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Publisher: Elsevier   (Total: 3183 journals)

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Showing 1 - 200 of 3183 Journals sorted alphabetically
Academic Pediatrics     Hybrid Journal   (Followers: 37, SJR: 1.655, CiteScore: 2)
Academic Radiology     Hybrid Journal   (Followers: 25, SJR: 1.015, CiteScore: 2)
Accident Analysis & Prevention     Partially Free   (Followers: 101, SJR: 1.462, CiteScore: 3)
Accounting Forum     Hybrid Journal   (Followers: 27, SJR: 0.932, CiteScore: 2)
Accounting, Organizations and Society     Hybrid Journal   (Followers: 38, 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: 434, SJR: 0.758, CiteScore: 2)
Acta Automatica Sinica     Full-text available via subscription   (Followers: 2)
Acta Biomaterialia     Hybrid Journal   (Followers: 27, SJR: 1.967, CiteScore: 7)
Acta Colombiana de Cuidado Intensivo     Full-text available via subscription   (Followers: 3)
Acta de Investigación Psicológica     Open Access   (Followers: 3)
Acta Ecologica Sinica     Open Access   (Followers: 11, SJR: 0.18, CiteScore: 1)
Acta Histochemica     Hybrid Journal   (Followers: 3, SJR: 0.661, CiteScore: 2)
Acta Materialia     Hybrid Journal   (Followers: 297, 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: 2, 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: 25, 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: 7, 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: 15, 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: 9, 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: 177, 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: 9, SJR: 0.277, CiteScore: 1)
Advances in Agronomy     Full-text available via subscription   (Followers: 16, SJR: 2.384, CiteScore: 5)
Advances in Anesthesia     Full-text available via subscription   (Followers: 29, 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: 11, 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: 15, 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: 5)
Advances in Cellular and Molecular Biology of Membranes and Organelles     Full-text available via subscription   (Followers: 14)
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: 13)
Advances in Digestive Medicine     Open Access   (Followers: 12)
Advances in DNA Sequence-Specific Agents     Full-text available via subscription   (Followers: 7)
Advances in Drug Research     Full-text available via subscription   (Followers: 26)
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: 49, 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: 65, 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: 20, 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: 25, SJR: 0.368, CiteScore: 1)
Advances in Heterocyclic Chemistry     Full-text available via subscription   (Followers: 11, 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: 3, 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: 20, SJR: 0.88, CiteScore: 2)
Advances in Mathematics     Full-text available via subscription   (Followers: 12, 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: 6)
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: 18, 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: 17)
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: 9, 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: 419, 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: 13, SJR: 0.555, CiteScore: 2)
Advances in the Study of Behavior     Full-text available via subscription   (Followers: 36, SJR: 2.208, CiteScore: 4)
Advances in Veterinary Medicine     Full-text available via subscription   (Followers: 20)
Advances in Veterinary Science and Comparative Medicine     Full-text available via subscription   (Followers: 15)
Advances in Virus Research     Full-text available via subscription   (Followers: 5, SJR: 2.262, CiteScore: 5)
Advances in Water Resources     Hybrid Journal   (Followers: 53, SJR: 1.551, CiteScore: 3)
Aeolian Research     Hybrid Journal   (Followers: 6, SJR: 1.117, CiteScore: 3)
Aerospace Science and Technology     Hybrid Journal   (Followers: 375, 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: 468, 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: 44, 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: 58, 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: 2, SJR: 0.604, CiteScore: 3)
Alexandria J. of Medicine     Open Access   (Followers: 1, SJR: 0.191, CiteScore: 1)
Algal Research     Partially Free   (Followers: 11, 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: 53, SJR: 4.66, CiteScore: 10)
Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring     Open Access   (Followers: 6, SJR: 1.796, CiteScore: 4)
Alzheimer's & Dementia: Translational Research & Clinical Interventions     Open Access   (Followers: 6, SJR: 1.108, CiteScore: 3)
Ambulatory Pediatrics     Hybrid Journal   (Followers: 6)
American Heart J.     Hybrid Journal   (Followers: 58, SJR: 3.267, CiteScore: 4)
American J. of Cardiology     Hybrid Journal   (Followers: 63, SJR: 1.93, CiteScore: 3)
American J. of Emergency Medicine     Hybrid Journal   (Followers: 45, SJR: 0.604, CiteScore: 1)
American J. of Geriatric Pharmacotherapy     Full-text available via subscription   (Followers: 12)
American J. of Geriatric Psychiatry     Hybrid Journal   (Followers: 14, SJR: 1.524, CiteScore: 3)
American J. of Human Genetics     Hybrid Journal   (Followers: 35, 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: 50)
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: 241, 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: 30, SJR: 2.139, CiteScore: 4)
American J. of Preventive Medicine     Hybrid Journal   (Followers: 28, SJR: 2.164, CiteScore: 4)
American J. of Surgery     Hybrid Journal   (Followers: 39, 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: 64, SJR: 0.138, CiteScore: 0)
Anaesthesia Critical Care & Pain Medicine     Full-text available via subscription   (Followers: 23, 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: 44, SJR: 1.512, CiteScore: 5)
Analytica Chimica Acta : X     Open Access  
Analytical Biochemistry     Hybrid Journal   (Followers: 206, SJR: 0.633, CiteScore: 2)
Analytical Chemistry Research     Open Access   (Followers: 13, SJR: 0.411, CiteScore: 2)
Analytical Spectroscopy Library     Full-text available via subscription   (Followers: 14)
Anesthésie & Réanimation     Full-text available via subscription   (Followers: 2)
Anesthesiology Clinics     Full-text available via subscription   (Followers: 25, 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: 210, SJR: 1.58, CiteScore: 3)
Animal Feed Science and Technology     Hybrid Journal   (Followers: 6, SJR: 0.937, CiteScore: 2)
Animal Reproduction Science     Hybrid Journal   (Followers: 7, SJR: 0.704, CiteScore: 2)

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Similar Journals
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  [3183 journals]
  • An asymmetric and optimized encryption method to protect the
           confidentiality of 3D mesh model
    • Abstract: Publication date: October 2019Source: Advanced Engineering Informatics, Volume 42Author(s): Yaqian Liang, Fazhi He, Haoran Li 3D models are widely used in computer graphics, design and manufacture engineering, art animation and entertainment. With the universal of acquisition equipment and sensors, a huge number of 3D models are generated, which are becoming the major source of engineering data. How to preserve the privacy of the 3D models is a challenge issue. In this paper, an asymmetric and optimized encryption method is presented to protect the 3D mesh models. Firstly, we propose an asymmetric encryption method for 3D mesh models to overcome the drawbacks of traditional symmetric encryption. The primary benefit is that our approach can enhance the security of the key. Secondly, we extend the typically asymmetric encryption algorithm from integer domain to float domain. In our method, we present a normalization function to map the float DC (Discrete Cosine) coefficients to integer domain. Thirdly, considering that the shape error and encryption/decryption computation cost are contradictory in the normalization mapping, we formulate the contradiction as a multi-objective optimization problem. And then, we propose a multi-objective solution to find an optimized mapping range for encryption/decryption efficiently. Furthermore, benefiting from the proposed asymmetric encryption framework, we continue to put forward a method to check the integrity of the encrypted 3D mesh model, in which the digest is encrypted twice to generate digital signature more safely. The proposed method has been tested on 3D mesh models from Stanford university and other sources to demonstrate the effect of the proposed encryption method and optimization mechanism.
       
