Publisher: Elsevier   (Total: 3161 journals)

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Showing 1 - 200 of 3161 Journals sorted alphabetically
Academic Pediatrics     Hybrid Journal   (Followers: 39, SJR: 1.655, CiteScore: 2)
Academic Radiology     Hybrid Journal   (Followers: 26, SJR: 1.015, CiteScore: 2)
Accident Analysis & Prevention     Partially Free   (Followers: 106, SJR: 1.462, CiteScore: 3)
Accounting Forum     Hybrid Journal   (Followers: 28, SJR: 0.932, CiteScore: 2)
Accounting, Organizations and Society     Hybrid Journal   (Followers: 44, SJR: 1.771, CiteScore: 3)
Achievements in the Life Sciences     Open Access   (Followers: 7)
Acta Anaesthesiologica Taiwanica     Open Access   (Followers: 6)
Acta Astronautica     Hybrid Journal   (Followers: 448, SJR: 0.758, CiteScore: 2)
Acta Automatica Sinica     Full-text available via subscription   (Followers: 2)
Acta Biomaterialia     Hybrid Journal   (Followers: 30, SJR: 1.967, CiteScore: 7)
Acta Colombiana de Cuidado Intensivo     Full-text available via subscription   (Followers: 3)
Acta de Investigación Psicológica     Open Access   (Followers: 2)
Acta Ecologica Sinica     Open Access   (Followers: 11, SJR: 0.18, CiteScore: 1)
Acta Histochemica     Hybrid Journal   (Followers: 5, SJR: 0.661, CiteScore: 2)
Acta Materialia     Hybrid Journal   (Followers: 326, 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: 2, SJR: 1.793, CiteScore: 6)
Acta Psychologica     Hybrid Journal   (Followers: 26, 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   (Followers: 1)
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: 18, SJR: 1.29, CiteScore: 3)
Addictive Behaviors Reports     Open Access   (Followers: 9, SJR: 0.755, CiteScore: 2)
Additive Manufacturing     Hybrid Journal   (Followers: 13, SJR: 2.611, CiteScore: 8)
Additives for Polymers     Full-text available via subscription   (Followers: 22)
Advanced Drug Delivery Reviews     Hybrid Journal   (Followers: 189, SJR: 4.09, CiteScore: 13)
Advanced Engineering Informatics     Hybrid Journal   (Followers: 13, 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: 17, SJR: 2.384, CiteScore: 5)
Advances in Anesthesia     Full-text available via subscription   (Followers: 30, 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: 12, SJR: 0.992, CiteScore: 1)
Advances in Applied Mechanics     Full-text available via subscription   (Followers: 12, 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: 1, SJR: 0.686, CiteScore: 2)
Advances in Cancer Research     Full-text available via subscription   (Followers: 35, 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: 29, SJR: 0.156, CiteScore: 1)
Advances in Child Development and Behavior     Full-text available via subscription   (Followers: 11, SJR: 0.713, CiteScore: 1)
Advances in Chronic Kidney Disease     Full-text available via subscription   (Followers: 11, 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: 21, 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: 16)
Advances in Developmental Biology     Full-text available via subscription   (Followers: 14)
Advances in Digestive Medicine     Open Access   (Followers: 13)
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: 45, SJR: 2.524, CiteScore: 4)
Advances in Engineering Software     Hybrid Journal   (Followers: 30, SJR: 1.159, CiteScore: 4)
Advances in Experimental Biology     Full-text available via subscription   (Followers: 9)
Advances in Experimental Social Psychology     Full-text available via subscription   (Followers: 52, SJR: 5.39, CiteScore: 8)
Advances in Exploration Geophysics     Full-text available via subscription   (Followers: 2)
Advances in Fluorine Science     Full-text available via subscription   (Followers: 9)
Advances in Food and Nutrition Research     Full-text available via subscription   (Followers: 68, 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: 21, SJR: 1.354, CiteScore: 4)
Advances in Genome Biology     Full-text available via subscription   (Followers: 12, SJR: 12.74, CiteScore: 13)
Advances in Geophysics     Full-text available via subscription   (Followers: 8, SJR: 1.193, CiteScore: 3)
Advances in Heat Transfer     Full-text available via subscription   (Followers: 26, 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: 26)
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: 37, 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: 9, 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: 21, SJR: 0.88, CiteScore: 2)
Advances in Mathematics     Full-text available via subscription   (Followers: 17, SJR: 3.027, CiteScore: 2)
Advances in Medical Sciences     Hybrid Journal   (Followers: 9, 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: 5, SJR: 1.158, CiteScore: 3)
Advances in Molecular and Cell Biology     Full-text available via subscription   (Followers: 26)
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: 5)
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: 6, SJR: 1.579, CiteScore: 4)
Advances in Pediatrics     Full-text available via subscription   (Followers: 27, SJR: 0.461, CiteScore: 1)
Advances in Pharmaceutical Sciences     Full-text available via subscription   (Followers: 19)
Advances in Pharmacology     Full-text available via subscription   (Followers: 17, SJR: 1.536, CiteScore: 3)
Advances in Physical Organic Chemistry     Full-text available via subscription   (Followers: 10, 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: 11)
Advances in Plant Pathology     Full-text available via subscription   (Followers: 6)
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: 69)
Advances in Quantum Chemistry     Full-text available via subscription   (Followers: 7, SJR: 0.371, CiteScore: 1)
Advances in Radiation Oncology     Open Access   (Followers: 3, 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: 7)
Advances in Space Research     Full-text available via subscription   (Followers: 431, SJR: 0.569, CiteScore: 2)
Advances in Structural Biology     Full-text available via subscription   (Followers: 6)
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: 37, 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: 6, SJR: 2.262, CiteScore: 5)
Advances in Water Resources     Hybrid Journal   (Followers: 57, SJR: 1.551, CiteScore: 3)
Aeolian Research     Hybrid Journal   (Followers: 6, SJR: 1.117, CiteScore: 3)
Aerospace Science and Technology     Hybrid Journal   (Followers: 398, 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: 12, SJR: 3.671, CiteScore: 9)
Aggression and Violent Behavior     Hybrid Journal   (Followers: 489, SJR: 1.238, CiteScore: 3)
Agri Gene     Hybrid Journal   (Followers: 1, SJR: 0.13, CiteScore: 0)
Agricultural and Forest Meteorology     Hybrid Journal   (Followers: 18, SJR: 1.818, CiteScore: 5)
Agricultural Systems     Hybrid Journal   (Followers: 32, SJR: 1.156, CiteScore: 4)
Agricultural Water Management     Hybrid Journal   (Followers: 47, 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: 8, 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: 12)
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: 11, SJR: 0.201, CiteScore: 1)
Alzheimer's & Dementia     Hybrid Journal   (Followers: 55, 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: 5)
American Heart J.     Hybrid Journal   (Followers: 58, SJR: 3.267, CiteScore: 4)
American J. of Cardiology     Hybrid Journal   (Followers: 67, SJR: 1.93, CiteScore: 3)
