-driven approach. Owing to the fact that multi-source data of an AM system involves nested hierarchy, a hybrid approach is proposed to tackle the issue. This hybrid approach incorporates clustering techniques and deep learning to integrate the multi-source data that is collected using the Internet of Things (IoT), and then to build the energy consumption prediction model for AM systems. This study aims to optimise the AM system by exploiting energy consumption information. An experimental study using the energy consumption data of a real AM system shows the merits of the proposed approach. Results derived using this hybrid approach reveal that it outperforms pre-existing approaches.
  • Utilizing text mining and Kansei Engineering to support data-driven design
           automation at conceptual design stage
    • Abstract: Publication date: October 2018Source: Advanced Engineering Informatics, Volume 38Author(s): Ming-Chuan Chiu, Kong-Zhao Lin PurposeThe purpose of this research was to develop a case-based method for analyzing online customer reviews and extracting customer preferences through an integration of text mining and Kansei Engineering (KE) in an effort to achieve conceptual data-driven design automation and to successfully identify future trends in a particular consumer product.Design/Methodology/Approach This study’s model merges text mining and KE to extract key descriptive Kansei terminology according to actual customer reviews and use it to forecast consumer preferred product design while reducing certain repetitious tasks of designers. This work first collects online product reviews using text mining. Then, through the application of KE, the customer-preferred design components are identified and incorporated into the product design specifications. Finally, an Application Programming Interface (API) is developed to automatically generate a CAD preliminary design.Case Study A road bike case study is provided to demonstrate the practicality of proposed method. The online reviews are collected from The related design elements are classified into six key components which can be modified in the proposed conceptual design automation system.Originality/value This is the first paper that has applied text mining and KE for use in product development. This work can also reduce the time and cost of product design through the automation of repetitive design tasks. The conceptual design automation system is valuable for designers wishing to identify customer needs and to generate engineering drawings in a timely manner without significant repetition during the design process.
  • Automated 3D volumetric reconstruction of multiple-room building interiors
           for as-built BIM
    • Abstract: Publication date: October 2018Source: Advanced Engineering Informatics, Volume 38Author(s): Jaehoon Jung, Cyrill Stachniss, Sungha Ju, Joon Heo Currently, fully automated as-built modeling of building interiors using point-cloud data still remains an open challenge, due to several problems that repeatedly arise: (1) complex indoor environments containing multiple rooms; (2) time-consuming and labor-intensive noise filtering; (3) difficulties of representation of volumetric and detail-rich objects such as windows and doors. This study aimed to overcome such limitations while improving the amount of details reproduced within the model for further utilization in BIM. First, we input just the registered three-dimensional (3D) point-cloud data and segmented the point cloud into separate rooms for more effective performance of the later modeling phases for each room. For noise filtering, an offset space from the ceiling height was used to determine whether the scan points belonged to clutter or architectural components. The filtered points were projected onto a binary map in order to trace the floor-wall boundary, which was further refined through subsequent segmentation and regularization procedures. Then, the wall volumes were estimated in two ways: inside- and outside-wall-component modeling. Finally, the wall points were segmented and projected onto an inverse binary map, thereby enabling detection and modeling of the hollow areas as windows or doors. The experimental results on two real-world data sets demonstrated, through comparison with manually-generated models, the effectiveness of our approach: the calculated RMSEs of the two resulting models were 0.089 m and 0.074 m, respectively.
  • A psychometric user experience model based on fuzzy measure approaches
    • Abstract: Publication date: October 2018Source: Advanced Engineering Informatics, Volume 38Author(s): Jyh-Rong Chou User experience (UX) is considered a key quality of interactive products in today’s competitive mass markets and is of growing interest in both academia and industry. UX concerns the encounters a user has while interacting with products, systems, and services. It is ubiquitous, omnipresent, and dynamic in nature, referring to the non-quantifiable, subjective, affective-based, and context-dependent processes. UX is difficult for researchers to objectively and uniformly measure as it involves complex human perceptual interpretations of experiential responses with a certain degree of uncertainty, imprecision, and vagueness. Based on the user experience questionnaire (UEQ), this paper presents a psychometric UX model using fuzzy measure approaches, the purpose of which is to develop a metric for quantitative assessment of certain product UXs through a user experience index (UXI). This model enables researchers to understand users’ perceptions of the interactions that constitute qualities of using a specific product. An empirical study concerning the episodic UX measures of using a set of touch mice was conducted to verify the applicability and effectiveness of the proposed UX model. The theoretical and practical implications of the UX model are also discussed.
  • Modelling residual value risk through ontology to address vulnerability of
           PPP project system
    • Abstract: Publication date: October 2018Source: Advanced Engineering Informatics, Volume 38Author(s): Jingfeng Yuan, Xuan Li, Kaiwen Chen, Mirosław J. Skibniewski For facilitating the management of Residual Value Risk (RVR) in Public Private Partnership (PPP) projects, an ontology-based model is established to describe the generation process and complex relationships of RVR. An ontology-based approach is proposed to analyze the RVR in PPPs, which is a framework addressing the vulnerability and a knowledge-based modeling for RVR management. The RVR ontology model is composed of class of Project, Risk, and Vulnerability, as well as taxonomy of risk factors for risk sources (RS), risk events (RE), risk consequences (RC), exposure (V1), resilience (V2) and sensitivity (V3). Meanwhile, different relationships among taxonomies, classes and individuals are expressed in model. Moreover, the object properties for class project and the object properties of inherited/non-inherited relationships are defined. Meanwhile, project-based, risk-based, and vulnerability-based datatype property are further described. Then a real individual is established by using the ontology editing software Protégé. The proposed RVR ontology model can be used to visualize and manipulate various representations in RVR management as well as to implement the work of risk reasoning and analyzing. The proposed RVR ontology framework provides a useful framework to systematize different knowledge of RVRs in PPP projects, in which the related knowledge can be described clearly and effectively. Moreover, the proposed framework can enhance data process function and improve the analysis of RVR probability and vulnerability in PPP projects through sharedness and transferability of RVR knowledge provided by ontology-based RVR model for different stakeholders in PPP projects.
  • The effects of augmented reality on improving spatial problem solving for
           object assembly
    • Abstract: Publication date: October 2018Source: Advanced Engineering Informatics, Volume 38Author(s): Abhiraj Deshpande, Inki Kim The capability of Augmented Reality (AR) technology to track and visualize relations of objects in space has led to diverse industry applications to support complex engineering tasks. Object assembly is one of them. For an AR aid to support Ready-to-Assemble (RTA) furniture particularly, the challenge is to effectively design the visual features and mode of interaction, so that the first-time users can quickly conceive spatial relations of its parts. However, AR developers and engineers do not have sufficient guidelines to achieve such performance-driven goals. The scientific evidence and account of how one could cognitively benefit in object assembly can be useful to guide them. This experimental research developed an Augmented Reality (AR) application on the Microsoft HoloLens™ headset, and tested it on the first-time users of RTA furniture. The controlled experiments and behavioral analyses of fourteen participants in working out the two RTA furniture with different assembly complexity showed that, the application was effective to improve spatial problem-solving abilities. Especially, the positive effects of the AR-based supports stood out against the assembly of higher complexity. The findings have implications for the information design of advanced AR applications to support assembly tasks with high demands of spatial knowledge. In addition, the extensive analyses of the participants’ performance, behaviors, cognitive workload, and subjective responses helped identify usability issues with the Microsoft HoloLens™.
  • OntoProg: An ontology-based model for implementing Prognostics Health
           Management in mechanical machines
    • Abstract: Publication date: October 2018Source: Advanced Engineering Informatics, Volume 38Author(s): David Lira Nuñez, Milton Borsato Trends in Prognostics Health Management (PHM) have been introduced into mechanical items of manufacturing systems to predict Remaining Useful Life (RUL). PHM as an estimate of the RUL allows Condition-based Maintenance (CBM) before a functional failure occurs, avoiding corrective maintenance that generates unnecessary costs on production lines. An important factor for the implementation of PHM is the correct data collection for monitoring a machine’s health, in order to evaluate its reliability. Data collection, besides providing information about the state of degradation of the machine, also assists in the analysis of failures for intelligent interventions. Thus, the present work proposes the construction of an ontological model for future applications such as expert system in the support in the correct decision-making, besides assisting in the implementation of the PHM in several manufacturing scenarios, to be used in the future by web semantics tools focused on intelligent manufacturing, standardizing its concepts, terms, and the form of collection and processing of data. The methodological approach Design Science Research (DSR) is used to guide the development of this study. The model construction is achieved using the ontology development 101 procedure. The main result is the creation of the ontological model called OntoProg, which presents: a generic ontology addressing by international standards, capable of being used in several types of mechanical machines, of different types of manufacturing, the possibility of storing the knowledge contained in events of real activities that allow through consultations in SPARQL for decision-making which enable timely interventions of maintenance in the equipment of a real industry. The limitation of the work is that said model can be implemented only by specialists who have knowledge in ontology.
  • Random generation of industrial pipelines’ data using Markov chain
    • Abstract: Publication date: October 2018Source: Advanced Engineering Informatics, Volume 38Author(s): Mubarak AL-Alawi, Ahmed Bouferguene, Yasser Mohamed Random generation of data sets is a vital step in simulation modeling. It involves in generating the variation associated with the real system behavior. In the industrial fabrication of construction components, unique products such as pipelines are produced. The fabrication processes are dependent on pipelines features, and complexity; randomly generating pipelines structure is imperative in the simulation of such processes. This paper investigates the nature of industrial pipelines and proposes a Markov chain model to randomly generate pipelines data structure. The performance of Markov chain model was tested against real pipelines through a three-stage validation process. The validation process includes (1) a validation based on the number of components and the pipelines components correlation analysis, (2) clustering-based model validation, and (3) model validation using similarity distances between pipelines feature vectors. The Markov chain model was found to generate a reasonable pipelines data structure when compared with real pipelines. It was found that 89% of the generated pipelines share similar properties equivalent to 0.88 (a scale from 0 (not identical) to 1 (identical)) to 85.5% of the original pipelines.