  • Parametric modelling and evolutionary optimization for cost-optimal and
           low-carbon design of high-rise reinforced concrete buildings
    • Abstract: Publication date: October 2019Source: Advanced Engineering Informatics, Volume 42Author(s): Vincent J.L. Gan, C.L. Wong, K.T. Tse, Jack C.P. Cheng, Irene M.C. Lo, C.M. Chan Design optimization of reinforced concrete structures helps reducing the global carbon emissions and the construction cost in buildings. Previous studies mainly targeted at the optimization of individual structural elements in low-rise buildings. High-rise reinforced concrete buildings have complicated structural designs and consume tremendous amounts of resources, but the corresponding optimization techniques were not fully explored in literature. Furthermore, the relationship between the optimization of individual structural elements and the topological arrangement of the entire structure is highly interactive, which calls for new optimization methods. Therefore, this study aims to develop a novel optimization approach for cost-optimal and low-carbon design of high-rise reinforced concrete structures, considering both the structural topology and individual element optimizations. Parametric modelling is applied to define the relationship between individual structural members and the behavior of the entire building structure. A novel evolutionary optimization technique using the genetic algorithm is proposed to optimize concrete building structures, by first establishing the optimal structural topology and then optimizing individual member sizes. In an illustrative example, a high-rise reinforced concrete building is used to examine the proposed optimization approach, which can systematically explore alternative structural designs and identify the optimal solution. It is shown that the carbon emissions and material cost are both reduced by 18–24% after performing optimization. The proposed approach can be extended to optimize other types of buildings (such as steel framework) with a similar problem nature, thereby improving the cost efficiency and environmental sustainability of the built environment.
       
  • Topological semantics for lumped parameter systems modeling
    • Abstract: Publication date: October 2019Source: Advanced Engineering Informatics, Volume 42Author(s): Randi Wang, Vadim Shapiro Behaviors of many engineering systems are described by lumped parameter models that encapsulate the spatially distributed nature of the system into networks of lumped elements; the dynamics of such a network is governed by a system of ordinary differential and algebraic equations. Languages and simulation tools for modeling such systems differ in syntax, informal semantics, and in the methods by which such systems of equations are generated and simulated, leading to numerous interoperability challenges. Logical extensions of SysML aim specifically at unifying a subset of the underlying concepts in such languages.We propose to unify semantics of all such systems using standard notions from algebraic topology. In particular, Tonti diagrams classify all physical theories in terms of physical laws (topological and constitutive) defined over a pair of dual cochain complexes and may be used to describe different types of lumped parameter systems. We show that all possible methods for generating the corresponding state equations within each physical domain correspond to paths over Tonti diagrams. We further propose a generalization of Tonti diagram that captures the behavior and supports canonical generation of state equations for multi-domain lumped parameter systems.The unified semantics provides a basis for greater interoperability in systems modeling, supporting automated translation, integration, reuse, and numerical simulation of models created in different authoring systems and applications. Notably, the proposed algebraic topological semantics is also compatible with spatially and temporally distributed models that are at the core of modern CAD and CAE systems.
       
  • An automatic literature knowledge graph and reasoning network modeling
           framework based on ontology and natural language processing
    • Abstract: Publication date: October 2019Source: Advanced Engineering Informatics, Volume 42Author(s): Hainan Chen, Xiaowei Luo With the advancement of scientific and engineering research, a huge number of academic literature are accumulated. Manually reviewing the existing literature is the main way to explore embedded knowledge, and the process is quite time-consuming and labor intensive. As the quantity of literature is increasing exponentially, it would be more difficult to cover all aspects of the literature using the traditional manual review approach. To overcome this drawback, bibliometric analysis is used to analyze the current situation and trend of a specific research field. In the bibliometric analysis, only a few key phrases (e.g., authors, publishers, journals, and citations) are usually used as the inputs for analysis. Information other than those phrases is not extracted for analysis, while that neglected information (e.g., abstract) might provide more detailed knowledge in the article. To tackle with this problem, this study proposed an automatic literature knowledge graph and reasoning network modeling framework based on ontology and Natural Language Processing (NLP), to facilitate the efficient knowledge exploration from literature abstract. In this framework, a representation ontology is proposed to characterize the literature abstract data into four knowledge elements (background, objectives, solutions, and findings), and NLP technology is used to extract the ontology instances from the abstract automatically. Based on the representation ontology, a four-space integrated knowledge graph is built using NLP technology. Then, reasoning network is generated according to the reasoning mechanism defined in the proposed ontology model. To validate the proposed framework, a case study is conducted to analyze the literature in the field of construction management. The case study proves that the proposed ontology model can be used to represent the knowledge embedded in the literatures’ abstracts, and the ontology elements can be automatically extracted by NLP models. The proposed framework can be an enhancement for the bibliometric analysis to explore more knowledge from the literature.
       
  • Intelligent collaborative patent mining using excessive topic generation
    • Abstract: Publication date: October 2019Source: Advanced Engineering Informatics, Volume 42Author(s): Usharani Hareesh Govindarajan, Amy J.C. Trappey, Charles V. Trappey An inevitable consequence of the technology-driven economy has led to the increased importance of intellectual property protection through patents. Recent global pro-patenting shifts have further resulted in high technology overlaps. Technology components are now spread across a huge corpus of patent documents making its interpretation a knowledge-intensive engineering activity. Intelligent collaborative patent mining facilitates the integration of inputs from patented technology components held by diverse stakeholders. Topic generative models are powerful natural language tools used to decompose data corpus topics and associated word bag distributions. This research develops and validates a superior text mining methodology, called Excessive Topic Generation (ETG), as a preprocessing framework for topic analysis and visualization. The presented ETG methodology adapts the topic generation characteristics from Latent Dirichlet Allocation (LDA) with added capability to generate word distance relationships among key terms. The novel ETG approach is used as the core process for intelligent collaborative patent mining. A case study of 741 global Industrial Immersive Technology (IIT) patents covering inventive and novel concepts of Virtual Reality (VR), Augmented Reality (AR), and Brain Machine Interface (BMI) are systematically processed and analyzed using the proposed methodology. Based on the discovered topics of the IIT patents, patent classification (IPC/CPC) predictions are analyzed to validate the superior ETG results.
       