American J. of Emergency Medicine     Hybrid Journal   (Followers: 48, SJR: 0.604, CiteScore: 1)
American J. of Geriatric Pharmacotherapy     Full-text available via subscription   (Followers: 13)
American J. of Geriatric Psychiatry     Hybrid Journal   (Followers: 15, SJR: 1.524, CiteScore: 3)
American J. of Human Genetics     Hybrid Journal   (Followers: 39, 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: 37, 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: 266, SJR: 2.7, CiteScore: 4)
American J. of Ophthalmology     Hybrid Journal   (Followers: 67, 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: 32, SJR: 2.139, CiteScore: 4)
American J. of Preventive Medicine     Hybrid Journal   (Followers: 30, 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: 67, SJR: 0.138, CiteScore: 0)
Anaesthesia Critical Care & Pain Medicine     Full-text available via subscription   (Followers: 25, 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: 6, 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: 215, 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: 239, SJR: 1.58, CiteScore: 3)
Animal Feed Science and Technology     Hybrid Journal   (Followers: 7, SJR: 0.937, CiteScore: 2)
Animal Reproduction Science     Hybrid Journal   (Followers: 7, SJR: 0.704, CiteScore: 2)
Annales d'Endocrinologie     Full-text available via subscription   (Followers: 3, SJR: 0.451, CiteScore: 1)

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Similar Journals
Journal Cover
Advanced Engineering Informatics
Journal Prestige (SJR): 1.167
Citation Impact (citeScore): 4
Number of Followers: 13  
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1474-0346 - ISSN (Online) 1474-0346
Published by Elsevier Homepage  [3161 journals]
  • Two-stage stochastic programming model for generating container yard
           template under uncertainty and traffic congestion
    • Abstract: Publication date: January 2020Source: Advanced Engineering Informatics, Volume 43Author(s): Junliang He, Caimao Tan, Wei Yan, Wei Huang, Mei Liu, Hang YuAbstractYard template is a space assignment at the tactical level, which is kept unchanged within a long period of time and significantly impacts the handling efficiency of a container terminal. This paper addresses a yard template planning problem considering uncertainty and traffic congestion. A two-stage stochastic programming model is formulated for minimizing the risk of containers with no available slots in the designated yard area and minimizing total transportation distances. The first-stage model is formulated for assigning vessels in each block without considering the physical location properties of blocks, and the second-stage model is formulated for designating physical locations to all blocks. Subsequently, a solving framework based on genetic algorithm is proposed for solving the first-stage model, and the CPLEX (a commercial solver) is used for solving the second-stage model. Finally, numerical experiments and scenario analysis are conducted to validate the effectiveness of the proposed model and the efficiency of the proposed solution approach.
  • Ensemble data mining modeling in corrosion of concrete sewer: A
           comparative study of network-based (MLPNN & RBFNN) and tree-based (RF,
           CHAID, & CART) models
    • Abstract: Publication date: January 2020Source: Advanced Engineering Informatics, Volume 43Author(s): Mohammad Zounemat-Kermani, Dietmar Stephan, Matthias Barjenbruch, Reinhard HinkelmannAbstractThis research aims to evaluate ensemble learning (bagging, boosting, and modified bagging) potential in predicting microbially induced concrete corrosion in sewer systems from the data mining (DM) perspective. Particular focus is laid on ensemble techniques for network-based DM methods, including multi-layer perceptron neural network (MLPNN) and radial basis function neural network (RBFNN) as well as tree-based DM methods, such as chi-square automatic interaction detector (CHAID), classification and regression tree (CART), and random forests (RF). Hence, an interdisciplinary approach is presented by combining findings from material sciences and hydrochemistry as well as data mining analyses to predict concrete corrosion. The effective factors on concrete corrosion such as time, gas temperature, gas-phase H2S concentration, relative humidity, pH, and exposure phase are considered as the models’ inputs. All 433 datasets are randomly selected to construct an individual model and twenty component models of boosting, bagging, and modified bagging based on training, validating, and testing for each DM base learners. Considering some model performance indices, (e.g., Root mean square error, RMSE; mean absolute percentage error, MAPE; correlation coefficient, r) the best ensemble predictive models are selected. The results obtained indicate that the prediction ability of the random forests DM model is superior to the other ensemble learners, followed by the ensemble Bag-CHAID method. On average, the ensemble tree-based models acted better than the ensemble network-based models; nevertheless, it was also found that taking the advantages of ensemble learning would enhance the general performance of individual DM models by more than 10%.
  • An integrated decision-making method for product design scheme evaluation
           based on cloud model and EEG data
    • Abstract: Publication date: January 2020Source: Advanced Engineering Informatics, Volume 43Author(s): Shanhe Lou, Yixiong Feng, Zhiwu Li, Hao Zheng, Jianrong TanAbstractSelecting the optimal design scheme is a vital task in the product design area. It not only improves the performance of the product, but also leads to the greatest satisfaction of customers. However, existing methods express qualitative evaluation information roughly, and none of them has taken the implicit psychological states of customers into consideration. Therefore, an integrated decision-making method for product design scheme evaluation is proposed. This method applies the cloud model to facilitate the evaluation process of experts and uses the EEG data to reveal the psychological states of customers. Benefit from the probability theory and fuzzy set theory, the cloud model deals with the fuzziness and randomness simultaneously. It can decrease the cognitive discrepancy of experts and allow the information distortion to be neutralized to a great extent. Since the experts are not the final users of products, the evaluation results from experts cannot truly reflect the psychological states of customers when they use the product. An experiment is designed to collect the EEG data which can reveal the implicit psychological states of customers. The recorded data are segmented based on the operation process and tagged with the self-reported psychological states. Subsequently, the wavelet packet decomposition is applied and the sample entropy of each EEG frequency band is extracted as the feature. Taking advantage of the random forest classifier, the psychological states of customers can be classified with the average accuracy of 90.76%. This study can lead to a practical system for automatic assessment of psychological states in future applications. The evaluation process of elevator design schemes is conducted as a case study to illustrate the feasibility of the proposed method.