  • A generative design technique for exploring shape variations
    • Abstract: Publication date: October 2018Source: Advanced Engineering Informatics, Volume 38Author(s): Shahroz Khan, Muhammad Junaid Awan Because innovative and creative design is essential to a successful product, this work brings the benefits of generative design in the conceptual phase of the product development process so that designers/engineers can effectively explore and create ingenious designs and make better design decisions. We proposed a state-of-the-art generative design technique (GDT), called Space-filling-GDT (Sf-GDT), for the creation of innovative designs. The proposed Sf-GDT has the ability to create variant optimal design alternatives for a given computer-aided design (CAD) model. An effective GDT should generate design alternatives that cover the entire design space. Toward that end, the criterion of space-filling is utilized, which uniformly distribute designs in the design space thereby giving a designer a better understanding of possible design options. To avoid creating similar designs, a weighted-grid-search approach is developed and integrated into the Sf-GDT. One of the core contributions of this work lies in the ability of Sf-GDT to explore hybrid design spaces consisting of both continuous and discrete parameters either with or without geometric constraints. A parameter-free optimization technique, called Jaya algorithm, is integrated into the Sf-GDT to generate optimal designs. Three different design parameterization and space formulation strategies; explicit, interactive, and autonomous, are proposed to set up a promising search region(s) for optimization. Two user interfaces; a web-based and a Windows-based, are also developed to utilize Sf-GDT with the existing CAD software having parametric design abilities. Based on the experiments in this study, Sf-GDT can generate creative design alternatives for a given model and outperforms existing state-of-the-art techniques.
  • Participatory decision-support model in the context of building structural
           design embedding BIM with QFD
    • Abstract: Publication date: October 2018Source: Advanced Engineering Informatics, Volume 38Author(s): S. Eleftheriadis, P. Duffour, D. Mumovic The design and optimisation of building structures is a complex undertaking that requires the effective collaboration of various stakeholders and involves technical and non-technical expertise. The paper investigated an integrated decision-support framework using Quality Function Deployment (QFD) in structural design optimisation. The aim of the study was to develop and test a systematic participatory model that utilises Building Information Modelling (BIM)-enabled technologies for data collection and group decision-making theory. The uncertainties associated with the decision-makers’ preferences were computed using Evidential Reasoning (ER) algorithms in the QFD house of quality. An actual decision scenario was used to test the proposed framework and investigate its capabilities in the context of reinforced concrete buildings. The study demonstrated how the proposed QFD model could effectively enhance decision-making by managing the diversity of stakeholders’ preferences via design integration, enhanced communication and shared domain knowledge.
  • Fall risk assessment of construction workers based on biomechanical gait
           stability parameters using wearable insole pressure system
    • Abstract: Publication date: October 2018Source: Advanced Engineering Informatics, Volume 38Author(s): Maxwell Fordjour Antwi-Afari, Heng Li Falls on the same level are a leading cause of non-fatal injuries in the construction industry, and loss of balance events are the primarily contributory risk factors associated with workers’ fall injuries. Previous studies have indicated that changes in biomechanical gait stability parameters provide substantial safety gait metrics for assessing workers’ fall risks. However, scant research has been conducted on changes in biomechanical gait stability parameters based on foot plantar pressure patterns to assess workers’ fall risks. This research examined the changes in spatial foot regions and loss of balance events associated with biomechanical gait stability parameters based on foot plantar pressure patterns measured by wearable insole pressure system. To test the hypotheses of this study, ten asymptomatic participants conducted laboratory simulated loss of balance events which are often initiated by extrinsic fall risk factors. Our results found: (1) statistically significant differences in biomechanical gait stability parameters between spatial foot regions, especially with the peak pressure parameter; and (2) statistically significant differences in biomechanical gait stability parameters between loss of balance events when compared to normal gait (baseline), especially with the pressure-time integral parameter. Overall, the findings of this study not only provide useful safety gait metrics for early detection of specific spatial foot regions but also allow safety managers to understand the mechanism of loss of balance events in order to implement proactive fall-prevention strategies.
  • Prototyping virtual reality serious games for building earthquake
           preparedness: The Auckland City Hospital case study
    • Abstract: Publication date: October 2018Source: Advanced Engineering Informatics, Volume 38Author(s): Ruggiero Lovreglio, Vicente Gonzalez, Zhenan Feng, Robert Amor, Michael Spearpoint, Jared Thomas, Margaret Trotter, Rafael Sacks Enhancing evacuee safety is a key factor in reducing the number of injuries and deaths that result from earthquakes. One way this can be achieved is by training occupants. Virtual Reality (VR) and Serious Games (SGs), represent novel techniques that may overcome the limitations of traditional training approaches. VR and SGs have been examined in the fire emergency context; however, their application to earthquake preparedness has not yet been extensively examined.We provide a theoretical discussion of the advantages and limitations of using VR SGs to investigate how building occupants behave during earthquake evacuations and to train building occupants to cope with such emergencies. We explore key design components for developing a VR SG framework: (a) what features constitute an earthquake event; (b) which building types can be selected and represented within the VR environment; (c) how damage to the building can be determined and represented; (d) how non-player characters (NPC) can be designed; and (e) what level of interaction there can be between NPC and the human participants. We illustrate the above by presenting the Auckland City Hospital, New Zealand as a case study, and propose a possible VR SG training tool to enhance earthquake preparedness in public buildings.
  • A case based heuristic for container stacking in seaport terminals
    • Abstract: Publication date: October 2018Source: Advanced Engineering Informatics, Volume 38Author(s): Ines Rekik, Sabeur Elkosantini, Habib Chabchoub In this paper, we suggest a Case Based heuristic for the online container stacking management system in seaport terminals. The main objectives of the system are to determine the exact position of each import container in the storage area and to control container allocation and react to unexpected events and disturbances in an intelligent, self-organizing and real-time manner. First, we propose learning mechanisms and knowledge models for a better management of knowledge related to disturbances and container environment. This system takes into account different types of containers especially the storage of dangerous containers. For assessment of the suggested system, real data are collected from King Abdul Aziz Dammam seaport terminal (Saudi Arabia). The performance of the developed heuristic is assessed with different scenarios and compared to three other stacking strategies studied in the scientific literature. The obtained results are promising and show that the developed CBR (Case Based Reasoning) based heuristic can be efficient or similar problems, i.e. online container staking.
  • Intelligent computing in Architecture, Engineering and Construction
    • Abstract: Publication date: October 2018Source: Advanced Engineering Informatics, Volume 38Author(s): Christian Koch, Walid Tizani, Timo Hartmann
  • Traffic signal optimization through discrete and continuous reinforcement
           learning with robustness analysis in downtown Tehran
    • Abstract: Publication date: October 2018Source: Advanced Engineering Informatics, Volume 38Author(s): Mohammad Aslani, Stefan Seipel, Mohammad Saadi Mesgari, Marco Wiering Traffic signal control plays a pivotal role in reducing traffic congestion. Traffic signals cannot be adequately controlled with conventional methods due to the high variations and complexity in traffic environments. In recent years, reinforcement learning (RL) has shown great potential for traffic signal control because of its high adaptability, flexibility, and scalability. However, designing RL-embedded traffic signal controllers (RLTSCs) for traffic systems with a high degree of realism is faced with several challenges, among others system disturbances and large state-action spaces are considered in this research.The contribution of the present work is founded on three features: (a) evaluating the robustness of different RLTSCs against system disturbances including incidents, jaywalking, and sensor noise, (b) handling a high-dimensional state-action space by both employing different continuous state RL algorithms and reducing the state-action space in order to improve the performance and learning speed of the system, and (c) presenting a detailed empirical study of traffic signals control of downtown Tehran through seven RL algorithms: discrete state Q-learning(λ), SARSA(λ), actor-critic(λ), continuous state Q-learning(λ), SARSA(λ), actor-critic(λ), and residual actor-critic(λ).In this research, first a real-world microscopic traffic simulation of downtown Tehran is carried out, then four experiments are performed in order to find the best RLTSC with convincing robustness and strong performance. The results reveal that the RLTSC based on continuous state actor-critic(λ) has the best performance. In addition, it is found that the best RLTSC leads to saving average travel time by 22% (at the presence of high system disturbances) when it is compared with an optimized fixed-time controller.
  • CA-FCM: Towards a formal representation of expert’s causal judgements
           over construction project changes
    • Abstract: Publication date: October 2018Source: Advanced Engineering Informatics, Volume 38Author(s): Mehrzad Shahinmoghaddam, Ahad Nazari, Mostafa Zandieh Aimed at improving the proactive benefits of Fuzzy Cognitive Mapping (FCM) for predicting construction project changes, this paper presents CA-FCM: a Context-aware Fuzzy Cognitive Mapping approach. CA-FCM’s main functionality is to imitate the intuitive causal judgements of project experts over change causation in different contextual settings. Invoking the logical inference capabilities of semantic web tools, a hybrid inference mechanism is embedded within the proposed framework which enables establishing contextual connections between prospective causal factors through a semi-automated process of generating relevant causal statements. Hence, CA-FCM can assist decision-makers with (1) a shared sense-making of the domain concepts which would significantly facilitate the manual construction of FCM scenarios, (2) providing contextualized recommendations of causal information required for developing FCM scenarios, (3) dynamic modelling of causal inferences, imitating expert reasoning on change causation and propagation. Towards providing a detailed delineation of CA-FCM’s effectiveness on providing assistance in planning for project changes, a partial implementation of the proposed framework was conducted within a real case scenario.