  • Untangling parameters: A formalized framework for identifying overlapping
           design parameters between two disciplines for creating an
           interdisciplinary parametric model
    • Abstract: Publication date: October 2019Source: Advanced Engineering Informatics, Volume 42Author(s): Niloufar Emami Employing an interdisciplinary approach in design is an important part of the future of architecture. Therefore, taking a step toward better understanding the overlaps between disciplines, and formalizing the process of integration between disciplines accelerates progress in the field. In examining an interdisciplinary design approach using computational design and simulation tools, while considering shell structures as a special case for spanning large-span roofs, structural and daylighting discipline are considered. The aim is to understand what are the design parameters that co-exist in the structural and daylighting design disciplines, and how may these parameters be implemented in a parametric model created by designers. The parametric model that includes discipline specific parameters can later be used for interdisciplinary performance-based design. Implementing design parameters calls for an understanding of the ways in which parameters affect design and performance. This research considers the application of parametric design methods at the early stages of design for designing high-performance buildings.
       
  • Research on multi-objective decision-making under cloud platform based on
           quality function deployment and uncertain linguistic variables
    • Abstract: Publication date: October 2019Source: Advanced Engineering Informatics, Volume 42Author(s): Jiashuang Fan, Suihuai Yu, Jianjie Chu, Dengkai Chen, Mingjiu Yu, Tong Wu, Jian Chen, Fangmin Cheng, Chuan Zhao With developments in cloud computing and big data, the term “cloud” has become a household name owing to its characteristics of smartness and connectedness. Cloud-based platforms are associated with people. However, multi-objective decision-making problem in cloud platforms has not been extensively studied. There is no consensus on the reasonable and effective implementation of the decision-making model to select the optimum design scheme in a cloud platform and its establishment to objectively evaluate its significance. In this study, to efficiently and accurately realize the optimal selection of design scheme, a fuzzy evaluation mechanism in handling the vagueness and uncertainty is established, with the advantage of quality function deployment and strength Pareto evolutionary algorithm in decision analysis. A case study is provided to validate the proposed approach in cloud platforms. To reveal the advantages of the proposed method, it was compared with other methods such as the requirement-scenario-experience evaluation framework and a rough number-based integrated method. MATLAB programming was used for this comparison and accuracy certification. The result shows that the proposed model is more efficient and dynamic and provided users with multidimensional evaluation based on open and distributed resources system.
       
  • A novelty detection patent mining approach for analyzing technological
           opportunities
    • Abstract: Publication date: October 2019Source: Advanced Engineering Informatics, Volume 42Author(s): Juite Wang, Yi-Jing Chen Early opportunity identification is critical for technology-based firms seeking to develop technology or product strategies for competitive advantage in the future. This research develops a patent mining approach based on the novelty detection statistical technique to identify unusual patents that may provide a fresh idea for potential opportunities. A natural language processing technique, latent semantic analysis, is applied to extract hidden relations between words in patent documents for alleviating the vocabulary mismatch problem and reducing the cumbersome efforts of keyword selection by experts. The angle-based outlier detection method, a novelty detection statistical technique, is used to determine outlier patents that are distinct from the majority of collected patent documents in a high-dimensional data space. Finally, visualization tools are developed to analyze the identified outlier patents for exploring potential technological opportunities. The developed methodology is applied in the telehealth industry and research findings can help telehealth firms formulate their technology strategies.
       
  • Times-series data augmentation and deep learning for construction
           equipment activity recognition
    • Abstract: Publication date: October 2019Source: Advanced Engineering Informatics, Volume 42Author(s): Khandakar M. Rashid, Joseph Louis Automated, real-time, and reliable equipment activity recognition on construction sites can help to minimize idle time, improve operational efficiency, and reduce emissions. Previous efforts in activity recognition of construction equipment have explored different classification algorithms anm accelerometers and gyroscopes. These studies utilized pattern recognition approaches such as statistical models (e.g., hidden-Markov models); shallow neural networks (e.g., Artificial Neural Networks); and distance algorithms (e.g., K-nearest neighbor) to classify the time-series data collected from sensors mounted on the equipment. Such methods necessitate the segmentation of continuous operational data with fixed or dynamic windows to extract statistical features. This heuristic and manual feature extraction process is limited by human knowledge and can only extract human-specified shallow features. However, recent developments in deep neural networks, specifically recurrent neural network (RNN), presents new opportunities to classify sequential time-series data with recurrent lateral connections. RNN can automatically learn high-level representative features through the network instead of being manually designed, making it more suitable for complex activity recognition. However, the application of RNN requires a large training dataset which poses a practical challenge to obtain from real construction sites. Thus, this study presents a data-augmentation framework for generating synthetic time-series training data for an RNN-based deep learning network to accurately and reliably recognize equipment activities. The proposed methodology is validated by generating synthetic data from sample datasets, that were collected from two earthmoving operations in the real world. The synthetic data along with the collected data were used to train a long short-term memory (LSTM)-based RNN. The trained model was evaluated by comparing its performance with traditionally used classification algorithms for construction equipment activity recognition. The deep learning framework presented in this study outperformed the traditionally used machine learning classification algorithms for activity recognition regarding model accuracy and generalization.
       
  • Detection of correlation characteristics between financial time series
           based on multi-resolution analysis
    • Abstract: Publication date: October 2019Source: Advanced Engineering Informatics, Volume 42Author(s): Xiang-Xin Wang, Ling-Yu Xu, Jie Yu, Huai-Yu Xu, Xuan Yu Interactions between financial time series are complex and changeable in both time and frequency domains. To reveal the evolution characteristics of the time-varying relations between bivariate time series from a multi-resolution perspective, this study introduces an approach combining wavelet analysis and complex networks. In addition, to reduce the influence the phase lag between the time series has on the correlations, we propose dynamic time-warping (DTW) correlation coefficients to reflect the correlation degree between bivariate time series. Unlike previous studies that symbolized the time series only based on the correlation strength, the second-level symbol is set according to the correlation length during the coarse-graining process. This study presents a novel method to analyze bivariate time series and provides more information for investors and decision makers when investing in the stock market. We choose the closing prices of two stocks in China’s market as the sample and explore the evolutionary behavior of correlation modes from different resolutions. Furthermore, we perform experiments to discover the critical correlation modes between the bull market and the bear market on the high-resolution scale, the clustering effect during the financial crisis on the middle-resolution scale, and the potential pseudo period on the low-resolution scale. The experimental results exactly match reality, which provides powerful evidence to prove that our method is effective in financial time series analysis.
       