  • Intelligent compilation of patent summaries using machine learning and
           natural language processing techniques
    • Abstract: Publication date: January 2020Source: Advanced Engineering Informatics, Volume 43Author(s): Amy J.C. Trappey, Charles V. Trappey, Jheng-Long Wu, Jack W.C. WangPatents are a type of intellectual property with ownership and monopolistic rights that are publicly accessible published documents, often with illustrations, registered by governments and international organizations. The registration allows people familiar with the domain to understand how to re-create the new and useful invention but restricts the manufacturing unless the owner licenses or enters into a legal agreement to sell ownership of the patent. Patents reward the costly research and development efforts of inventors while spreading new knowledge and accelerating innovation. This research uses artificial intelligence natural language processing, deep learning techniques and machine learning algorithms to extract the essential knowledge of patent documents within a given domain as a means to evaluate their worth and technical advantage. Manual patent abstraction is a time consuming, labor intensive, and subjective process which becomes cost and outcome ineffective as the size of the patent knowledge domain increases. This research develops an intelligent patent summarization methodology using artificial intelligence machine learning approaches to allow patent domains of extremely large sizes to be effectively and objectively summarized, especially for cases where the cost and time requirements of manual summarization is infeasible. The system learns to automatically summarize patent documents with natural language texts for any given technical domain. The machine learning solution identifies technical key terminologies (words, phrases, and sentences) in the context of the semantic relationships among training patents and corresponding summaries as the core of the summarization system. To ensure the high performance of the proposed methodology, ROUGE metrics are used to evaluate precision, recall, accuracy, and consistency of knowledge generated by the summarization system. The Smart machinery technologies domain, under the sub-domains of control intelligence, sensor intelligence and intelligent decision-making provide the case studies for the patent summarization system training. The cases use 1708 training pairs of patents and summaries while testing uses 30 randomly selected patents. The case implementation and verification have shown the summary reports achieve 90% and 84% average precision and recall ratios respectively.
  • Benchmark value determination of energy efficiency indexes for coal-fired
           power units based on data mining methods
    • Abstract: Publication date: January 2020Source: Advanced Engineering Informatics, Volume 43Author(s): Dongchao Chen, Lihua Cao, Heyong SiAbstractThe operational optimisation of coal-fired power units is important for saving energy and reducing losses in the electric power industry. One of the key issues is how to determine the benchmark values of the energy efficiency indexes of the units. Therefore, a new framework for determining these benchmark values is proposed, based on data mining methods. First, the energy efficiency key performance indicators (KPIs) associated with the net coal consumption rate (NCCR) were selected based on the domain knowledge. Second, the decision-making samples with minimal NCCR were acquired with the fuzzy C-means (FCM) clustering algorithm, and the corresponding clustering centres were employed as the benchmark values. Finally, based on the support vector regression (SVR) algorithm, the target values of the NCCR were obtained with the KPIs as input, and the energy saving potential was evaluated by comparing the target values with the historical values of the NCCR. An actual on-duty 1000 MW unit was taken as study unit, and the results show that the energy saving potential is remarkable when the operators adjust the KPIs based on the calculated benchmark values.
  • Deep learning-based extraction of construction procedural constraints from
           construction regulations
    • Abstract: Publication date: January 2020Source: Advanced Engineering Informatics, Volume 43Author(s): Botao Zhong, Xuejiao Xing, Hanbin Luo, Qirui Zhou, Heng Li, Timothy Rose, Weili FangAbstractConstruction procedural constraints are critical in facilitating effective construction procedure checking in practice and for various inspection systems. Nowadays, the manual extraction of construction procedural constraints is costly and time-consuming. The automatic extraction of construction procedural constraint knowledge (e.g., knowledge entities and interlinks/relationships between them) from regulatory documents is a key challenge. Traditionally, natural language processing is implemented using either rule-based or machine learning approaches. Limited efforts on rule-based extraction of construction regulations often rely on pre-defined vocabularies and involve heavy feature engineering. Based on characteristics of the knowledge expression of construction procedural constraints in Chinese regulations, this paper explores a hybrid deep neural network, combining the bidirectional long short-term memory (Bi-LSTM) and the conditional random field (CRF), for the automatic extraction of the qualitative construction procedural constraints. Based on the proposed deep neural network, the recognition and extraction of named entities and relations between them are realized. Unlike existing information extraction research efforts using rule-based methods, the proposed hybrid deep learning approach can be applied without complex handcrafted features engineering. Besides, the long distance dependency relationships between different entities in regulations are considered. The model implementation results demonstrate the good performance of the end-to-end deep neural network in the extraction of construction procedural constraints. This study can be considered as one of the early explorations of knowledge extraction from construction regulations.
  • Dynamic BIM component recommendation method based on probabilistic matrix
           factorization and grey model
    • Abstract: Publication date: January 2020Source: Advanced Engineering Informatics, Volume 43Author(s): Pin-Chan Lee, Danbing Long, Bo Ye, Tzu-Ping LoAbstractWith rapid advances in building information modeling (BIM), a huge amount of BIM components has been built to increase design efficiency. Meanwhile, finding the appropriate BIM component in the huge library has become a challenge. Besides the methods of case-based reasoning (CBR) or multi-attribute decision model (MADM), the probabilistic matrix factorization (PMF) method of a recommendation system can be an efficient alternative. However, the user behavior patterns (i.e., the rating matrices) are changing with time to influence the recommendation precision. Therefore, this study aims to enhance the dynamic recommendation ability for BIM components by proposing a hybrid probabilistic matrix factorization method (PMF-GMn). The latent user preference matrix and the latent BIM component feature matrix can be generated by the PMF method from the rating matrix. Then, the predicted latent matrices can be obtained by the optimized grey model. Finally, the predicted latent matrices are further combined into the predicted rating matrix to recommend the appropriate BIM components. An illustrative example of the prefabricated building design is used to demonstrate the feasibility. This experiment is implemented by inviting twenty users to use the proposed SharePBIM platform for five months. The statistical results indicated that PMF-GMn can provide better performance than PMF in both two criteria of RMSE and Recall@k.