  • Point cloud occlusion recovery with shallow feedforward neural networks
    • Abstract: Publication date: October 2018Source: Advanced Engineering Informatics, Volume 38Author(s): Luigi Barazzetti 3D scenes reconstructed from point clouds, acquired by either laser scanning or photogrammetry, are subject to data voids generated by occluding objects. Modeling from incomplete data is usually a manual process in which human interpretation plays an essential role. This paper presents a machine learning algorithm based on neural networks capable of recovering point cloud occlusions for surfaces that can be approximated with injective functions. Starting from the point clouds acquired around the occlusion, a set of single-layer feedforward networks with a variable number of neurons is trained and validated with a subset of the original cloud, which is preliminarily decimated using local curvature to reduce CPU cost. The averaged result of the best neural networks is evaluated on a spatial domain that contains the 2D projection of the void, obtaining a complete 3D point cloud for the occluded volume. Criteria for choosing the number of neurons and the activation function for hidden and output layers are illustrated and discussed. Results are presented for both simulated and real occlusions, describing the pros and cons of the proposed method.
  • Prediction of soil compression coefficient for urban housing project using
           novel integration machine learning approach of swarm intelligence and
           Multi-layer Perceptron Neural Network
    • Abstract: Publication date: October 2018Source: Advanced Engineering Informatics, Volume 38Author(s): Dieu Tien Bui, Viet-Ha Nhu, Nhat-Duc Hoang In many engineering projects, the soil compression coefficient is an important parameter used for estimating the settlement of soil layers. The common practice of determining the soil compression coefficient via the oedometer test is time-consuming and expensive. This study proposes a machine learning solution to replace the conventional tests used for obtaining the coefficient of soil compression. The new approach is an integration of the Multi-Layer Perceptron Neural Network (MLP Neural Nets) and Particle Swarm Optimization (PSO). These two computational intelligence methods work synergistically to establish a prediction model of soil compression coefficient. The PSO metaheuristic is employed to optimize the MLP Neural Nets model structure. To train and validate the proposed method, named as PSO-MLP Neural Nets, a dataset of 154 soil samples featuring 12 influencing factors has been collected from the geotechnical investigation process of a high-rise building project. Experimental results show that the proposed PSO-MLP Neural Nets has attained the most accurate prediction of the soil compression coefficient performance with RMSE = 0.0267, MAE = 0.0145, and R2 = 0.884. The result of the proposed model is significantly better than those obtained from other benchmark methods including the backpropagation neural network, the radial basis function neural network, the support vector regression, the random forest, and the Gaussian process. Based on the experimental results, the newly constructed PSO-MLP Neural Nets is very potential to be a new alternative to assist geotechnical engineers in design phase of civil engineering projects.
  • Real-time collaborative reconstruction of digital building models with
           mobile devices
    • Abstract: Publication date: October 2018Source: Advanced Engineering Informatics, Volume 38Author(s): Steffen Franz, Robert Irmler, Uwe Rüppel Many software-based building processes require digital building models. Since the building stock does not have sufficient data in this regard, the demand for Scan-to-BIM processes is increasing. In this paper we present a system for the reconstruction of ‘as-built’ BIM content of house interiors based on the Google Tango technology. The strength of our approach is the use of low-cost mobile scanning devices and a client-server system that allows for a real-time collaborative scanning and reconstruction of indoor scenes. We developed a server application that continuously aggregates scan data of multiple scanning devices (clients) and applies the data stream to a real-time post-processing pipeline to reconstruct rooms, walls, doors and windows. The reconstruction result is then distributed to all clients, where it is visualized in real time. The collaborative workflow and real-time data processing make our system especially useful in situations that are time-critical and require concurrent collection and processing of data. One of our targeted use cases therefore is the model generation for crime scene documentation. The effectiveness of our approach was demonstrated on three test sites. Our results compare well to other state-of-art methods regarding the reconstruction of walls, but they also revealed potential for improvement regarding the detection of doors and windows in occluded and cluttered environments.
  • A decision approach with multiple interactive qualitative objectives for
           product conceptual schemes based on noncooperative-cooperative game theory
    • Abstract: Publication date: October 2018Source: Advanced Engineering Informatics, Volume 38Author(s): Liting Jing, Xiang Peng, Jiquan Li, Jianxiang Wang, Shaofei Jiang Product development based on a morphological matrix involves the process of decision-based design. Although the decision process can generate conceptual schemes under the guidance of qualitative decision objectives, analysis of the interactions among the qualitative objectives is seldom considered, which can lead to unreliable optimal solutions by combining conflicting principle solutions. In addition, due to the ambiguity of the constraints among the qualitative objectives, multiple feasible schemes with equilibrium states are not considered in the concept decision stage. To solve these problems, a decision approach with multiple interactive qualitative objectives is developed for conceptual schemes based on noncooperative-cooperative game theory to consider the tradeoffs among objectives (e.g., cost, quality and operability) using discrete principle solution evaluation data. First, the morphological analysis method can obtain feasible schemes and determine the principle solutions for each subfunction. Second, the principle solutions are quantified using linguistic terms. Then, the subfunctions are categorized through cluster analysis to determine the suitable principle solution. Third, based on the clustering results, a noncooperative game decision model is constructed to identify multiple Nash equilibrium solutions that satisfy the constraints among the objectives. Fourth, a cooperative game decision model is constructed to obtain the optimal scheme as screened by the noncooperative game model. The case study proves that this approach can choose a relatively superior scheme under the existing technical conditions, thereby preventing inconsistency with the actual design expectations.
  • Generation and visualization of earthquake drill scripts for first
           responders using ontology and serious game platforms
    • Abstract: Publication date: October 2018Source: Advanced Engineering Informatics, Volume 38Author(s): Chien-Cheng Chou, An-Ping Jeng, Chun-Ping Chu, Chih-Hsiung Chang, Ru-Guan Wang As there is an increasing number of disasters happening worldwide, numerous mitigation approaches have been proposed to alleviate the impact of disasters. In Taiwan, public and private organizations often work together to prepare various disaster scenarios to train emergency response units. Thus, the design of appropriate drill scripts plays an important role in enhancing the capabilities of first responders in a real disaster. However, developing a reasonable drill script is a time-consuming, error-prone, and costly task. Drill scripts designed may need to accommodate time-dependent, region-specific requirements so that first responders can see varied disaster scenarios for improvement. Therefore, an ontology model with Semantic Web Rule Language (SWRL) constructs is proposed to help the drill script generation process for earthquakes in Taiwan. Drill script designers need to prepare an input data set describing a simulated earthquake using the Taiwan Earthquake Loss Estimation System, a simplified version of the Hazards U.S. – Multi Hazard (HAZUS-MH) program. Then, a drill script following pre-defined rules can be generated and combined with Unity, a serious game platform, in order to display all earthquake-related events in a virtual environment. Additional rules to accommodate varied requirements of an earthquake can be represented by customized SWRL constructs, which can be seamlessly added into the proposed drill script generation process. The developed system is demonstrated using data sets for buildings in Taiwan. During a disaster exercise, first responders can gain better situational awareness regarding an earthquake’s spatiotemporal progress. Finally, it is suggested that first responders review the scene using the proposed approach immediately after a real earthquake, so that improved search and rescue plans can be defined and implemented.
  • Improving construction industry process interoperability with Industry
           Foundation Processes (IFP)
    • Abstract: Publication date: October 2018Source: Advanced Engineering Informatics, Volume 38Author(s): Behrooz Golzarpoor, Carl T. Haas, Derek Rayside, Seokyoung Kang, Matthew Weston With massive amounts of information generated during the life cycle of large-scale construction projects, interoperability among project stakeholders’ information systems is a requirement for effective and timely communication, collaboration, and information exchange, and ultimately for project success. While data interoperability has been substantially improved by initiatives such as IFC (Industry Foundation Classes) standardizing construction industry data, emphasis on process interoperability which facilitates timely and effective exchange of information via interaction of workflow processes is in its early stage. By conforming to a reference model such as IFP (Industry Foundation Processes), project stakeholders can communicate and collaborate using workflow processes while abstracting the information exchange to essential items to preserve their privacy. This paper explores interoperability in the AEC/FM domain, reviews the main components of the IFP system, presents two IFP interoperability models, and discusses their relationships with the IFP system. The models are demonstrated with specific examples and implemented with a process customization framework based on workflow inheritance rules. Interoperability models that conform to the IFP system not only allow seamless information exchange but can also yield active interaction and communication among stakeholders.
  • Visual analysis of asphalt pavement for detection and localization of
    • Abstract: Publication date: October 2018Source: Advanced Engineering Informatics, Volume 38Author(s): Muhammad Haroon Yousaf, Kanza Azhar, Fiza Murtaza, Fawad Hussain Identifying and restoring distresses in asphalt pavement have key significance in durability and long life of roads and highways. A vast number of accidents occurs on the roads and highways due to the pavement distresses. This paper aims to detect and localize one of the critical roadway distresses, the potholes, using computer vision. We have processed images of asphalt pavement for experimentation containing the pothole and non-pothole regions. We proposed a top-down scheme for the detection and localization of potholes in the pavement images. First, we classified pothole/non-pothole images using a bag of words (BoW) approach. We employed and computed famous scale-invariant feature transform (SIFT) features to establish the visual vocabulary of words to represent pavement surface. Support vector machine (SVM) is employed for the training and testing of histograms of words of pavement images. Secondly, we proposed graph cut segmentation scheme to localize the potholes in the labelled pothole images. This paper presents both, subjective and objective evaluation of potholes localization results with the ground truth. We evaluated the proposed scheme on a pavement surface dataset containing the wide-ranging pavement images in different scenarios. Experimentation results show that we achieved an accuracy of 95.7% for the identification of pothole images with significant precision and recall. Subjective evaluation of potholes localization results in high recall with relatively good accuracy. However, the objective assessment shows the 91.4% accuracy for localization of potholes.