  • Coordinating patient preferences through automated negotiation: A
           multiagent systems model for diagnostic services scheduling
    • Abstract: Publication date: October 2019Source: Advanced Engineering Informatics, Volume 42Author(s): Jie Gao, Terrence Wong, Chun Wang This paper presents a multiagent systems model for patient diagnostic services scheduling. We assume a decentralized environment in which patients are modeled as self-interested agents who behave strategically to advance their own benefits rather than the system wide performance. The objective is to improve the utilization of diagnostic imaging resources by coordinating patient individual preferences through automated negotiation. The negotiation process consists of two stages, namely patient selection and preference scheduling. The contract-net protocol and simulated annealing based meta-heuristics are used to design negotiation protocols at the two stages respectively. In terms of game theoretic properties, we show that the proposed protocols are individually rational and incentive compatible. The performance of the preference scheduling protocol is evaluated by a computational study. The average percentage gap analysis of various configurations of the protocol shows that the results obtained from the protocol are close to the optimal ones. In addition, we present the algorithmic properties of the preference scheduling protocol through the validation of a set of eight hypotheses.
       
  • Deep topology network: A framework based on feedback adjustment learning
           rate for image classification
    • Abstract: Publication date: October 2019Source: Advanced Engineering Informatics, Volume 42Author(s): Long Fan, Tao Zhang, Xin Zhao, Hao Wang, Mingming Zheng Convolutional Neural Network (CNN) has demonstrated its superior ability to achieve amazing accuracy in computer vision field. However, due to the limitation of network depth and computational complexity, it is still difficult to obtain the best classification results for the specific image classification tasks. In order to improve classification performance without increasing network depth, a new Deep Topology Network (DTN) framework is proposed. The key idea of DTN is based on the iteration of multiple learning rate feedback. The framework consists of multiple sub-networks and each sub-network has its own learning rate. After the determined iteration period, these learning rates can be adjusted according to the feedback of training accuracy, in the feature learning process, the optimal learning rate is updated iteratively to optimize the loss function. In practice, the proposed DTN framework is applied to several state-of-the-art deep networks, and its performance is tested by extensive experiments and comprehensive evaluations of CIFAR-10 and MNIST benchmarks. Experimental results show that most deep networks can benefit from the DTN framework with an accuracy of 99.5% on MINIST dataset, which is 5.9% higher than that on the CIFAR-10 benchmark.
       
  • A multi-objective reliability-based decision support system for
           incorporating decision maker utilities in the design of infrastructure
    • Abstract: Publication date: October 2019Source: Advanced Engineering Informatics, Volume 42Author(s): Yasaman Shahtaheri, Madeleine M. Flint, Jesús M. de la Garza Infrastructure comprises the most fundamental facilities and systems serving society. Because infrastructure exists in economic, social, and environmental contexts, all lifecycle phases of such facilities should maximize utility for society, occupants, and designers. However, due to uncertainties associated with the nature of the built environment, the economic, social, and environmental (i.e., triple bottom line) impacts of infrastructure assets must be described as probabilistic. For this reason, optimization models should aim to maximize decision maker utilities with respect to multiple and potentially conflicting probabilistic decision criteria. Although stochastic optimization and multi-objective optimization are well developed in the field of operations research, their intersection (multi-objective optimization under uncertainty) is much less developed and computationally expensive. This article presents a computationally efficient, adaptable, multi-objective decision support system for finding optimal infrastructure design configurations with respect to multiple probabilistic decision criteria and decision maker requirements (utilities). The proposed model utilizes the First Order Reliability Method (FORM) in a systems reliability approach to assess the reliability of alternative infrastructure design configurations with regard to the probabilistic decision criteria and decision maker defined utilities, and prioritizes the decision criteria that require improvement. A pilot implementation is undertaken on a nine-story office building in Los Angeles, California to illustrate the capabilities of the framework. The results of the pilot implementation revealed that “high-performing” design configurations (with higher initial costs and lower failure costs) had a higher probability of meeting the decision maker’s preferences than more traditional, low initial cost configurations. The proposed framework can identify low-impact designs that also maximize decision maker utilities.
       
  • Method for digital evaluation of existing production systems adequacy to
           changes in product engineering in the context of the automotive industry
    • Abstract: Publication date: October 2019Source: Advanced Engineering Informatics, Volume 42Author(s): Jaqueline Sebastiany Iaksch, Milton Borsato Current industry practices during the Product Development Process (PDP) still points to the isolation of knowledge domains even with the increase of digitalization. Considering manufacturing process constrains from the beginning of the PDP avoids problems in later stages during the whole product life cycle. Through the application of concepts of the Digital Thread approach, the opportunity to intelligently integrate knowledge into product development is presented, creating a “digital fabric” capable of directing and supporting all stages of the product life cycle. Through these concepts, this research proposes the elaboration of an ontological model and application method capable of evaluating, in real time, the adequacy of the existing production systems, integrating the project and process information. The methodological framework used for the development of this method was Design Science Research. In this way, six steps were performed: (i) problem identification and motivation; (ii) definition of the objectives of the solution; (iii) artifact design and development; (iv) demonstration; (v) evaluation; and, (vi) results report. Through the description of manufacturing systems, the solution contributes to the digital evaluation and facilitates the decision making regarding productive systems, as well as data recovery, reutilization and management. In order to do an initial framework validation, it was performed the application of adequacy principles of a specific production line in automotive sector. However, this choice of a complex industry sector translates a clear possibility of framework adaptation to another industrial segment.
       
  • A hypernetwork-based approach to collaborative retrieval and reasoning of
           engineering design knowledge
    • Abstract: Publication date: October 2019Source: Advanced Engineering Informatics, Volume 42Author(s): Gongzhuang Peng, Hongwei Wang, Heming Zhang, Keke Huang Complex product development increasingly entails creation and sharing of design knowledge in a collaborative and integrated working environment. In this context, it has become a central issue to address the multifaceted feature of design knowledge for such a collaborative knowledge sharing scheme. This paper proposes a hypernetwork-based approach to explicitly capturing the relationships between various elements in a multifaceted knowledge representation. Specifically, a knowledge hypernetwork model is constructed, which is composed of a designer network, a product network, an issue network and a knowledge unit network. The relationships between various nodes from different networks are identified and defined according to specific node properties. In addition, topological characteristics of the hypernetwork structure are analyzed together with the statistical indicators. Based on this model, the Bayesian approach is adopted to conduct the collaborative reasoning process whereby knowledge elements relevant to the current design task are recommended according to the issues to be resolved and the current design context. A case study conducted in this work shows that the proposed approach is effective in capturing the complex relationships between multi-faceted knowledge elements and enables collaborative retrieval and reasoning of knowledge records.
       