  • Guidelines for applied machine learning in construction industry—A case
           of profit margins estimation
    • Abstract: Publication date: January 2020Source: Advanced Engineering Informatics, Volume 43Author(s): Muhammad Bilal, Lukumon O. OyedeleAbstractThe progress in the field of Machine Learning (ML) has enabled the automation of tasks that were considered impossible to program until recently. These advancements today have incited firms to seek intelligent solutions as part of their enterprise software stack. Even governments across the globe are motivating firms through policies to tape into ML arena as it promises opportunities for growth, productivity and efficiency. In reflex, many firms embark on ML without knowing what it entails. The outcomes so far are not as expected because the ML, as hyped by tech firms, is not the silver bullet. However, whatever ML offers, firms urge to capitalise it for their competitive advantage. Applying ML to real-life construction industry problems goes beyond just prototyping predictive models. It entails intensive activities which, in addition to training robust ML models, provides a comprehensive framework for answering questions asked by construction folks when intelligent solutions are getting deployed at their premises to substitute or facilitate their decision-making tasks. Existing ML guidelines used in the IT industry are vastly restricted to training ML models. This paper presents guidelines for Applied Machine Learning (AML) in the construction industry from training to operationalising models, which are drawn from our experience of working with construction folks to deliver Construction Simulation Tool (CST). The unique aspect of these guidelines lies not only in providing a novel framework for training models but also answering critical questions related to model confidence, trust, interpretability, bias, feature importance and model extrapolation capabilities. Generally, ML models are presumed black boxes; hence argued that nobody knows what a model learns and how it generates predictions. Even very few ML folks barely know approaches to answer questions asked by the end users. Without explaining the competence of ML, the broader adoption of intelligent solutions in the construction industry cannot be attained. This paper proposed a detailed process for AML to develop intelligent solutions in the construction industry. Most discussions in the study are elaborated in the context of profit margin estimation for new projects.
  • Photo-realistic visualization of seismic dynamic responses of urban
           building clusters based on oblique aerial photography
    • Abstract: Publication date: January 2020Source: Advanced Engineering Informatics, Volume 43Author(s): Zhen Xu, Yuan Wu, Xinzheng Lu, Xinlei JinAbstractHighly realistic visualizations of seismic dynamic responses of building clusters are critical for earthquake safety education. To this end, a photo-realistic visualization method of the seismic dynamic responses of urban building clusters is proposed based on oblique aerial photography. Specifically, a sparsification algorithm of aerial photograph footprints and the model optimization solutions are designed to reduce the size of a city model reconstructed by oblique aerial photography. A building segmentation algorithm based on Boolean operations and building footprints is designed to separate buildings from a reconstructed three-dimensional city model. A visualization algorithm for the seismic dynamic responses of building clusters is designed based on the Callback mechanism, by which the shaking process of building clusters can be realistically displayed according to the results of a city-scale nonlinear time-history analysis. New Beichuan City in China is adopted as a case study to visualize seismic dynamic response. The visualization produced by the proposed method is more realistic than that of the finite element method and can support decision making on earthquake safety actions. The outcome of this study provides well-founded and photo-realistic scenes of the seismic dynamic response of building clusters and has promising application prospects for earthquake safety education.
  • An operation synchronization model for distribution center in E-commerce
           logistics service
    • Abstract: Publication date: January 2020Source: Advanced Engineering Informatics, Volume 43Author(s): Ying Yu, Chenglin Yu, Gangyan Xu, Ray Y. Zhong, George Q. HuangAbstractThis paper is among the first that proposes a synchronization measurement model for the distribution centre operation synchronization (DCOS) problem, which aims to ensure the E-commerce order’s punctuality and synchronization at the same time. The main motivation of DCOS is that the intensified competition in E-commerce market makes efficient E-commerce logistics service extremely important, which means saving logistics cost and ensuring customer service at the same time. The synchronized operation may be a possible solution to ensure efficient order transhipment in the distribution center and to save cost. We thus introduce a measurement approach that is able to address the distribution center operation synchronization (DCOS) problem such as the trade-off relationship between synchronization and punctuality. In order to get persuasive conclusions, we adopt data from a real practice case and apply CPLEX to get the optimal solution. Our computational results show that considering the asynchronous cost in the total cost objective function will greatly improve the operation synchronization in the distribution center, by saving the storage space, the equipment, and the labour resources. And if the storage cost is in a reasonable range, the synchronized operation can be realized while the punctuality is also optimized. It is found in our case that the most efficient way to improve distribution center operation is expanding inbound operation capacity.
  • Information requirements for multi-level-of-development BIM using
           sensitivity analysis for energy performance
    • Abstract: Publication date: January 2020Source: Advanced Engineering Informatics, Volume 43Author(s): Manav Mahan Singh, Philipp GeyerAbstractThe concept of multi-Level-of-Development (multi-LOD) modelling represents a flexible approach of information management and compilation in building information modelling (BIM) on a set of consistent levels. From an energy perspective during early architectural design, the refinement of design parameters by addition of information allows a more precise prediction of building performance. The need for energy-efficient buildings requires a designer to focus on the parameters in order of their ability to reduce uncertainty in energy performance to prioritise energy relevant decisions. However, there is no method for assigning and prioritising information for a particular level of multi-LOD. In this study, we performed a sensitivity analysis of energy models to estimate the uncertainty caused by the design parameters in energy prediction. This study allows to rank the design parameters in order of their influence on the energy prediction and determine the information required at each level of multi-LOD approach. We have studied the parametric energy model of different building shapes representing architectural design variation at the early design stage. A variance-based sensitivity analysis method is used to calculate the uncertainty contribution of each design parameter. The three levels in the uncertainty contribution by the group of parameters are identified which form the basis of information required at each level of multi-LOD BIM approach. The first level includes geometrical parameters, the second level includes technical specification and operational design parameters, and the third level includes window construction and system efficiency parameters. These findings will be specifically useful in the development of a multi-LOD approach to prioritise performance relevant decisions at early design phases.