  • Automated ergonomic risk monitoring using body-mounted sensors and machine
    • Abstract: Publication date: October 2018Source: Advanced Engineering Informatics, Volume 38Author(s): Nipun D. Nath, Theodora Chaspari, Amir H. Behzadan Workers in various industries are often subject to challenging physical motions that may lead to work-related musculoskeletal disorders (WMSDs). To prevent WMSDs, health and safety organizations have established rules and guidelines that regulate duration and frequency of labor-intensive activities. In this paper, a methodology is introduced to unobtrusively evaluate the ergonomic risk levels caused by overexertion. This is achieved by collecting time-stamped motion data from body-mounted smartphones (i.e., accelerometer, linear accelerometer, and gyroscope signals), automatically detecting workers’ activities through a classification framework, and estimating activity duration and frequency information. This study also investigates various data acquisition and processing settings (e.g., smartphone’s position, calibration, window size, and feature types) through a leave-one-subject-out cross-validation framework. Results indicate that signals collected from arm-mounted smartphone device, when calibrated, can yield accuracy up to 90.2% in the considered 3-class classification task. Further post-processing the output of activity classification yields very accurate estimation of the corresponding ergonomic risk levels. This work contributes to the body of knowledge by expanding the current state in workplace health assessment by designing and testing ubiquitous wearable technology to improve the timeliness and quality of ergonomic-related data collection and analysis.
  • Game-based crowdsourcing to support collaborative customization of the
           definition of sustainability
    • Abstract: Publication date: October 2018Source: Advanced Engineering Informatics, Volume 38Author(s): Mazdak Nik Bakht, Tamer E. El-Diraby, Moein Hossaini Successful adoption and management of sustainable urban systems hinges on the community embracing these systems. Capturing citizens’ ideas, views, and assessments of the built environment will be essential to this goal. In collaborative city planning, these are qualified and valued forms of partial knowledge that should be collectively used to shape the decision making process of urban planning. Among other tools, social media and online social network analytics can provide means to capture elements of such a distributed knowledge. While a structured definition of sustainability (normally dictated in a top-down fashion) may not sufficiently respond well to the pluralist nature of such knowledge acquisition; dealing with the unstructured community inputs, assessments and contributions on social media can be confusing. We can detect fully relevant topics/ideas in community discussions; but they typically suffer from lack of coherence.In this paper, we advocate the use of a semi-structured approach for capturing, analyzing, and interpreting citizens’ inputs. Public officials and professionals can develop the main elements (topical aspects) of sustainability, which can act as the skeleton of a taxonomy. It is however, the community inputs/ideas (in our case collected via social media and parsed), that can shape-up that skeleton and augment those topical aspects with adding the required semantic depth. In more specific terms, we collected tweets for four urban infrastructure mega-projects in North America. Then we used a game-with-a-purpose to crowdsource the identification of topics for a training set of tweets. This was then used to train machine learning algorithms to cluster the rest of collected tweets. We studied the semantic (finding the topics) of tweets as well as their sentiment (in terms of being opposing or supportive of a project). Our classification tested different decision trees with different topic hierarchies. We considered/extracted eight different linguistic features in studying contents of a tweet. Finally, we examined the accuracy of three algorithms in classifying tweets according to the sequence in the tree, and based on the extracted features. These are: K-nearest neighbors, Naïve Bayes classifiers and Support Vector Machines (SVM).Respective to our data set, SVM outperformed other algorithms. Semantic analysis was insensitive to the depth/number of linguistic features considered. In contrast, sentiment analysis was enhanced when part of speech (PoS) was tracked. Interestingly, our work shows that considering the topic (semantic) of a tweet helped enhance the accuracy of sentiment analysis: including topical class as a feature in conducting sentiment analysis results in higher accuracies. This could be used as means to detect the evolution of community opinion: that topic-based social networks are evolving within the communities tweeting about urban projects. It could also be used to identify the topics of top priority to the community or the ones that have the widest spread of views. In our case, these were mainly the impacts of the design and engineering features on social issues.
  • Semi-automated site equipment selection and configuration through formal
           knowledge representation and inference
    • Abstract: Publication date: October 2018Source: Advanced Engineering Informatics, Volume 38Author(s): Katrin Jahr, André Borrmann The selection and configuration of site equipment is a fundamental part of construction preparation. Suitable site equipment supports the timely, cost-efficient and qualitative execution of the construction process. The use of planning tools based on formal knowledge management methods can both speed up the process of construction site planning and lead to better results. In this paper, we propose a rule-based knowledge inference system to support site equipment planners in a semi-automated manner using input data from building information models and working schedules. The knowledge-based system is built using the business rule management system Drools. Using a sample construction site, the feasibility of the proposed approach has been proven.
  • Reconstruction of edges in digital building models
    • Abstract: Publication date: October 2018Source: Advanced Engineering Informatics, Volume 38Author(s): Wolfgang Huhnt Traditionally, the geometry of building components or rooms is either described by their boundary or it is defined by constructive solid models. Modeling tools are available which are based on the principle of constructing building components and of composing a building by adding building components step by step. However, topological relations are relevant besides the shape of components and rooms. These topological relationships are not necessarily explicit information in boundary representation or constructive solid models. As a consequence, they must be computed. At present time, there exists to the best knowledge of the author only a single approach in this subject area that is able to reconstruct topological relations including their geometry. The objects which need to be considered are building components, built-in components and rooms. The challenge is to calculate the three relevant aspects of geometry in digital building models completely and in an efficient way. These three relevant aspects are clashes, voids and contact faces. The existing approach to calculate these aspects is based on space partitioning concepts. Space partitioning concepts store neighboring relations explicitly. The approach presented in this paper is also based on space partitioning. One basic and novel consideration of the approach presented in this paper is to execute the reconstruction procedure in a mesh. The mesh itself is not refined anymore at a certain point during the calculations to avoid uncontrollable refinements. The second basic and novel consideration is the way of avoiding topological inconsistencies. Integer values are chosen for coordinates, and a specific algorithm is presented that guarantees that topological inconsistencies cannot occur. The research presented in this paper addresses the first step on the way to compute clashes, voids and contact faces. This is the reconstruction of edges. This paper presents the theory and a pilot implementation for the reconstruction of straight edges. Examples show the benefits of the approach presented. Open questions are discussed.
  • Overlay Design Methodology for virtual environment design within digital
    • Abstract: Publication date: October 2018Source: Advanced Engineering Informatics, Volume 38Author(s): Ikhwan Kim, SukJoo Hong, Ji-Hyun Lee, Jean-Charles Bazin Virtual landscapes in digital games have become more expressive and impressive. However, design methodologies that can efficiently implement them have rarely been developed. In real-world landscape architecture, a design methodology called Overlay Design Methodology has been commonly utilized that allows efficient spatial design by computing the hierarchy and the placement of the environmental resources. In this paper, we investigate how to apply Overlay Design Methodology for the creation of virtual environments within digital gaming contexts. Along with the establishment of the design methodology, we measure the effectiveness of the methodology with protocol analysis and survey to 30 game developers. As we observed, the Overlay Design Methodology doubled the collaboration among team members and reduced unnecessary time in the design process by over 98%.
  • Knowledge-driven intelligent quality problem-solving system in the
           automotive industry
    • Abstract: Publication date: October 2018Source: Advanced Engineering Informatics, Volume 38Author(s): Zhaoguang Xu, Yanzhong Dang, Peter Munro In the current automotive industry, quality management, especially quality problem-solving (QPS), plays an important role in fulfilling the expectations of demanding customers who seek high-quality products at low-cost. During the problem-solving process, various real-time and historical quality data are often not fully used, yet these data are of high value. This paper provides a comprehensive quality data mining process and method, as well as an intelligent quality problem-solving system (IQPSS). First, based on original quality problem data, an ontology library is constructed using the ontology generating module (OGM). Second, based on the generated ontology and the textual data of the original quality problem, this study builds a quality problem-solving knowledge base (QPSKB) by employing relevant algorithms in the knowledge transformation module (KTM). The component and fault relational matrix mining (CFRMM) algorithm is designed to extract the relationship matrix between the components and faults. The semi-supervised classification algorithm based on the K-nearest neighbor algorithm (KNN) is used to classify the immediate measures, causes and long-term measures into the corresponding ontology and express the ontology as their knowledge. Furthermore, the binary tree-based support vector machine (SVM) approach is applied to classify the cause texts into the factors of Man, Machine, Material, Method, and Environment (4M1E), which are the five factors in a fishbone diagram. In particular, the digital fishbone diagram is a brand-new type of fishbone diagram that subverts the traditional method of fishbone diagram analysis through brainstorming. A pilot run of the IQPSS has been undertaken in an automotive manufacturing company to demonstrate how quality management employees obtain this knowledge by searching in the IQPSS. The results show that the IQPSS contributes appreciably to the quality problem-solving in the manufacturing industry.
  • A comprehensive review on identification of the geomaterial constitutive
           model using the computational intelligence method
    • Abstract: Publication date: October 2018Source: Advanced Engineering Informatics, Volume 38Author(s): Wei Gao It is crucial to determine geomaterial constitutive models to analyze the mechanical behavior of geomaterials and geotechnical engineering stability. Thus, identification of a geomaterial constitutive model is a very important aspect of back analysis. Because the real mechanical behavior of geomaterials are very complicated, it is difficult to identify a suitable geomaterial constitutive model based on traditional methods. Therefore, some computational intelligence methods have been used to solve this problem, and many related studies have been performed. In this study, previous research is reviewed according to the following four aspects: constitutive model approach via an artificial neural network, constitutive model description via an artificial neural network, constitutive model selection via an evolutionary computation, and constitutive model construction via an evolutionary computation. Moreover, the state-of-the-art research advancement of the four research aspects is summarized. The merits and demerits of these research aspects have been comprehensively analyzed and discussed. Finally, possible research directions to identify a geomaterial constitutive model based on computational intelligence are also provided.