  • Hybrid data-driven vigilance model in traffic control center using
           eye-tracking data and context data
    • Abstract: Publication date: October 2019Source: Advanced Engineering Informatics, Volume 42Author(s): Fan Li, Ching-Hung Lee, Chun-Hsien Chen, Li Pheng Khoo Vigilance decrement of traffic controllers would greatly threaten public safety. Hence, extensive studies have been conducted to establish the physiological data-based vigilance model for objectively monitoring or detecting vigilance decrement. Nevertheless, most of them using intrusive devices to collect physiological data and failed to consider context information. Consequently, these models can be used in a laboratory environment while cannot adapt to dynamic working conditions of traffic controllers. The goal of this research is to develop an adaptive vigilance model for monitoring vigilance objectively and non-intrusively. In recent years, with advanced information and communication technology, a massive amount of data can be collected from connected daily use items. Hence, we proposed a hybrid data-driven approach based on connected objects for establishing vigilance model in the traffic control center and provide an elaborated case study to illustrate the method. Specifically, eye movements are selected as the primary inputs of the proposed vigilance model; Bagged trees technique is adapted to generate the vigilance model. The results of case study indicated that (1) eye metrics would be correlated with the vigilance performance subjected to the mental fatigue levels, (2) the bagged trees with the fusion features as inputs achieved a relatively stable performance under the condition of data loss, (3) the proposed method could achieve better performance than the other classic machine learning methods.
       
  • Introducing article numbering to Advanced Engineering Informatics
    • Abstract: Publication date: August 2019Source: Advanced Engineering Informatics, Volume 41Author(s):
       
  • Developing a conceptual framework of smart work packaging for constraints
           management in prefabrication housing production
    • Abstract: Publication date: October 2019Source: Advanced Engineering Informatics, Volume 42Author(s): Xiao Li, Geoffrey Qiping Shen, Peng Wu, Fan Xue, Hung-lin Chi, Clyde Zhengdao Li Constraints management is the process of satisfying bottlenecks to facilitate tasks assigned to crews being successfully executed. However, managing constraints is inherently challenging in prefabrication housing production (PHP), due to the fragmentation of processes and information during project delivery. Enlightened by the broadly accepted work packaging method and the smart construction objects (SCOs) model, this study aims to define and implement smart work packaging (SWP) for constraints management in PHP. Firstly, the framework of SWP-enabled constraints management (SWP-CM) with three primary functions, including constraints modeling, constraints optimization, and constraints monitoring, is established. In addition, this study develops a layered abstract model as a prototype representation to elaborate on the implementation of SWP for practitioners. Finally, a laboratory-based test is applied to validate the framework. It can prove that SWP indeed opens new avenues for smart constraints management for PHP.
       
  • Road pothole extraction and safety evaluation by integration of point
           cloud and images derived from mobile mapping sensors
    • Abstract: Publication date: October 2019Source: Advanced Engineering Informatics, Volume 42Author(s): Hangbin Wu, Lianbi Yao, Zeran Xu, Yayun Li, Xinran Ao, Qichao Chen, Zhengning Li, Bin Meng The automatic detection and extraction of road pothole distress is an important issue regarding healthy road structures, monitoring, and maintenance. In this paper, a new algorithm that integrates the mobile point cloud and images is proposed for the detection of road potholes. The algorithm includes three steps: 2D candidate pothole extraction from the images using a deep learning method, 3D candidate pothole extraction via a point cloud, and pothole determination by depth analysis. Because the texture features of the pothole and asphalt or concrete patches greatly differ from those of a normal road, pothole or patch distress images are used to establish a training set and train and test the deep learning system. Subsequently, the 2D candidate pothole is extracted from the images and labeled via the trained DeepLabv3+, a state-of-the-art pixel-wise classification (semantic segmentation) network. The edge of the candidate pothole in the image is then used to establish the relationship between the mobile point cloud and images. The original road point cloud around the edge of the candidate pothole is categorized into two groups, that is, interior and exterior points, according to the relationship between the point cloud and images. The exterior points are used to fit the road plane and calculate the accurate 3D shape of the candidate potholes. Finally, the interior points of a candidate pothole are used to analyze the depth distribution to determine if the candidate pothole is a pothole or patch. To verify the proposed method, two cases, including real and simulation cases, are selected. The real case is an expressway in Shanghai with a length of 26.4 km. Based on the proposed method, 77 candidate potholes are extracted by the DeepLabv3+ system; 49 potholes and 28 patches are finally filtered. The affected lanes and pothole locations are analyzed. The simulation case is selected to verify the geometric accuracy of the detected potholes. The results show that the mean accuracy of the detected potholes is ∼1.5–2.8 cm.
       
  • Registering georeferenced photos to a building information model to
           extract structures of interest
    • Abstract: Publication date: October 2019Source: Advanced Engineering Informatics, Volume 42Author(s): Junjie Chen, Donghai Liu, Shuai Li, Da Hu Vision-based techniques are being used to inspect structures such as buildings and infrastructure. Due to various backgrounds in the acquired images, conventional vision-based techniques rely heavily on manual processing to extract relevant structures of interest for subsequent analysis in many applications, such as distress detection. This practice is laborious, time-consuming, and error-prone. To address the challenge, this study proposes a new method that automatically matches a georeferenced real-life photo with a building information model-rendered synthetic image to allow the extraction of relevant structure of interest. Field experiments were conducted to validate and evaluate the proposed method. The average accuracy of this method is 79.21% and the processing speed is 140 s per image. The proposed method has the potential to reduce the workload of image processing for vision-based structural inspection.
       
  • One class based feature learning approach for defect detection using deep
           autoencoders
    • Abstract: Publication date: October 2019Source: Advanced Engineering Informatics, Volume 42Author(s): Abdul Mujeeb, Wenting Dai, Marius Erdt, Alexei Sourin Detecting defects is an integral part of any manufacturing process. Most works still utilize traditional image processing algorithms to detect defects owing to the complexity and variety of products and manufacturing environments. In this paper, we propose an approach based on deep learning which uses autoencoders for extraction of discriminative features. It can detect different defects without using any defect samples during training. This method, where samples of only one class (i.e. defect-free samples) are available for training, is called One Class Classification (OCC). This OCC method can also be used for training a neural network when only one golden sample is available by generating many copies of the reference image by data augmentation. The trained model is then able to generate a descriptor—a unique feature vector of an input image. A test image captured by an Automatic Optical Inspection (AOI) camera is sent to the trained model to generate a test descriptor, which is compared with a reference descriptor to obtain a similarity score. After comparing the results of this method with a popular traditional similarity matching method SIFT, we find that in the most cases this approach is more effective and more flexible than the traditional image processing-based methods, and it can be used to detect different types of defects with minimum customization.
       