  • Improvement of transportation cost estimation for prefabricated
           construction using geo-fence-based large-scale GPS data feature extraction
           and support vector regression
    • Abstract: Publication date: January 2020Source: Advanced Engineering Informatics, Volume 43Author(s): SangJun Ahn, SangUk Han, Mohamed Al-HusseinIn panelized construction, transportation is an essential process linking a manufacturing facility to a project’s jobsite using hauling equipment (e.g., trucks and trailers). Accordingly, the cost associated with transportation operations is considerable compared to a traditional stick build. Nevertheless, transportation cost estimation has often relied on a fixed-cost approach, regarding the cost as part of the overhead cost, rather than conducting detailed estimation of actual transportation operations. This is because operation-level data might be challenging to collect and analyze in practice. In this regard, the prevalent use of GPS devices for construction equipment may provide an automated means of monitoring the operations of transportation equipment, and large and detailed spatial and temporal data can be generated from multiple pieces of equipment in multiple construction projects on a daily basis or even in real time. This study thus proposes a spatial and temporal data filtering and abstracting approach to transportation cost estimation using fleet GPS data which extracts equipment activities from the GPS data and accordingly predicts the transportation demands required for an individual project. From large-scale GPS data, key operation information, such as the number of trailers and durations required (i.e., transportation demands), is extracted using a geo-fence and a rule-based equipment operation analysis algorithm. Then, the extracted transportation demand information, along with related project specifications, is used to train support vector regression (SVR) models for the purpose of predicting the transportation demand in new projects, which is in turn utilized to estimate the transportation cost using the relevant transportation unit cost of the equipment. To evaluate the performance, GPS datasets collected from 221 panelized residential projects over a period of 8 months are used to train the prediction model and are compared with actual transportation costs estimated in practice. The results show that the SVR model has an accuracy of 86% and 88% in predicting the number of trailers and the duration, respectively. For the cost estimation performance, the results reveal that the average cost difference of 57% between the fixed cost and the actual transportation cost was reduced to 14% by implementing the GPS-data-based method in various project locations and for projects of various sizes. The GPS-data-based estimation approach thus is found to provide a more accurate transportation cost estimation result for various panelized construction projects, and the method improves the understanding of large-scale spatial and temporal equipment data while increasing the utilization of the GPS data already available.
  • Sensor data reconstruction using bidirectional recurrent neural network
           with application to bridge monitoring
    • Abstract: Publication date: October 2019Source: Advanced Engineering Informatics, Volume 42Author(s): Seongwoon Jeong, Max Ferguson, Rui Hou, Jerome P. Lynch, Hoon Sohn, Kincho H. LawAbstractSensors are now commonly employed for monitoring and controlling of engineering systems. Despite significant advances in sensor technologies and their reliability, sensor fault is inevitable. Sensor data reconstruction methods have been studied to recover the missing or faulty sensor data, as well as to enable sensor fault detection and identification. Most existing sensor data reconstruction methods use only the spatial correlations among the sensor data, but they rarely consider the temporal correlations among the data. Use of temporal correlations among the sensor data can potentially improve the accuracy for reconstructing the data. This paper presents a data-driven bidirectional recurrent neural network (BRNN) for sensor data reconstruction, taking into consideration the spatiotemporal correlations among the sensor data. The methodology is demonstrated using the sensor data collected from the Telegraph Road Bridge located along the I-275 Corridor in Michigan. The results show that the BRNN-based method performs better than other current data-driven methods for accurately reconstructing the sensor data.
  • Smart concept design based on recessive inheritance in complex
           electromechanical system
    • Abstract: Publication date: January 2020Source: Advanced Engineering Informatics, Volume 43Author(s): Peng Zhang, Zifeng Nie, Yafan Dong, Zhimin Zhang, Fei Yu, Runhua TanAbstractDue to the substantial increase in users demand, the scale of complex electromechanical systems is rapidly enlarging when systems are rapidly merging, and the complexity of the system has been greatly increased. Even if the original system is running well, singular phenomena often occurs in the process of complex electromechanical systems integration, which leads to the failure of the functional requirements of the new generation of products. In the face of the singular phenomena of complex electromechanical systems, traditional solutions focus more on solving the problems existing in the current complex electromechanical systems, and do not deeply study the root causes of the singular phenomena in the design process of complex electromechanical systems. The singular phenomena of system has high concealment and is not easy to be detected at the design stage, and similar singular phenomena will appear repeatedly in multiple generations of the same product. These characteristics are very similar to the characteristics of recessive inheritance of biological systems. Therefore, the paper will compare complex electromechanical systems with biological systems, and propose a recessive inheritance mechanism of complex electromechanical systems that can be used in smart concept design by introducing the recessive inheritance mechanism of biological system into the complex electromechanical system design process. First, use function trimming to build a functional gene for new complex electromechanical systems. Second, combine the Length and Time dimension chart (L-T chart) and Computer Aided Innovation (CAI) to find the recessive parameters that may exist in the design of the new generation system and analyze the possible coupling phenomena between the recessive parameters. Then, using the Invention Problem Solving Theory (TRIZ) tools to solve the coupling relationship between recessive parameters to reduce the possibility of singular phenomena in the new generation system, forming a new generation of complex electromechanical system smart concept design theory framework. Finally, for the design example of the new generation of energy-saving surface platform, the proposed method is used to determine the design scheme, establish a 3D model and verify the feasibility and scientificity of the theory by Ansys analysis.
  • Smart work packaging-enabled constraint-free path re-planning for tower
           crane in prefabricated products assembly process
    • Abstract: Publication date: January 2020Source: Advanced Engineering Informatics, Volume 43Author(s): Xiao Li, Hung-lin Chi, Peng Wu, Geoffrey Qiping ShenAbstractLack of constraint-free crane path planning is one of the critical concerns in the dynamic on-site assembly process of prefabrication housing production (PHP). For decades, researchers and practitioners have endeavored to improve both the efficiency and safety of crane path planning from either static environment or re-planning the path when colliding with constraints or periodically updating the path in the dynamic environment. However, there is a lack of approach related to the in-depth exploration of the nature of dynamic constraints so as to assist the crane operators in making adaptive path re-planning decisions by categorizing and prioritizing constraints. To address this issue, this study develops the smart work packaging (SWP)-enabled constraints optimization service. This service embraces the core characteristics of SWP, including adaptivity, sociability, and autonomy to achieve autonomous initial path planning, networked constraints classification, and adaptive decisions on path re-planning. This service is simulated and verified in the BIM environment, and it is found that SWP-enabled constraints optimization service can generate the constraint-free path when it is necessary.