  • Quantifying the physical intensity of construction workers, a mechanical
           energy approach
    • Abstract: Publication date: October 2018Source: Advanced Engineering Informatics, Volume 38Author(s): Liulin Kong, Heng Li, Yantao Yu, Hanbin Luo, Martin Skitmore, Maxwell Fordjour Antwi-Afari Construction workers typically undertake highly demanding physical tasks involving various types of stresses from awkward postures, using excessive force, highly repetitive actions, and excessive energy expenditure, which increases the likelihood of unsafe actions, productivity loss, and human errors. Biomechanical models have been developed to estimate joint loadings, which can help avoid strenuous physical exertion, potentially enhancing construction workforce productivity, safety, and well-being. However, the models used are mainly in 2D, or to predict static strength ignored their velocity and acceleration or using marker-based method for dynamic motion data collection. To address this issue, this paper proposes a novel framework for investigating the mechanical energy expenditure (MEE) of workers using a 3D biomechanical model based on computer vision-based techniques. Human 3D Pose Estimation algorithm based on 2D videos is applied to approximate the coordinates of human joints for working postures, and smart insoles are used to collect foot pressures and plantar accelerations, as input data for the biomechanical analyses. The results show a detailed MEE rate for the whole body, at which joints the maximum and minimum values were obtained to avoid excessive physical exertion. The proposed method can approximate the total daily MEE of construction tasks by summing the assumed cost of individual tasks (such as walking, lifting, and stooping), providing suggestions for the design of a daily workload that workers can sustain without developing cumulative fatigue.
  • Capacitated closed-loop supply chain network design under uncertainty
    • Abstract: Publication date: October 2018Source: Advanced Engineering Informatics, Volume 38Author(s): Lu Zhen, Yiwei Wu, Shuaian Wang, Yi Hu, Wen Yi This study optimizes the design of a closed-loop supply chain network, which contains forward and reverse directions and is subject to uncertainty in demands for new & returned products. To address uncertainty in decision-making, we formulate a two-stage stochastic mixed-integer non-linear programming model to determine the distribution center locations and their corresponding capacity, and new & returned product flows in the supply chain network to minimize total design and expected operating costs. We convert our model to a conic quadratic programming model given the complexity of our problem. Then, the conic model is added with certain valid inequalities, such as polymatroid inequalities, and extended with respect to its cover cuts so as to improve computational efficiency. Furthermore, a tabu search algorithm is developed for large-scale problem instances. We also study the impact of inventory weight, transportation weight, and marginal value of time of returned products by the sensitivity analysis. Several computational experiments are conducted to validate the effectiveness of the proposed model and valid inequalities.
  • Selecting manufacturing partners in push and pull-type smart collaborative
    • Abstract: Publication date: October 2018Source: Advanced Engineering Informatics, Volume 38Author(s): Marko Mladineo, Stipo Celar, Luka Celent, Marina Crnjac The idea of Collaborative Manufacturing, also known as the Production Networks or Social Manufacturing, has been around for more than 25 years. It is a production concept based on non-hierarchical collaboration among enterprises often referred as Virtual Enterprise (VE). Despite many scientific research and projects with this topic, it is difficult to find an example of fully operational non-hierarchical production network anywhere in the world. However, that fact could be changed very soon. Namely, the new industrial revolution, called Industry 4.0, encourages industrial enterprises to adopt information-communication technology (ICT) and Internet of Things (IoT) into their production systems, thus creating Cyber-Physical Production System (CPPS). From the aspect of production networks, CPPS represents crucial infrastructure, or a missing link between enterprises. Now, with CPPS in place, non-hierarchical networking and collaboration becomes possible through Smart Collaborative Production Networks. In this research, the concept of information system for Smart Collaborative Production Networks was developed and called ‘VENTIS’. Although the idea of the concept is to manage the collaboration inside Virtual Enterprise, in this research, a special focus has been put on manufacturing planning phase in which optimization problem known as the Partner Selection Problem (PSP) occurs. Since the PSP in manufacturing phase is far more complex than partner selection during the collaborative product development phase, new research premises regarding the Virtual Enterprise type have been set. Two types of Virtual Enterprise business models – Push-type and Pull-type – have been defined in this research. If VE is Push-type, HUMANT algorithm is used to solve PSP that occurs in that case. If VE is Pull-type, a special procedure, inspired by phenomenological reduction, has been established in which set of a priori created VEs is compared with theoretically ‘the best’ VE and theoretically ‘the worst’ VE. Enterprises’ data of production network from Dalmatia (Split-Dalmatia County, Croatia) is used as a Case Study to present ‘VENTIS’ concept and to present the procedure for creation of sustainable Virtual Enterprise.
  • Modeling of transdisciplinary engineering assets using the design platform
           approach for improved customization ability
    • Abstract: Publication date: October 2018Source: Advanced Engineering Informatics, Volume 38Author(s): Samuel André, Fredrik Elgh Original equipment suppliers (OES) that develop unique products are continuously faced with changing requirements during both the quotation and product development processes. This challenge is a different reality from companies that develop off-the-shelf products for the end consumer, which use fixed specifications and where product platforms have been a strong enabler for efficient mass customization. However, product platforms cannot adequately support companies working as OES. The reason is that a high level of customization is required which means that interfaces cannot be standardized, the performance is not negotiable, requirements are not initially fixed, and the specific system interacts with, is affected by, or affects other systems that are simultaneously developed in a transdisciplinary environment. The design platform (DP) approach provides a coherent environment for heterogeneous and transdisciplinary design resources to be used in product development by supporting both designing and off-the-shelf solutions. This research describes the introduction, application and further development of the DP approach at an automotive supplier to support the development of customized solutions when traditional modularity or platform scalability do not suffice. A computer tool called Design Platform Manager has been developed to support the creation and visualization of the DP. The support tool has a connection to a product data management database to link the platform model to the various kinds of engineering assets needed or intended to support variant creation. Finally, the support tool was evaluated by the case company representatives showing promising results.
  • Collaborative engineering decision-making for building information
           channels and improving Web visibility of product manufacturers
    • Abstract: Publication date: October 2018Source: Advanced Engineering Informatics, Volume 38Author(s): Sylvain Sagot, Alain-Jérôme Fougères, Egon Ostrosi Product manufacturers have spent the last years improving productivity and process efficiency in order to face increasingly competitive markets. Today, the visibility of technological innovations has become essential to achieve the targeted market. It is now very difficult for a product manufacturer to reach customers without owning a website that is visible on search engine results pages. The goal of this paper is to build information channels between a company and its customers through improving both a company’s content of information on the Web and its website rank on the Internet through search engine results pages. Company information and knowledge are distributed through multiple stakeholders. The problem of building information channels between a company and customers is solved through a collaborative and distributed approach, on the one hand, and is supported by decision-making tools, on the other hand. The paper proposes an engineering model for building information channels and improving the visibility of the company on the Web. Agents are used for the implementation of the approach. The proposed model and its implementation handle the requirements, constraints, functions and solutions for improving Web visibility. The prototype tool, called CAWIS (Computer Aided Web Information Sharing), examines Web visibility in real time and evaluates the performance of the proposed content of information. CAWIS allows an exploratory and open way for building information channels and improving the visibility of product manufacturers on the Web.
  • Supporting connectivism in knowledge based engineering with graph theory,
           filtering techniques and model quality assurance
    • Abstract: Publication date: October 2018Source: Advanced Engineering Informatics, Volume 38Author(s): Joel Johansson, Manuel Contero, Pedro Company, Fredrik Elgh Mass-customization has forced manufacturing companies to put significant efforts to digitize and automate their engineering and production processes. When new products are to be developed and introduced the production is not alone to be automated. The application of knowledge regarding how the product should be designed and produced based on customer requirements also must be automated. One big academic challenge is helping industry to make sure that the background knowledge of the automated engineering processes still can be understood by its stakeholders throughout the product life cycle.The research presented in this paper aims to build an infrastructure to support a connectivistic view on knowledge in knowledge based engineering. Fundamental concepts in connectivism include network formation and contextualization, which are here addressed by using graph theory together with information filtering techniques and quality assurance of CAD-models. The paper shows how engineering knowledge contained in spreadsheets, knowledge-bases and CAD-models can be penetrated and represented as filtered graphs to support a connectivistic working approach. Three software demonstrators developed to extract filtered graphs are presented and discussed in the paper.
  • Enhanced predictive modelling process of broadband services adoption based
           on time series data
    • Abstract: Publication date: October 2018Source: Advanced Engineering Informatics, Volume 38Author(s): Višnja Križanović, Drago Žagar, Krešimir Grgić, Mario Vranješ In this paper, the importance of the predictive modelling process of broadband services adoption is described. A detailed overview of different analytical models used for prediction, i.e., fitting and forecasting processes of broadband services adoption are presented. Furthermore, a comparison of several analytical models commonly used for prediction of broadband adoption is conducted. In order to more accurately fit to the existing broadband adoption time series data, and to forecast the future broadband services adoption paths, the features of the most accurate common predictive models have been identified for different phases of broadband services adoption. Considering the given results, usage of additional models in the predictive modelling process is analyzed. The objective of these analyses is set to improve the accuracy of the existing predictive modelling process. The accuracy of the predictive modelling process using additional models is tested and compared in different phases of broadband adoption. The model which gives the most accurate results is identified. Finally, in order to enable the usage of this model within a whole broadband service life cycle, as well as to include a greater number of explanatory parameters in predictive modelling process, an enhanced predictive modelling process is proposed.