  • A CNN-based 3D patch registration approach for integrating sequential
           models in support of progress monitoring
    • Abstract: Publication date: August 2019Source: Advanced Engineering Informatics, Volume 41Author(s): Lei Lei, Ying Zhou, Hanbin Luo, Peter E.D. Love Significant advancements in three-dimensional (3D) imaging technologies have enabled the ability to effectively monitor and manage the progress of works in construction. Traditionally, 3D point clouds have been used in conjunction with building information models to visualize the progress of works. The discrepancies between ‘as-planned’ and ‘actual’ models are unable to be automatically identified using the existing approaches due the absence of an effective registration algorithm. To ensure the registration accuracy of multi-scanned point clouds, an automated method based on a data-driven Convolutional Neural Network (CNN) deep learning algorithm is proposed. In this instance, 3D Point cloud patches are aligned with spatial datasets that are scanned from different locations using range cameras. The registration results are used to automatically detect spatial changes when compared with different point clouds. The quantified changes are utilized to determine the percentage of work that has been completed at fixed intervals. The developed registration approach is tested and validated using a series of experiments. It is demonstrated that discrepancies between ‘as-planned’ and ‘actual’ models can be identified with a higher level of accuracy, which can enable the baseline for monitoring construction to be undertaken in real-time.
       
  • A lattice-based approach for navigating design configuration spaces
    • Abstract: Publication date: October 2019Source: Advanced Engineering Informatics, Volume 42Author(s): Alison McKay, Hau Hing Chau, Christopher F. Earl, Amar Kumar Behera, Alan de Pennington, David C. Hogg Design configurations, such as Bills of Materials (BoMs), are indispensable parts of any product development process and integral to the design descriptions stored in proprietary Computer Aided Design and Product Lifecycle Management systems. Engineers use BoMs and other design configurations as lenses to repurpose design descriptions for specific purposes. For this reason, multiple BoMs typically occur in any given product development process. For example, an engineering BoM may be used to define a configuration that best supports a design activity whereas a manufacturing BoM may be used to define the configuration of parts that best supports a manufacturing process. Current practice for the definition of BoMs involves the use of indented parts lists and dendograms that are prone to error because it is easy to create discrepancies across BoMs that, in essence, are defined through collections of part identifiers such as names and part numbers. Such errors have a significant detrimental effect on the performance of product development processes by creating the need for rework, adding costs and increasing time to market.This paper introduces a design description capability that ensures consistency across BoMs for a given design. A boolean hypercube lattice is used to define a design configuration space that includes all possible configurations for a given design description. Valid operations within the space are governed by the mathematics of hypercube lattices. The design description capability is demonstrated through an early engineering design configuration software tool that offers significant benefits by ensuring consistency across the BoMs for a given design. The software uses and generates design descriptions that are exported from and imported to commercially available design systems through a standard (ISO 10303-214) interface format. In this way, potential for early impact on industry practice is high.
       
  • A novel inverse data driven modelling approach to performance-based
           building design during early stages
    • Abstract: Publication date: August 2019Source: Advanced Engineering Informatics, Volume 41Author(s): Roya Rezaee, Jason Brown, John Haymaker, Godfried Augenbroe Energy analysis at the early stage of building design is a critical, yet difficult task in performance-based design. The difficulty arises from the complex, iterative, and uncertain nature of building design and the challenges of integration with well-posed energy assessment tools. The purpose of this article is to first review characteristics of performance-based design and establish requirements for a methodology that includes generating promising design alternatives, assessing the energy performance in tandem with the generation of alternatives, and choosing an alternative design solution with confidence. The study then proposes a novel systematic data-driven method, based on linear inverse modeling that generates plausible ranges for design parameters given a preferred energy target. The energy performance in this method is described as a linear function of the design parameters for a particular scenario of design. The application of the proposed method in a case study shows that it is capable of helping designers make informed decisions regarding energy performance iteratively and confidently at the early stages of building design.
       
  • Improving reconstruction of tunnel lining defects from ground-penetrating
           radar profiles by multi-scale inversion and bi-parametric full-waveform
           inversion
    • Abstract: Publication date: August 2019Source: Advanced Engineering Informatics, Volume 41Author(s): Deshan Feng, Xun Wang, Bin Zhang Complex irregular defects of tunnel linings under complicated geological conditions cannot be accurately reconstructed with the traditional full-waveform inversion (FWI) method due to their irregular geometrical characteristics and complex dielectric properties. Because of extensive inversion calculations and high memory requirements, the traditional FWI method is very sensitive to the initial model and plunge into a local minimum or cycle skipping. To solve this problem, a novel ground-penetrating radar (GPR) FWI method involving two parameters (i.e. permittivity and conductivity) is proposed for improving reconstruction accuracy of lining defects using the total variation (TV) regularization. First, the Delaunay unstructured triangular mesh in finite element time-domain (FETD) method is employed to perform GPR forward modeling, and then the total-variation model constraint and multi-scale inversion strategy are implemented during execution of the conjugate gradient (CG) algorithm, which facilitates the quick search for the global optimal minimum value, thus guaranteeing the avoidance of the ill-posed problem during the inversion process. Accordingly, the detailed features of lining defects can be characterized and reconstructed even for those complicated geological conditions, more specifically, the results show that, with fewer iterations (up to 59 times less), the proposed method present a lower reconstruction error both on permittivity (up to 12.05% lower) and conductivity (up to 7.35% lower). From a comparison of the inversion results and the model, it can be concluded that the proposed FWI algorithm can effectively eliminate the non-physical oscillation and artifacts in the image reconstruction, which may significantly improving the accuracy of defects interpretation and assessing the severity of complex defects.
       
  • A metamodel for cyber-physical systems
    • Abstract: Publication date: August 2019Source: Advanced Engineering Informatics, Volume 41Author(s): Theresa Fitz, Michael Theiler, Kay Smarsly With the advent of the Internet of Things and Industry 4.0 concepts, cyber-physical systems in civil engineering experience an increasing impact on structural health monitoring (SHM) and control applications. Designing, optimizing, and documenting cyber-physical system on a formal basis require platform-independent and technology-independent metamodels. This study, with emphasis on communication in cyber-physical systems, presents a metamodel for describing cyber-physical systems. First, metamodeling concepts commonly used in computing in civil engineering are reviewed and possibilities and limitations of describing communication-related information are discussed. Next, communication-related properties and behavior of distributed cyber-physical systems applied for SHM and control are explained, and system components relevant to communication are specified. Then, the metamodel to formally describe cyber-physical systems is proposed and mapped into the Industry Foundation Classes (IFC), an open international standard for building information modeling (BIM). Finally, the IFC-based approach is verified using software of the official IFC certification program, and it is validated by BIM-based example modeling of a prototype cyber-physical system, which is physically implemented in the laboratory. As a result, cyber-physical systems applied for SHM and control are described and the information is stored, documented, and exchanged on the formal basis of IFC, facilitating design, optimization, and documentation of cyber-physical systems.
       