  • Smartphone customer segmentation based on the usage pattern
    • Abstract: Publication date: October 2019Source: Advanced Engineering Informatics, Volume 42Author(s): Hansi Chen, Lei Zhang, Xuening Chu, Bo YanAbstractThe dimension used to measure user heterogeneity plays a key role in the customer segmentation. For most traditional products, customer requirements (CRs) for products are often related to their basic characteristics and psychological characteristics. Therefore, in the traditional market segmentation theory, the dimensions used to distinguish customer differences include the demographic attributes such as gender, age, income etc., or the customer psychology. Customer behaviour, especially the purchasing behaviour, is also used as an important dimension for market segmentation. However, for the smart product like smartphone with rich functionality and multi-interactions, the customer’s interaction preference can be fully released. That makes the heterogeneity of customer stems from the usage characteristics rather than the traditional demographic attributes. Hence, the usage pattern is defined and proposed as the description of the usage characteristics and be used to measure the heterogeneity of customers in this research. The Equivalence CLAss Transformation (ECLAT) algorithm is employed to identify the customer’s APP frequent sets from the operating data and to construct the usage pattern. Thereafter, the customer can be segmented based on the distance among customers’ usage pattern. Compared with the demographic attributes, the usage pattern can provide more reliable and truthful measures for the smartphone customer segmentation.
  • Computer vision for behaviour-based safety in construction: A review and
           future directions
    • Abstract: Publication date: January 2020Source: Advanced Engineering Informatics, Volume 43Author(s): Weili Fang, Peter E.D. Love, Hanbin Luo, Lieyun DingAbstractThe process of identifying and bringing to the fore people’s unsafe behaviour is a core function of implementing a behaviour-based safety (BBS) program in construction. This can be a labour-intensive and challenging process but is needed to enable people to reflect and learn about how their unsafe actions can jeopardise not only their safety but that of their co-workers. With advances being made in computer vision, the capability exists to automatically capture and identify unsafe behaviour and hazards in real-time from two-dimensional (2D) digital images/videos. The corollary developments in computer vision have stimulated a wealth of research in construction to examine its potential application to practice. Hindering the application of computer vision in construction has been its inability to accurately, and generalise the detection of objects. To address this shortcoming, developments in deep learning have provided computer vision with the ability to improve the accuracy, reliability and ability to generalise object detection and therefore its usage in construction. In this paper we review the developments of computer vision studies that have been used to identify unsafe behaviour from 2D images that arises on construction sites. Then, in light of advances made with deep learning, we examine and discuss its integration with computer vision to support BBS. We also suggest that future computer-vision research should aim to support BBS by being able to: (1) observe and record unsafe behaviour; (2) understand why people act unsafe behaviour; (3) learn from unsafe behaviour; and (4) predict unsafe behaviour.
  • Soldering defect detection in automatic optical inspection
    • Abstract: Publication date: January 2020Source: Advanced Engineering Informatics, Volume 43Author(s): Wenting Dai, Abdul Mujeeb, Marius Erdt, Alexei SourinAbstractThis paper proposes an integrated detection framework of solder joint defects in the context of Automatic Optical Inspection (AOI) of Printed Circuit Boards (PCBs). Both localization and classifications tasks were considered. For the localization part, in contrast to the existing methods that are highly specified for particular PCBs, we used a generic deep learning method which can be easily ported to different configurations of PCBs and soldering technologies and also gives real-time speed and high accuracy. For the classification part, an active learning method was proposed to reduce the labeling workload when a large labeled training database is not easily available because it requires domain-specified knowledge. The experiments show that the localization method is fast and accurate. In addition, high accuracy with only minimal user input was achieved in the classification framework on two different datasets. The results also demonstrated that our method outperforms three other active learning benchmarks.
  • BIM-based task-level planning for robotic brick assembly through
           image-based 3D modeling
    • Abstract: Publication date: January 2020Source: Advanced Engineering Informatics, Volume 43Author(s): Lieyun Ding, Weiguang Jiang, Ying Zhou, Cheng Zhou, Sheng LiuAbstractThe application of robotics in the assembly of building bricks has become a popular topic, while the planning of construction robots is still lagged far behind the manufacturing industry. New robotic assembly task with manual teaching–planning method is always time consuming. A task-level planning method was proposed, and the implementation details were described to improve the planning efficiency of robotic brick assembly without affecting accuracy. In this work, a BIM (Building Information Model)-based robotic assembly model that contains all the required information for planning was proposed. Image-based 3D modeling was utilized to help the calibration of the robotic assembly scene and building task models. The placement point coordinates of each assembly brick were generated in the robot base coordinate system. Finally, three different building information model tasks of modular structures (e.g., wall, stair, and pyramid) were designed. The feasibility and effectiveness of the proposed method were verified by comparing the efficiency and accuracy of three models through manual teaching and task-level planning.
  • Integrated parametric multi-level information and numerical modelling of
           mechanised tunnelling projects
    • Abstract: Publication date: January 2020Source: Advanced Engineering Informatics, Volume 43Author(s): Jelena Ninić, Christian Koch, Andre Vonthron, Walid Tizani, Markus KönigAbstractThis paper presents a concept for parametric modelling of mechanized tunnelling within a state of the art design environment, as the basis for design assessments for different levels of details (LoDs). To this end, a parametric representation of each system component (soil with excavation, tunnel lining with grouting, Tunnel Boring Machine (TBM) and buildings) is developed in an information model for three LoDs (high, medium and low) and used for the automated generation of numerical models of the tunnel construction process and soil-structure interaction. The platform enables a flexible, user-friendly generation of the tunnel structure for arbitrary alignments based on predefined structural templates for each component, supporting the design process and at the same time providing an insight into the stability and safety of the design. This model, with selected optimal LoDs for each component, dependent on the objective of the analysis, is used for efficient design and process optimisation in mechanized tunnelling. Efficiency and accuracy are further demonstrated through an error-free exchange of information between Building Information Modelling (BIM) and the numerical simulation and with significantly reduced computational effort. The interoperability of the proposed multi-level framework is enabled through the use of an efficient multi-level representation context of the Industry Foundation Classes (IFC). The results reveal that this approach is a major step towards sensible modelling and numerical analysis of complex tunnelling project information at the early design stages.