  • BIM-based modeling and management of design options at early planning
    • Abstract: Publication date: October 2018Source: Advanced Engineering Informatics, Volume 38Author(s): Hannah Mattern, Markus König At early planning phases, the investigation of possible design options helps to find a suitable design for the client despite complex boundary conditions. In this context, the use of BIM models offers new prospects by representing a valuable input for simulation and analysis tasks. This paper aims at enabling a model-based management of design options. An applicable data model needs to be defined that supports the generation and exchange of design options. The developed concept avoids the use of separate models for explicit designs and thus, prevents the creation of redundant information. Furthermore, managing possible design variants within a single model increases the consistency of the provided information. Option categories to represent design options are introduced to structure the complex possibilities which might evolve when proceeding with the design process. Graph Data Models (GDM) are proposed as a transparent approach to describe and manage the resulting models as they provide a structured overview on affected elements and interdependencies. The results of the developed concept are shown by a case study focusing on high-rise buildings. Following the presented approach, architects, designers, contractors and clients are provided with a transparent description of design options which supports the decision making process from the very beginning.
  • Crowdsourcing with online quantitative design analysis
    • Abstract: Publication date: October 2018Source: Advanced Engineering Informatics, Volume 38Author(s): David Birch, Alvise Simondetti, Yi-ke Guo Design is a balancing act between people’s competing concerns, design options and design performance. Recently collecting data on such concerns such as sustainability or aesthetics has become possible through online crowdsourcing, particularly in 3d. However, such systems rarely present more than a single design alternative or allow users to change the design and seldom provide quantitative design analysis to gauge design performance. This precludes a more participatory approach including a wider audience and their insight in the design process.To improve the design process we propose a system to assist the design team in exploring the balance of concerns, design options and their performance. We augment a 3d visualisation crowdsourcing environment with quantitative on-demand assessment of design variants run in the cloud. This enables crowdsourced exploration of the design space and its performance. Automated participant tracking and explicit submitted feedback on design options are collated and presented to aid the design team in balancing the demands of urban master planning. We report application of this system to an urban masterplan with Arup.
  • Machine learning and BIM visualization for maintenance issue
           classification and enhanced data collection
    • Abstract: Publication date: October 2018Source: Advanced Engineering Informatics, Volume 38Author(s): J.J. McArthur, Nima Shahbazi, Ricky Fok, Christopher Raghubar, Brandon Bortoluzzi, Aijun An Occupant-generated work orders are recognized as a good potential data to support Facility Management (FM) activities, however they are unstructured and rarely contain the specific information engineers require to resolve the reported issues. Instead, this often requires multiple trips are often needed to identify the required trade, identify the problem and required parts/tools, and resolve. A key challenge is data quality: free-form (unstructured) text is collected that frequently lacks necessary detail for problem diagnosis. Machine Learning provides new opportunities within the FM domain to improve the quality of information collected through online work order reporting systems by automatically classifying WOs and prompting building occupants with appropriate FM team-developed questions in real time to gather the required specific information in structured form. This paper presents the development, comparison, and application of two sets of supervised machine learning models to perform this classification for WOs generated from occupant complaints. A set of ∼150,000 historical WOs was used for model development and textual classification using with various term and itemset frequency approaches was tested. Classifier prediction accuracies ranged from 46.6% to 81.3% for classification by detailed subcategory; this increased to between 68% (simple term frequency) to 90% (random forest) when the dataset only included the ten most common (accounting for 70% of all WOs) subcategories. Hierarchical classification decreased performance. An FM-BIM integration approach is finally presented using the resultant classifiers to provide facilities management teams with spatio-temporal visualization of the work order categories across a series of buildings to help prioritize and streamline operations and maintenance task assignments.
  • BIM-based investigation of total energy consumption in delivering building
    • Abstract: Publication date: October 2018Source: Advanced Engineering Informatics, Volume 38Author(s): Cheng Zhang, Raja Shahmir Nizam, Lu Tian Considerable efforts have been made to reduce buildings’ operational energy use over the last decades, but little attention has been paid to reduce the material transportation and construction energy. Focusing only on the operation phase forgoes the opportunity to reduce other building-related energy consumption, and even if the environmental impacts arising from construction and transportation are small as compared to the operation phases, its cumulative impact at the national level is of concern.The energy consumed by a building is divided into two parts embodied energy and operation energy. Further, the embodied energy is constituted of energy intensity of materials, energy consumed during transportation and energy consumed for construction. This paper proposes a methodology to integrate embodied energy consumption into a BIM platform and provides a seamless analysis based on available information. Plug-ins are developed to fulfill a convenient linkage between the BIM model and external databases. Simulation models are created, which can be used as templates for energy optimization during transportation and construction. By analyzing different resource combination scenarios, lower energy consumption can be achieved.
  • A vision-based statistical methodology for automatically modeling
           continuous urban traffic flows
    • Abstract: Publication date: October 2018Source: Advanced Engineering Informatics, Volume 38Author(s): Hana Rabbouch, Foued Saâdaoui, Rafaa Mraihi We introduce an online video-based virtual sensor allowing to automatically estimate and forecast the number of vehicles passing through a road section over a continuous time interval. The strategy consists, in the first place, in defining a Motion Intensity Index (MII) whose role is to quantify the visual activity in a traffic video. A wavelet-based cause-and-effect statistical model is then used to match the actual number of vehicles to their respective motion scores. This leads to an efficient estimator of the urban traffic flow. The implementation is well optimized in such a way that the local sampling rate is directly proportional to the amount of visual activity in localized sub-shot units of the video. The procedure allows designing an autonomous sensor giving every moment a measure of the flow on a road section and an expectation of its future levels. The device can be very useful for optimizing transportation management, facilitating strategic decision-making, and analyzing networks with the purpose of optimizing transportation equipment efficiency.
  • A novel CRDT-based synchronization method for real-time collaborative CAD
    • Abstract: Publication date: October 2018Source: Advanced Engineering Informatics, Volume 38Author(s): Xiao Lv, Fazhi He, Yuan Cheng, Yiqi Wu CRDT (Conflict-free Replicated Data Type) has been proposed as an alternative synchronization mechanism for collaborative text editing systems in recent years. However, CRDT-based synchronization methods for collaborative systems with sophisticated objects, such as collaborative CAD (Co-CAD) systems, are rarely investigated in previous literatures. How well CRDT-based synchronization methods for Co-CAD systems could perform remains unknown. This paper presents a novel CRDT-based synchronization method to maintain eventual consistency for the feature-based CAD model. Firstly, three operation relations are defined as the dependency-conflict relation, the mutual exclusive relation and the compatible relation in context of the feature-based CAD systems. Secondly, a feature-based conflict detection mechanism is proposed to detect the three relations. Thirdly, a feature-based conflict resolution approach under the CRDT framework is presented to solve the conflicts. Fourthly, the time complexity and the space complexity are analyzed in theory. Finally, case studies of collaborative modeling procedures verify the correctness and feasibility of the proposed method.
  • Feature-based intelligent system for steam simulation using computational
           fluid dynamics
    • Abstract: Publication date: October 2018Source: Advanced Engineering Informatics, Volume 38Author(s): Lei Li, Carlos F. Lange, Zhen Xu, Pingyu Jiang, Yongsheng Ma In the development of products involving fluids, computational fluid dynamics (CFD) has been increasingly applied to investigate the flow associated with various product operating conditions or product designs. The batch simulation is usually conducted when CFD is heavily used, which is not able to respond to the changes in flow regime when the fluid domain changes. In order to overcome this defect, a rule-based intelligent CFD simulation system for steam simulation is proposed to analyze the specific product design and generate the corresponding robust simulation model with accurate results. The rules used in the system are based on physical knowledge and CFD best practices which make this system easy to be applied in other application scenarios by changing the relevant knowledge base. Fluid physics features and dynamic physics features are used to model the intelligent functions of the system. Incorporating CAE boundary features, the CFD analysis view is fulfilled, which maintains the information consistency in a multi-view feature modeling environment. The prototype software tool is developed by Python 3 with separated logics and settings. The effectiveness of the proposed system is proven by the case study of a disk-type gate valve and a pipe reducer in a piping system.
  • Cloud-based ubiquitous object sharing platform for heterogeneous logistics
           system integration
    • Abstract: Publication date: October 2018Source: Advanced Engineering Informatics, Volume 38Author(s): Ming Li, Peng Lin, Gangyan Xu, George Q. Huang The intelligence of infrastructure gradually becomes the straw for logistics enterprises to make data-based or date-driven optimization. The integration of heterogeneous logistics systems with existing enterprise information systems is one of the most critical steps to achieve the intelligent infrastructure. Unfortunately, the integration is always a time-consuming process with heavy investment, which suppresses the longings of enterprises, especially for small and medium enterprises (SMEs). Aiming at simplifying the system integration, this paper proposed a cloud-based ubiquitous object sharing platform (CUOSP) to share the integration across SMEs based on the concept of sharing economy. CUOSP acts as a middleware system to make heterogeneous logistics systems universal plug-and-play (UPnP) for enterprise information systems. A kernel-based agent (KBA) is designed as the sharing entity of physical systems. It maintains the features of physical systems and is scalable for different application scenarios. A series of cloud gateway services are emerged not only to provide the basic running and sharing environment, but also to remedy KBA’s weaknesses in computing capacity. A prototype system is developed and implemented based on the framework of CUOSP and a laboratory case according to the consolidation scenario in E-commerce logistics is demonstrated. Comparison experiments are also conducted to explore the real-time and multitasking capacity of KBAs with different kernel characteristics and different computing resources.