  • A shared ontology for integrated highway planning
    • Abstract: Publication date: August 2019Source: Advanced Engineering Informatics, Volume 41Author(s): Jojo France-Mensah, William J. O'Brien Many highway agencies have several functional groups responsible for planning for safety, maintenance and rehabilitation (M&R), mobility, and other functions. The functional nature of State Highway Agencies (SHAs) can result in a siloed approach to planning. Such efforts are further challenged by functional groups utilizing legacy systems which lack interoperability. In practice, this leads to redundant planning efforts and potential spatial-temporal conflicts in the projects proposed by the different groups over a planning period. There is a need for an integrated approach to planning supported by information systems. However, the existing literature on formalized knowledge representation fails to adequately account for the level of information needed for cross-functional planning of projects scheduled for the same network. Hence, this study presents an ontology for integrating information to support the cross-functional and spatial-temporal planning of highway projects. The Integrated Highway Planning Ontology (IHP-Onto) is a shared representation of knowledge about pavement assets, M&R planning, and inter-project coordination. Sources of the knowledge acquired included expert interviews, a review of nation-wide studies, and previously published ontologies. The implementation phase included a case study demonstration of the ontology by answering relevant competency questions via SPARQL queries. Based on the data-driven evaluation of the ontology, the precision and recall rates obtained were 97% and 92% respectively. Based on the results of the evaluation approaches, IHP-Onto was demonstrated as being sufficient to represent domain knowledge capable of supporting integrated highway planning.
       
  • Development of an IoT-based big data platform for day-ahead prediction of
           building heating and cooling demands
    • Abstract: Publication date: August 2019Source: Advanced Engineering Informatics, Volume 41Author(s): X.J. Luo, Lukumon O. Oyedele, Anuoluwapo O. Ajayi, Chukwuka G. Monyei, Olugbenga O. Akinade, Lukman A. Akanbi The emerging technologies of the Internet of Things (IoT) and big data can be utilised to derive knowledge and support applications for energy-efficient buildings. Effective prediction of heating and cooling demands is fundamental in building energy management. In this study, a 4-layer IoT-based big data platform is developed for day-ahead prediction of building energy demands, while the core part is the hybrid machine learning-based predictive model. The proposed energy demand predictive model is based on the hybrids of k-means clustering and artificial neural network (ANN). Due to different temperatures of walls, windows, grounds, roofs and indoor air, various IoT sensors are installed at different locations of the building. To determine the input variables to the hybrid machine learning-based predictive model, correlation analysis is adopted. Through clustering analysis, the characteristic patterns of daily weather profile are identified. Thus, the annual profile is classified into several featuring groups. Each group of weather profile, along with IoT sensor readings, building operating schedules as well as heating and cooling demands, is used to train the sub-ANN predictive models. Due to the involvement of IoT sensors, the overall prediction accuracy can be improved. It is found that the mean absolute percentage error of energy demands prediction is 3% and 8% in training and testing cases, respectively.Graphical abstractGraphical abstract for this article
       
  • Fostering the transfer of empirical engineering knowledge under
           technological paradigm shift: An experimental study in conceptual design
    • Abstract: Publication date: August 2019Source: Advanced Engineering Informatics, Volume 41Author(s): Xinyu Li, Zuhua Jiang, Yeqin Guan, Geng Li, Fuhua Wang Knowledge transfer is a frequently-used method for novice engineers to rapidly adapt themselves to a related new technological paradigm. However, due to different types in knowledge transfer, it remains divergent about the mechanism and motivation, leading to the chaos in tackling the low-quality and low-efficiency transfer process. This study concentrated on the individual-level transfer of empirical engineering knowledge (EEK) in product designers’ mind triggered by technological paradigm shift, trying to accelerate the process and improve the result. Based on concept-knowledge model and eye-tracking data, this paper proposed a two-phase protocol and implements it in a practical case of conceptual design. Three factors of designers, prior cognitive level, utilization of the stimulus materials, and self-directed learning willingness, were evaluated and investigated. Results of 82 engineering students and young scholars showed that all three factors significantly impacted the validity and integrity of transferred EEK, and the efficiency of EEK transfer process. Specifically, key concepts in the stimulus materials, and creativity and initiative in learning willingness improved the validity and integrity. Non-essential words and too complex drawings lowered the efficiency. High cognitive level had side-effects on the transfer. These results inferred that the transfer of EEK could be fostered by wisely reusing past experience, selectively utilizing stimulus materials, and actively promoting learning willingness, which helps the designers to perform better in product design innovation.
       
  • An integrated highly synchronous, high resolution, real time eye tracking
           system for dynamic flight movement
    • Abstract: Publication date: August 2019Source: Advanced Engineering Informatics, Volume 41Author(s): Hong Jie Wee, Sun Woh Lye, Jean-Philippe Pinheiro Electronic surveillance systems are being used rapidly today, ranging from a simple video camera to a complex biometric surveillance system for facial patterns and intelligent computer vision based surveillance systems, which are applied in many fields such as home monitoring, security surveillance of important places and mission critical tasks like air traffic control surveillance. Such systems normally involve a computer system and a human surveillance operator, who looks at the dynamic display to perform his surveillance tasks. Exploitation of shared information between these physical heterogeneous data capture systems with human operated functions is one emerging aspect in electronic surveillance that has yet to be addressed deeply. Hence, an innovative interaction interface for such knowledge extraction and representation is required. Such an interface should establish a data activity register frame which captures information depicting various surveillance activities at a specified spatial and time reference.This paper presents a real time eye tracking system, which integrates two sets of activity data in a highly dynamic changing and synchronous manner in real-time with respect to both spatial and time frames, through the “Dynamic Data Alignment and Timestamp Synchronisation Model”. This model matches the timestamps of the two data streams, aligns them to the same spatial reference frame before fusing them together into a data activity register frame. The Air Traffic Control (ATC) domain is used to illustrate this model, where experiments are conducted under simulated radar traffic situations with participants and their radar input data. Test results revealed that this model is able to synchronise the timestamp of the eye and dynamic display data, align both of these data spatially, while taking into account dynamic changes in space and time on a simulated radar display. This system can also distinguish and show variations in the monitoring behaviour of participants. As such, new knowledge can be extracted and represented through this innovative interface, which can then be applied to other applications in the field of electronic surveillance to unearth monitoring behaviour of the human surveillance operator.
       
  • Inferring workplace safety hazards from the spatial patterns of
           workers’ wearable data
    • Abstract: Publication date: August 2019Source: Advanced Engineering Informatics, Volume 41Author(s): Kanghyeok Yang, Changbum R. Ahn Hazard identification in construction typically requires safety managers to manually inspect an area. However, current approach is very limited due to the dynamic nature of construction sites and the subjective nature of human perception. Using wearable inertial measurement units (WIMU), previous literatures revealed the relationship between a worker’s abnormal gait patterns and the existence of slip, trip and fall (STF) hazards. Though the prior work demonstrated the strong correlation between STF hazards and abnormal gait patterns, automated hazard identification is a challenging issue due to the lack of knowledge on decision threshold on identifying hazards under different construction environments. To fill the research gap, this study developed an approach that can automatically identify the STF hazards without knowledge about thresholds by investigating the spatial associations of workers’ abnormal gait occurrences. An experiment simulating a brick installation was performed with different types of STF hazards (e.g., poor housekeeping), and results demonstrate the feasibility of STF hazards identification with the developed approach. The results highlight the opportunities of revealing potential accident hotspots via an efficient and semi-automated methodology, which overcomes many of the limitations in current practice.
       