  • The design of an IoT-based route optimization system: A smart
           product-service system (SPSS) approach
    • Abstract: Publication date: October 2019Source: Advanced Engineering Informatics, Volume 42Author(s): Saijun Shao, Gangyan Xu, Ming LiAbstractThe aim of route optimization system (ROS) is to design a set of vehicle routes to fulfill transportation demands, in an attempt to minimize cost and/or other negative social and environmental impacts. ROS, established based on the fruitful studies of vehicle routing problem (VRP), has been applied in various industries and forms. During daily operations, dynamic traffic conditions, varying restriction policies, road constructions, drivers’ progressing familiarity with the routes and destinations are all common factors affecting the performance of ROS. However, most current systems are designed in a one-way and open-loop manner, i.e. these systems do not track how the planned vehicle routes are performed, which hinders the continuous improvement of the system and would lead to the failure of the system. This study proposes a smart product-service system (SPSS) approach to design an IoT-based ROS, arguing that the product (i.e. the ROS) and services (updating base data and learning users’ behaviors automatically to optimize the system) should be designed as a bundle. For this end, IoT devices are employed to acquire real-time information and feedbacks of vehicles and drivers, which are used to assess the execution of planned routes and dynamically modify the base data. Moreover, the driving records from IoT devices reveal drivers’ improving familiarity with routes and destinations, which will be considered to optimize the assignment of routes to drivers. Finally, we use a case of retailing industry to show the advantages of the proposed SPSS approach.
  • Production service system enabled by cloud-based smart resource hierarchy
           for a highly dynamic synchronized production process
    • Abstract: Publication date: October 2019Source: Advanced Engineering Informatics, Volume 42Author(s): Kai Zhang, Ming Wan, Ting Qu, Hongfei Jiang, Peize Li, Zefeng Chen, Jinjie Xiang, Xiaodong He, Congdong Li, George Q. HuangAbstractThe rapidly changing market demands entail a modern production system to cope with ever more diversified production orders. Not only the parameters and flow of the production process, sometimes the production resources and the corresponding application systems which make production decision and control even need to be reconfigurable. Due to the inability to provide dynamically reconfigurable manufacturing resources, systems and application systems, enterprises have to sacrifice profits to narrow their business scope or take risks to purchase a large number of production resources. In any case, it has brought operational burden to enterprises. This paper systematically analyses the production management requirements of a large-scale production system in terms of both hardware (production equipment) and software (application system) which is oriented to dynamic production demands, and then proposes a production service system enabled by cloud-based smart resource hierarchy (PnSS-CSRH). The platform is based on an open resource management system and inherits the general cloud structure and the AUTOM framework. PnSS-CSRH provides industrial users with integrated and synchronization services for the software and hardware resources involved in the production process in the PnSS mode. Using the integrated service mode, PnSS-CSRH not only helps resource providers to increase the frequency of resource leases, provides customers with targeted and systemic hardware and software overall solutions, and increases the platform usage rate, which brings more benefits to the stakeholders of PnSS-CSRH. At last, the PnSS-CSRH is used to provide services to the case company to validate the effectiveness.
  • An Internet of Things-enabled BIM platform for modular integrated
           construction: A case study in Hong Kong
    • Abstract: Publication date: October 2019Source: Advanced Engineering Informatics, Volume 42Author(s): Yue Zhai, Ke Chen, Jason X. Zhou, Jin Cao, Zhongyuan Lyu, Xin Jin, Geoffrey Q.P. Shen, Weisheng Lu, George Q. HuangAbstractIn recent years, Building Information Modelling (BIM) has been acting an important role in the delivery of Modular Integrated Construction (MiC) project. However, the full potential of BIM to MiC project cannot be realized without accurate information collection, timely information exchange, and automatic decision support throughout the project life cycle. In order to fulfil such requirements, this paper aims to develop an Internet of Things-enabled BIM platform (IBIMP) for the MiC project. A real-life project located in Hong Kong were deeply explored for developing the platform. The IBIMP consists of smart construction objects (SCOs) equipped with smart trinity tag (STT) and GPS sensor, smart gateway system, data source management service, location-based service, rule-based progress control service, as well as decision support services for prefabrication production, transportation, and on-site assembly processes. With the combination of advanced Internet of Things (IoT) technology and BIM technology, the barriers that hamper the possible functions of BIM can be overcame. By using application scenarios of a subsided sale flats MiC project in Hong Kong as examples, this study demonstrates how problems encountered by independent stakeholders such as inconvenient data collection, lack of automatic decision support, and incomplete information can be addressed by the IBIMP.
  • A high performance hybrid SSVEP based BCI speller system
    • Abstract: Publication date: October 2019Source: Advanced Engineering Informatics, Volume 42Author(s): D. Saravanakumar, M. Ramasubba ReddyAbstractThe existing EEG based keyboard/speller systems have a tradeoff between the target detection time and classification accuracy. This study focuses on increasing the accuracy and probability of target classification rates in the SSVEP based speller system. We proposed two different types of hybrid SSVEP system by combining SSVEP with vision based eye gaze tracker (VET) and electro-oculogram (EOG). Thirty six targets were randomly chosen for this study and their corresponding visual stimulus was presented with unique frequencies. The visual stimuli were segregated into three groups and each group were arranged into different regions (left/middle/right) of the keyboard/speller layout for improving the probability of target detection rate. The VET/ EOG data were utilized to identify the regions that belong to the selected target. The region/group determination decreases the issue of misclassification of SSVEP frequencies. The averaged spelling accuracies of SSVEP-VET and SSVEP-EOG system for all the subjects is 91.2% and 91.39% respectively. Later, a visual feedback was added to the SSVEP-EOG system (SSVEP-EOG-VF) for improving the target detection rate. In this case, an average classification accuracy of 98.33% was obtained with the information transfer rate (ITR) of 69.21 bits/min for all the subjects. An accuracy of 100% was obtained for five subjects with the ITR of 74.1 bits/min in this system.