  • An integrated framework for multi-criteria optimization of thin concrete
           shells at early design stages
    • Abstract: Publication date: October 2018Source: Advanced Engineering Informatics, Volume 38Author(s): Carlos Gomes, Manuel Parente, Miguel Azenha, José Carlos Lino Thin shells are crucially dependent on their shape in order to obtain proper structural performance. In this context, the optimal shape will guarantee performance and safety requirements, while minimizing the use of materials, as well as construction/maintenance costs.Thin shell design is a team-based, multidisciplinary, and iterative process, which requires a high level of interaction between the various parties involved, especially between the Architecture and Engineering teams. As a result of technological development, novel concepts and tools become available to support this process. On the one hand, concepts like Integrated Project Delivery (IPD) show the potential to have a high impact on multidisciplinary environments such as the one in question, supporting the early decision-making process with the availability of as much information as possible. On the other hand, optimization techniques and tools should be highlighted, as they fit the needs and requirements of both the shell shape definition process and the IPD concept. These can be used not only to support advanced design stages, but also to facilitate the initial formulation of shape during the early interactions between architect and structural engineer from an IPD point of view.This paper proposes a methodology aimed at enhancing the interactive and iterative process associated with the early stages of thin shell design, supported by an integrated framework. The latter is based on several tools, namely Rhinoceros 3D, Grasshopper, and Robot Structural Analysis. In order to achieve full integration of the support tools, a custom devised module was developed, so as to allow interoperability between Grasshopper and Robot Structural Analysis. The system resorts to various technologies targeted at improving the shell shape definition process, such as formfinding techniques, parametric and generative models, as well as shape optimization techniques that leverage on multi criteria evolutionary algorithms. The proposed framework is implemented in a set of fictitious scenarios, in which the best thin reinforced concrete shell structures are sought according to given design requirements. Results stemming from this implementation emphasize its interoperability, flexibility, and capability to promote interaction between the elements of the design team, ultimately outputting a set of diverse and creative shell shapes, and thus supporting the pre-design process.
  • Community detection in national-scale high voltage transmission networks
           using genetic algorithms
    • Abstract: Publication date: October 2018Source: Advanced Engineering Informatics, Volume 38Author(s): Manuel Guerrero, Francisco G. Montoya, Raúl Baños, Alfredo Alcayde, Consolacíon Gil The large-scale interconnection of electricity networks has been one of the most important investments made by electric companies, and this trend is expected to continue in the future. One of the research topics in this field is the application of graph-based analysis to identify the characteristics of power grids. In particular, the application of community detection techniques allows for the identification of network elements that share valuable properties by partitioning a network into some loosely coupled sub-networks (communities) of similar scale, such that nodes within a community are densely linked, while connections between different communities are sparser. This paper proposes the use of competitive genetic algorithms to rapidly detect any number of community structures in complex grid networks. Results obtained in several national- scale high voltage transmission networks, including Italy, Germany, France, the Iberian peninsula (Spain and Portugal), Texas (US), and the IEEE 118 bus test case that represents a portion of the American Electric Power System (in the Midwestern US), show the good performance of genetic algorithms to detect communities in power grids. In addition to the topological analysis of power grids, the implications of these results from an engineering point of view are discussed, as well as how they could be used to analyze the vulnerability risk of power grids to avoid large-scale cascade failures.Graphical abstractGraphical abstract for this article
  • Automatic code compliance with multi-dimensional data fitting in a BIM
    • Abstract: Publication date: October 2018Source: Advanced Engineering Informatics, Volume 38Author(s): P. Patlakas, A. Livingstone, R. Hairstans, G. Neighbour BIM-based tools can contribute to addressing some of the challenges faced by structural engineering practitioners. A BIM-based framework for the development of components that deliver Automatic Code Compliance (ACC) is presented. The structural design problems that such components solve are categorised as simple, where ACC can be implemented directly, or complex, where more advanced approaches are needed. The mathematical process of Multi-Dimensional Data Fitting (MDDF) is introduced in order for the latter, enabling the compression of complex engineering calculations to a single equation that can be easily implemented into a BIM software engineering package. Proof-of-concept examples are given for both cases: offsite-manufactured structural joists are utilised as a non-recursive example, implementing the results obtained in the manufacturer’s literature; the axial capacity of metal fasteners in axially loaded timber-to-timber connections are utilised as an example of recursive problems. The MDDF analysis and its implementation in a BIM package of those problems are presented. Finally, the concept is generalised for non-structural aspects at a framework level, and the challenges, implications, and prospects of ACC in a BIM context are discussed.
  • 3 D +reconstruction+based+on+a+robot+equipped+with+uncalibrated+infrared+stereovision+cameras&rft.title=Advanced+Engineering+Informatics&rft.issn=1474-0346&">Automated thermal 3 D reconstruction based on a robot equipped with
           uncalibrated infrared stereovision cameras
    • Abstract: Publication date: October 2018Source: Advanced Engineering Informatics, Volume 38Author(s): T. Sentenac, F. Bugarin, B. Ducarouge, M. Devy In many industrial sectors, Non Destructive Testing (NDT) methods are used for the thermomechanical analysis of parts in assemblies of engines or reactors or for the control of metal forming processes. This article suggests an automated multi-view approach for the thermal 3D reconstruction required in order to compute 3D surface temperature models. This approach is based only on infrared cameras mounted on a Cartesian robot.The low resolution of these cameras associated to a lack of texture to infrared images require to use a global approach based first on an uncalibrated rectification and then on the simultaneous execution, in a single step, of the dense 3D reconstruction and of an extended self-calibration.The uncalibrated rectification is based on an optimization process under constraints which calculates the homographies without prior calculation of the Fundamental Matrix and which minimizes the projective deformations between the initial images and the rectified ones.The extended self-calibration estimates both the parameters of virtual cameras that could provide the rectified images directly, and the parameters of the robot. It is based on two criteria evaluated according to the noise level of the infrared images. This global approach is validated through the reconstruction of a hot object against a reference reconstruction acquired by a 3D scanner.
  • Data based complex network modeling and analysis of shield tunneling
           performance in metro construction
    • Abstract: Publication date: October 2018Source: Advanced Engineering Informatics, Volume 38Author(s): C. Zhou, L.Y. Ding, Miroslaw J. Skibniewski, Hanbin Luo, H.T. Zhang Shield tunneling performance depends mainly on changes in geological conditions and machine working status. Understanding its characteristics is the key to operating and controlling shield machine during the metro construction. Despite the large set of shield tunneling data in having been a big challenge in interpreting the underlying meaning, a systematical view of the shield tunneling performance has not yet been identified. In this study, a methodology for the modeling and analysis of shield tunneling performance network is proposed which aims at integrating the high dimensional data mining and the complex network approaches for shield performance evaluation. It is tested by analyzing the heterogeneous data of shield tunneling performance acquired from in the first Yangtze river crossing metro tunnel project in China. Each segment ring tunneling cycle in the construction were considered to be nodes of the network mapped while edges are determined by nodes having the similarity greater than an optimal threshold value. The construct network exhibits high clustering coefficient combined with comparatively short path lengths, which demonstrates a small world topology feature. Communities in the performance network with different size based on the complex network are detected, which provides the vital decision information for geological conditions identification and shield tunneling performance risk evaluation.
  • A foundational ontology for the modelling of manufacturing systems
    • Abstract: Publication date: October 2018Source: Advanced Engineering Informatics, Volume 38Author(s): Viktor Zaletelj, Rok Vrabič, Elvis Hozdić, Peter Butala Models of distributed manufacturing systems cannot be consistent without a formal ontology. In this paper, the ontology formulation and maintenance are addressed in the scope of a collaborative modelling environment – in which concurrency, consistency, and model life cycle management should be supported. Thus, an extensible foundational ontology for manufacturing – system modelling is proposed in which the formal definitions of the modelling environment itself enable the definition of the manufacturing system’s elements. The presented approach ensures the consistency of ever-changing models. The ontology is integrated into a modelling framework through the concept of description layers that assist in the management of the model description’s complexity. The feasibility of the approaches is illustrated in an industrial case study that models of a manufacturing system for material processing.
  • Balancing homogeneity and heterogeneity in design exploration by
           synthesizing novel design alternatives based on genetic algorithm and
           strategic styling decision
    • Abstract: Publication date: October 2018Source: Advanced Engineering Informatics, Volume 38Author(s): Kyung Hoon Hyun, Ji-Hyun Lee Designers constantly and consistently draft and develop both general concepts and directions to identify the solution that best fits the styling objectives of the lead designer. Designers often confront design fixations that cognitively clash to explore different design combinations. As design teams explore the range of possible design spaces of a certain design strategy, there is an opportunity for computational approaches to improve the styling process. By implementing product appearance similarity and styling strategy in computational design synthesis, it is possible to discover combinations that would otherwise remain unexplored by human designers. Numerous studies on design synthesis have been conducted. However, there has been no focus on the morphological synthesis of designs with strategic styling decisions. Considering this, the proposed study develops a method to synthesize car styling based on product appearance similarity for effective design exploration in the concept generation phase. The similarities of products across different generations, product portfolios, and competitors’ products are calculated to evaluate the strategic styling decision. The results of the strategic styling decision are used to formulate a fitness function. Car styling is then synthesized with a genetic algorithm based on this fitness function to generate car styling in accordance with the target strategic styling decision. In this respect, designers can computationally synthesize novel design alternatives that consider both homogeneity (family look in design) and heterogeneity (design trend in the market) by pinpointing the desired design exploration area. Ultimately, the style synthesis methodology proposed in this research can help designers to utilize the gradual visualization of styling strategies for more effective and efficient managerial design decisions. To do this, we conduct five major tasks: first, car design data are collected for design synthesis; second, the product appearance similarity is calculated to measure the strategic styling decision; third, synthesis validation is conducted to test whether the proposed methodology can create outside-the-box designs; fourth, a genetic algorithm is used to synthesize car designs in consideration of the strategic styling decision; finally, a series of in-depth interviews with experts and validation experiments are conducted with in-house automobile designers to examine the impact of the proposed methodology. The results showed that designers can quantitatively measure and compare the styling strategies of each car brand, then implement design upgrades, while still maintaining that specific style. Correspondingly, computationally generated design alternatives improve the satisfaction in ease, time, objective reflection and novelty of design outcomes when formulating design strategies in the concept generation phase.