  • Corrigendum to “A structural service innovation approach for designing
           smart product service systems: Case study of smart beauty service” [Adv.
           Eng. Inform. 40 (2019) 154–167]
    • Abstract: Publication date: August 2019Source: Advanced Engineering Informatics, Volume 41Author(s): Ching-Hung Lee, Chun-Hsien Chen, Amy J.C. Trappey
       
  • Xgboost application on bridge management systems for proactive damage
           estimation
    • Abstract: Publication date: August 2019Source: Advanced Engineering Informatics, Volume 41Author(s): Soram Lim, Seokho Chi Bridge inspection is one of the most fundamental tasks in bridge management practices. Because of limited professional manpower and budget constraints, providing prior information about possible damage can reduce inspection errors and time. The purpose of this study was to estimate the condition of bridges at a damage level, considering various influencing factors for seven different damage types by six different main structure types, using data from the Korean Bridge Management System. The extreme gradient boosting (XGBoost) method was used because it has the advantage of not assuming determinacy and independence, and it clearly can handle the numerous variables that affect damage to bridges. As a result, out of the 38 decision trees that were generated, 36 trees were derived with significant performance measures. The influence of the variables was calculated by the Shapley Additive Explanation (SHAP) value. Age, average daily truck traffic, vehicle weight limit, total length, and effective width were found to be the major factors that influenced damage to bridges. This study confirmed that more detailed structural factors were significant contributors to severe damage to complex structural designs and the use of multiple kinds of materials, such as the cross-sectional properties of girders for the concrete deck of bridges with steel girders compared to the properties of the decks for bridges made of a simple slab of reinforced concrete. The research findings emphasized the benefits of artificial intelligence in the analysis of the conditions of bridges and showed its potential for use in network-level decision making for preventive maintenance.
       
  • A directed failure causality network (DFCN) based method for function
           components risk prioritization under interval type-2 fuzzy environment
    • Abstract: Publication date: August 2019Source: Advanced Engineering Informatics, Volume 41Author(s): Hongzhan Ma, Xuening Chu, Weizhong Wang, Xinwang Liu, Deyi Xue Failure risk prioritization of function components plays a key role in the process to redesign a mechanical product. However, the failure causality relationships (FCRs) among failure modes of components are often ignored in the existing design risk assessment methods, leading to inaccurate risk prioritization results. A failure mode in one component can be the cause of a failure mode in another component, and a failure mode with low chance of failure may result in another failure mode with high chance of failure through propagation among failure modes. Thus, the ultimate effects of each failure mode should be determined by considering the effects of failure propagations. In this research, a directed failure causality network (DFCN) model considering FCRs is proposed to describe the FCRs and to predict risks of the designed product. In addition, uncertainties of linguistic terms in evaluation are also considered in the developed model, because linguistic terms are more suitable and natural than quantitative numbers for design engineers to assess design risks based on their knowledge. To describe these uncertainties, interval type-2 fuzzy set is employed to model the designers’ subjective linguistic terms for determining the weights of edges and weights of vertices in the DFCN. A case study for failure risk prioritization of components in redesign of a large tonnage crawler crane (LTCC) is implemented to demonstrate the effectiveness of the proposed method.
       
  • A novel approach for sand liquefaction prediction via local mean-based
           pseudo nearest neighbor algorithm and its engineering application
    • Abstract: Publication date: August 2019Source: Advanced Engineering Informatics, Volume 41Author(s): Shuai Huang, Mingming Huang, Yuejun Lyu The prediction method plays crucial roles in accurate prediction of sand liquefaction. Recently, machine learning has been widely used for prediction of sand liquefaction, and the Local Mean-based Pseudo Nearest Neighbor (LMPNN) algorithm, one of machine learning techniques, showed good performance in pattern recognition. In this study, we propose a sand liquefaction prediction model based on the LMPNN algorithm, which is the first work of applying the LMPNN algorithm to sand liquefaction prediction. Then, our proposed prediction model is used for evaluation of site liquefaction grade in Tongzhou District of China. And the comparison between our proposed prediction model with the liquefaction evaluation method in the Chinese code is made, which will provide an important approach to predicting the sand liquefaction grades for the major construction project sites. Extensive experiments on grade prediction demonstrate that the effectiveness of our proposed prediction model based on the LMPNN algorithm. In addition, shaking table test of an engineering site model is conducted for evaluating whether this engineering site model is liquefaction and non-liquefaction or not. And the experiment result of the shaking table test is the same as that of our proposed prediction model based on LMPNN algorithm, which further demonstrates the effectiveness of our proposed prediction model. Consequently, our proposed prediction model is proved to have a good prospect of engineering application in the liquefaction prediction.
       
  • Automated classification of building information modeling (BIM) case
           studies by BIM use based on natural language processing (NLP) and
           unsupervised learning
    • Abstract: Publication date: Available online 30 April 2019Source: Advanced Engineering InformaticsAuthor(s): Namcheol Jung, Ghang Lee This paper comparatively analyzes a method to automatically classify case studies of building information modeling (BIM) in construction projects by BIM use. It generally takes a minimum of thirty minutes to hours of collection and review and an average of four information sources to identify a project that has used BIM in a manner that is of interest. To automate and expedite the analysis tasks, this study deployed natural language processing (NLP) and commonly used unsupervised learning for text classification, namely latent semantic analysis (LSA) and latent Dirichlet allocation (LDA). The results were validated against one of representative supervised learning methods for text classification—support vector machine (SVM). When LSA and LDA detected phrases in a BIM case study that had higher similarity values to the definition of each BIM use than the threshold values, the system determined that the project had deployed BIM in the detected approach. For the classification of BIM use, the BIM uses specified by Pennsylvania State University were utilized. The approach was validated using 240 BIM case studies (512,892 features). When BIM uses were employed in a project, the project was labeled as “1”; when they were not, the project was labeled as “0.” The performance was analyzed by changing parameters: namely, document segmentation, feature weighting, dimensionality reduction coefficient (k-value), the number of topics, and the number of iterations. LDA yielded the highest F1 score, 80.75% on average. LDA and LSA yielded high recall and low precision in most cases. Conversely, SVM yielded high precision and low recall in most cases and fluctuations in F1 scores.
       
 
 
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