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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: 96, 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: 421, 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: 276, 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: 167, 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: 28, 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: 20, 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: 29, 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: 17)
Advances in Genetics     Full-text available via subscription   (Followers: 19, 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: 10, 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: 4)
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: 25, 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: 403, 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: 358, 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: 463, 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: 56, SJR: 3.267, CiteScore: 4)
American J. of Cardiology     Hybrid Journal   (Followers: 59, 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: 11)
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: 28, 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: 225, 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: 19, 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: 43, SJR: 1.512, CiteScore: 5)
Analytical Biochemistry     Hybrid Journal   (Followers: 188, 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: 13)
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: 205, 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]
  • Data-driven operational risk analysis in E-Commerce Logistics
    • Abstract: Publication date: April 2019Source: Advanced Engineering Informatics, Volume 40Author(s): Gangyan Xu, Xuan Qiu, Meng Fang, Xiaofei Kou, Ying Yu The efficiency of E-Commerce Logistics (ECL) has become a major success factor for e-commerce companies in the competitive marketplace nowadays. However, the operation of ECL is complex and vulnerable to many risks, which would severely threaten its performance. A clear understanding of these risks would benefit a lot for conducting targeted measures to effectively mitigate their adverse effects. Therefore, this paper proposes a quantitatively analysis approach for operational risks in ECL based on extensive historical e-commerce transaction data. More specifically, the typical operation process of ECL is extracted through sequential analysis of key activities. After that, taking operation time as the key performance indicator, the performance patterns of different operation phases are analyzed. Then, considering the diverse distributions of operation time in different phases, especially the multimodal distribution of transportation time, a Gaussian Mixture Model (GMM) based risk analysis approach is proposed. Finally, an experimental case study is provided to measure the operational risks using real-life ECL data, and several managerial implications are also discussed based on the results.
       
  • Automated BIM data validation integrating open-standard schema with visual
           programming language
    • Abstract: Publication date: April 2019Source: Advanced Engineering Informatics, Volume 40Author(s): Pedram Ghannad, Yong-Cheol Lee, Johannes Dimyadi, Wawan Solihin A building design must comply with a wide spectrum of requirements stipulated by building codes, normative standards, owner’s specifications, industry’s guidelines, and project requirements. The current rule-based compliance checking practice is a costly bottleneck in a building project, and thus, there is a demand for a design evaluation process that incorporates automated checking capabilities to address the inefficiency and the error-prone nature of the current manual checking practice. The inherent complexity of building design rules and impracticability of existing automated checking approaches are two key challenges that must be addressed to enable practical compliance checking automation. This research study proposes a new modularized framework that integrates the emerging open standard, LegalRuleML, with a Visual Programming Language. The framework allows a standardized method of defining design rules in a machine-readable and executable format. The proposed approach encompasses the entire compliance checking process from the interpretation of natural language-based requirements to machine-readable rules, rule categorization, rule parameterization, and the execution of the rules on the ISO-standard building information model. This modularized BIM-based design validation framework is expected to help automatically and iteratively evaluate the level of quality and defects of information conveyed in a given building model as an essential part of the early design process.
       
  • Empty container repositioning strategy in intermodal transport with demand
           switching
    • Abstract: Publication date: April 2019Source: Advanced Engineering Informatics, Volume 40Author(s): Tian Luo, Daofang Chang In this paper, we study the empty container inventory repositioning problem with customer demand switching in intermodal transport. The objective of this article is to solve the empty container repositioning problem by contract coordination theory, and to improve the coordination of empty container management and the profit of each participant. We consider an intermodal transport system composed of the rail firm and the liner firm. First, we have considered the situation of no cooperation between the dry port and the seaport, and established a model where there is only the customer demand switching without the occurrence of empty container repositioning. Next, we consider the cooperation between the dry port and the seaport in the decentralized model and the centralized model, and set up the empty container repositioning models from the seaport to the dry port respectively. We analyse the optimal inventory level for the dry port and the seaport under different models, and the effect of the repositioning price on the optimal inventory level. We then apply the contract coordination theory to the empty container inventory repositioning problem. We propose an inventory coordination strategy based on a revenue sharing contract and coordinate the intermodal transport system by choosing the appropriate contract parameters. The results of the study show that under the guidance of the seaport, the revenue sharing contract can achieve a win-win situation for the dry port and the seaport.
       
  • Enabling metrology-oriented specification of geometrical variability
           – A categorical approach
    • Abstract: Publication date: January 2019Source: Advanced Engineering Informatics, Volume 39Author(s): Qunfen Qi, Luca Pagani, Xiangqian Jiang, Paul J. Scott In this paper a metrology-oriented specification schema is proposed to enrich the specification semantics with sufficient metrological information. It is designed particularly for applications where non-traditional measurement methods are applied; and it can also identify any redundancies, inconsistencies or incompletenesses of a specification. The proposed schema is based on category theoretical semantics which uses category theory as the foundation to model the semantics. A set of verification operations that derived from the measurement process was firstly formalised using the categorical semantics. Then a set of full faithful functors were constructed to map the set of verification operations to a set of specification operations. A set of simplification rules was then developed to deduce all of the necessary specification objects which are independent to each other. Then the residual specification objects provide a compact structure of the specification. Three test cases were conducted to validate the proposed schema. An industrial computed tomography (CT) measurement process for an impeller manufacturing using selective laser sintering (SLS) technique, was modelled and a set of independent specification elements was then deduced. The other two test cases for checking redundancy and incompleteness on general ISO specifications were carried out. The results show that the proposed schema works for proposing semantic enriched specification that are characterised by non-traditional measurement methods and for testing redundancy and incompleteness of specifications based on geometrical product specifications and verification (GPS) standards system.
       
  • 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
           information
    • 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
           technology
    • 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.
       
 
 
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