  • Fault diagnosis of rolling bearing based on optimized soft competitive
           learning Fuzzy ART and similarity evaluation technique
    • Abstract: Publication date: October 2018Source: Advanced Engineering Informatics, Volume 38Author(s): Xiao-Jin Wan, Licheng Liu, Zengbing Xu, Zhigang Xu, Qinglei Li, Fengxiang Xu In this work, a new classification method called Soft Competitive Learning Fuzzy Adaptive Resonance Theory (SFART) is proposed to diagnose bearing faults. In order to solve the misclassification caused by the traditional Fuzzy ART based on hard competitive learning, a soft competitive learning ART model is established using Yu’s norm similarity criterion and lateral inhibition theory. The proposed SFART is based on Yu’s norm similarity criterion and soft competitive learning mechanism. In SFART, Yu’s similarity criterion and the lateral inhibition theory were employed to measure the proximity and select winning neurons, respectively. To further improve the classification accuracy, a feature selection technique based on Yu’s norms is also proposed. In addition, Particle Swarm Optimization (PSO) is introduced to optimize the model parameters of SFART. Meanwhile, the validity of the feature selection technique and parameter optimization method is demonstrated. Finally, fuzzy ART/ ARTMAP (FAM) as well as the feasibility of the proposed SFART algorithm are validated by comparing the diagnosis effectiveness of the proposed algorithm with the classic Fuzzy c-means (FCM), Fuzzy ART and fuzzy ARTMAP (FAM).
  • Deep-learning neural-network architectures and methods: Using
           component-based models in building-design energy prediction
    • Abstract: Publication date: October 2018Source: Advanced Engineering Informatics, Volume 38Author(s): Sundaravelpandian Singaravel, Johan Suykens, Philipp Geyer Increasing sustainability requirements make evaluating different design options for identifying energy-efficient design ever more important. These requirements demand simulation models that are not only accurate but also fast. Machine Learning (ML) enables effective mimicry of Building Performance Simulation (BPS) while generating results much faster than BPS. Component-Based Machine Learning (CBML) enhances the capabilities of the monolithic ML model. Extending monolithic ML approach, the paper presents deep-learning architectures, component development methods and evaluates their suitability for space exploration in building design. Results indicate that deep learning increases the performance of models over simple artificial neural network models. Methods such as transfer learning and Multi-Task Learning make the component development process more efficient. Testing the deep-learning model on 201 new design cases indicates that its cooling energy prediction (R2: 0.983) is similar to BPS, while errors for heating energy predictions (R2: 0.848) are higher than BPS. Higher heating energy prediction error can be resolved by collecting heating data using better design space sampling methods that cover the heating demand distribution effectively. Given that the accuracy of the deep-learning model for heating predictions can be increased, the major advantage of deep-learning models over BPS is their high computation speed. BPS required 1145 s to simulate 201 design cases. Using the deep-learning model, similar results can be obtained in 0.9 s. High computation speed makes deep-learning models suitable for design space exploration.Graphical abstractGraphical abstract for this article
  • Real-time validation of vision-based over-height vehicle detection system
    • Abstract: Publication date: October 2018Source: Advanced Engineering Informatics, Volume 38Author(s): Bella Nguyen, Ioannis Brilakis Over-height vehicle strikes with low bridges and tunnels are an ongoing problem worldwide. While previous methods have used vision-based systems to address the over-height warning problem, such methods are sensitive to wind. In this paper, we perform a full validation of the system using a constraint-based approach to minimize the number of over-height vehicle misclassifications due to windy conditions. The dataset includes a total of 102 over-height vehicles recorded at frame rates of 25 and 30fps. An analysis is performed of wind and vehicle displacements to track over-height features using optical flow paired with SURF feature detectors. Motion captured within the region of interest was treated as a standard two-class binary linear classification problem with 1 indicating over-height vehicle presence and 0 indicating noise. The algorithm performed with 100% recall, 83.3% precision, false positive rate of 0.2% and warning accuracy of 96.6%.
  • BIMification: How to create and use BIM for retrofitting
    • Abstract: Publication date: October 2018Source: Advanced Engineering Informatics, Volume 38Author(s): Raimar J. Scherer, Peter Katranuschkov Building Information Modeling (BIM) is rapidly advancing as an efficient new approach to cooperative building design and construction. However, BIM methodology is still mainly developed and applied for new building projects. The strong societal needs to improve the quality and the overall performance of the existing building stock, especially with regard to energy use, are yet insufficiently supported by BIM. In this paper we propose a structured approach towards the creation of a building information model of an existing building and its use for the purpose of retrofitting or renovation, based on the standard IFC specification (ISO 16739). It implies a process we define as BIMification. This process undergoes two major stages: (1) Anamnesis, dedicated to the survey and collection of facts about the building, and (2) Diagnosis, dedicated to the analysis and interpretation of the collected facts to obtain the necessary understanding of the building and its performance and prepare for the retrofitting design. The paper outlines the broader research aim that triggered the development of the suggested approach and presents the overall concept and methodology, the ICT platform under implementation and the current state of the work. Discussed are also the scope of the approach, envisaged perspectives and further development efforts.Graphical abstractGraphical abstract for this article
  • Automated continuous construction progress monitoring using multiple
           workplace real time 3D scans
    • Abstract: Publication date: October 2018Source: Advanced Engineering Informatics, Volume 38Author(s): Zoran Pučko, Nataša Šuman, Danijel Rebolj In recent years, exponential growth has been detected in research efforts focused on automated construction progress monitoring. Despite various data acquisition methods and approaches, the success is limited. This paper proposes a new method, where changes are constantly perceived and as-built model continuously updated during the construction process, instead of periodical scanning of the whole building under construction. It turned out that low precision 3D scanning devices, which are closely observing active workplaces, are sufficient for correct identification of the built elements. Such scanning devices are small enough to fit onto workers’ protective helmets and on the applied machinery. In this way, workers capture all workplaces inside and outside of the building in real time and record partial point clouds, their locations, and time stamps. The partial point clouds are then registered and merged into a complete 4D as-built point cloud of a building under construction. Identification of as-designed BIM elements within the 4D as-built point cloud then results in the 4D as-built BIM. Finally, the comparison of the 4D as-built BIM and the 4D as-designed BIM enables identification of the differences between both models and thus the deviations from the time schedule. The differences are reported in virtual real-time, which enables more efficient project management.
  • Decentralized damage detection of seismically-excited buildings using
           multiple banks of Kalman estimators
    • Abstract: Publication date: October 2018Source: Advanced Engineering Informatics, Volume 38Author(s): Jau-Yu Chou, Chia-Ming Chang Natural hazards result in ill-conditioned structures with unfavorable damage. To early recognize damage existence, structures can be screened by damage detection methods after a critical hazard event. These damage detection methods are often developed based on a centralized acquiring and computing system that challenges the feasibility of deployment in a large-scale structure. Decentralized damage detection methods alter a single system to multiple subsystems that allow spatially distributing in a structure and yield comparable performance with the centralized approach. In this study, a decentralized damage detection method based on modal prediction errors via multiple banks of Kalman estimators is proposed. First, a sensor network is comprised of multiple subsystems over a structure of which the subsystems have overlapped sensing nodes. These subsystems are individually identified by an input–output frequency-domain system identification method under ambient vibrations. The identified models are then converted into several banks of Kalman estimators, and the estimators generate the estimation of structural modal responses. The prediction errors are calculated from the differentiation between measured and estimated modal responses, and the accumulated standard deviations of modal prediction errors serve as the damage indices for recognizing the damage occurrence, locations, and levels. A numerical example is introduced to demonstrate the proposed method as well as to evaluate the detection effectiveness. Moreover, the proposed method is also experimentally verified by a scaled twin-tower building using shake table testing. The experimental results indicate that the proposed method is quite effective to inform damage of structures in terms of damage occurrence, locations, and levels.
  • Semantic weldability prediction with RSW quality dataset and knowledge
    • Abstract: Publication date: October 2018Source: Advanced Engineering Informatics, Volume 38Author(s): Kyoung-Yun Kim, Fahim Ahmed This paper presents a semantic Resistance Spot Welding (RSW) weldability prediction framework. The framework constructs a shareable weldability knowledge database based on the regression rules from inconsistent RSW quality datasets. This research aims to effectively predict the weldability of RSW process for existing or new weldment design. A real welding test dataset collected from an automotive OEM is used to extract decision rules using a decision tree algorithm, Classification and Regression Trees (CART). The extracted decision rules are converted systematically into SWRL rules for capturing the semantics and to increase the shareability of the constructed knowledge. The experiments show that the RSW ontology, along with SWRL rules that contains weldability rules constructed from the datasets, successfully predicts the weldability (nugget width) values for RSW cases. The predicted nugget width values are found to be in-close proximity of the actual values. This paper shows that semantic prediction framework construes an intelligent way for constructing accurate and transparent predictive models for RSW weldability verification.
  • A methodology for brand feature establishment based on the decomposition
           and reconstruction of a feature curve
    • Abstract: Publication date: October 2018Source: Advanced Engineering Informatics, Volume 38Author(s): Shih-Wen Hsiao, Chu-Hsuan Lee, Rong-Qi Chen, Chien-Yu Lin For creative products, maintaining original brand elements and features in a new product is an important issue in the design process as brand features are conceived and generated for longevity. However, current methods rely on designers’ abilities, and the size of forms is easily affected when shape morphing is applied, causing limitations in computer-aided design. In order to focus on design while preserving key features, a systematic method for presenting brand features is proposed in this article. In this method, the feature curves of the brand features of a company are decomposed with defined feature parameters, which were then used to reconstruct the feature curve of the designed product in the design stage by using a residual modified gray prediction model. A classic vehicle configuration design is taken as an example to show the implementation procedure of the proposed method. With residual modification, this method can also assimilate other forms from the original form database, and generate new forms based on gray prediction. The results show that brand features can be retained in the newly designed product based on the proposed method. Though vehicle design is taken as the example, this method can also be used to develop designs for many other the brand features. For classic products with historical value, this method can generate new forms that maintain original brand features, thereby satisfying customers’ needs for brand authenticity.
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