  • Crowdsourced reliable labeling of safety-rule violations on images of
           complex construction scenes for advanced vision-based workplace safety
    • Abstract: Publication date: October 2019Source: Advanced Engineering Informatics, Volume 42Author(s): Yanyu Wang, Pin-Chao Liao, Cheng Zhang, Yi Ren, Xinlu Sun, Pingbo TangConstruction workplace hazard detection requires engineers to analyze scenes manually against many safety rules, which is time-consuming, labor-intensive, and error-prone. Computer vision algorithms are yet to achieve reliable discrimination of anomalous and benign object relations underpinning safety violation detections. Recently developed deep learning-based computer vision algorithms need tens of thousands of images, including labels of the safety rules violated, in order to train deep-learning networks for acquiring spatiotemporal reasoning capacity in complex workplaces. Such training processes need human experts to label images and indicate whether the relationship between the worker, resource, and equipment in the scenes violate spatiotemporal arrangement rules for safe and productive operations. False alarms in those manual labels (labeling no-violation images as having violations) can significantly mislead the machine learning process and result in computer vision models that produce inaccurate hazard detections. Compared with false alarms, another type of mislabels, false negatives (labeling images having violations as “no violations”), seem to have fewer impacts on the reliability of the trained computer vision models.This paper examines a new crowdsourcing approach that achieves above 95% accuracy in labeling images of complex construction scenes having safety-rule violations, with a focus on minimizing false alarms while keeping acceptable rates of false negatives. The development and testing of this new crowdsourcing approach examine two fundamental questions: (1) How to characterize the impacts of a short safety-rule training process on the labeling accuracy of non-professional image annotators? And (2) How to properly aggregate the image labels contributed by ordinary people to filter out false alarms while keeping an acceptable false negative rate? In designing short training sessions for online image annotators, the research team split a large number of safety rules into smaller sets of six. An online image annotator learns six safety rules randomly assigned to him or her, and then labels workplace images as “no violation” or ‘violation” of certain rules among the six learned by him or her. About one hundred and twenty anonymous image annotators participated in the data collection. Finally, a Bayesian-network-based crowd consensus model aggregated these labels from annotators to obtain safety-rule violation labeling results. Experiment results show that the proposed model can achieve close to 0% false alarm rates while keeping the false negative rate below 10%. Such image labeling performance outdoes existing crowdsourcing approaches that use majority votes for aggregating crowdsourced labels. Given these findings, the presented crowdsourcing approach sheds lights on effective construction safety surveillance by integrating human risk recognition capabilities into advanced computer vision.Graphical abstractGraphical abstract for this article
  • A method of automatic extraction of parameters of multi-LoD BIM models for
           typical components in wooden architectural-heritage structures
    • Abstract: Publication date: October 2019Source: Advanced Engineering Informatics, Volume 42Author(s): Haoyu Liu, Linlin Xie, Liwen Shi, Miaole Hou, Aiqun Li, Yungang HuAbstractA building-information-modeling (BIM) model, which is established based on high-fidelity point-cloud data, can be well used to preserve architectural heritage. Two related critical issues for this conservation are multiple-level-of-detail (multi-LoD) parametric models that emphasize different protection requirements for typical components, and a method for automatically extracting the corresponding parameters from a high-fidelity point cloud. Taking typical Chinese wooden architectural-heritage structures as an example, multi-LoD principles for typical components without damage are proposed according to the different requirements. Then, a framework of multi-LoD parametric models was developed and implemented in BIM. Based on this, a method for automatically extracting the abovementioned parameters is developed and the critical parameters of this method are recommended. To validate the reliability and efficiency of this method, the parameters of multi-LoD models of typical components are extracted. The results indicate that the relative and absolute errors of values of such parameters are mostly less than 2% and 0.5 mm, respectively. Moreover, this method is capable of extracting parameters from millions of point-cloud data within 7 min, thus validating the high efficiency and reliability of the proposed method.
  • Classification of brain signal (EEG) induced by shape-analogous letter
    • Abstract: Publication date: October 2019Source: Advanced Engineering Informatics, Volume 42Author(s): Rohit Bose, Sim Kuan Goh, Kian F. Wong, Nitish Thakor, Anastasios Bezerianos, Junhua LiAbstractVisual perception of English letters involves different underlying brain processes including brain activity alteration in multiple frequency bands. However, shape analogous letters elicit brain activities which are not obviously distinct and it is therefore difficult to differentiate those activities. In order to address discriminative feasibility and classification performance of the perception of shape-analogous letters, we performed an experiment in where EEG signals were obtained from 20 subjects while they were perceiving shape analogous letters (i.e., ‘p’, ‘q’, ‘b’, and ‘d’). Spectral power densities from five typical frequency bands (i.e., delta, theta, alpha, beta and gamma) were extracted as features, which were then classified by either individual widely-used classifiers, namely k-Nearest Neighbors (kNN), Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), Random Forest (RF) and AdaBoost (ADA), or an ensemble of some of them. The F-score was employed to select most discriminative features so that the dimension of features was reduced. The results showed that the RF achieved the highest accuracy of 74.1% in the case of multi-class classification. In the case of binary classification, the best performance (Accuracy 86.39%) was achieved by the RF classifier in terms of average accuracy across all possible pairs of the letters. In addition, we employed decision fusion strategy to exert complementary strengths of different classifiers. The results demonstrated that the performance was elevated from 74.10% to 76.63% for the multi-class classification and from 86.39% to 88.08% for the binary class classification.
  • Smart product-service systems in interoperable logistics: Design and
           implementation prospects
    • Abstract: Publication date: October 2019Source: Advanced Engineering Informatics, Volume 42Author(s): Shenle Pan, Ray Y. Zhong, Ting QuAbstractTo deal with the increasing complexity of customer demands, supply chain (SC) and logistics organisation and management have been constantly moving towards collaboration, intelligence, and service-orientation. The importance of service-oriented design for SC and logistics systems has been stressed, especially with regards to interoperability and sustainability. In this context, the recent intelligent interoperable logistics paradigm has been increasingly studied and the Smart Product-Service System (PSS) concept seems interesting for the paradigm. Smart PSS are characterised by their ability to collect and process information autonomously and subsequently make decisions and self-act/evolve. Interested in the potential for tackling complex logistics systems, this paper investigates how smart PSS could be considered and designed for service-oriented, intelligent interoperable logistics. A recent breakthrough logistics paradigm called the Physical Internet (PI) is taken as a practical example in this research. We present and discuss key design issues and innovative business models associated with smart PSS in PI. The results clearly indicate the promising potential of smart PSS in PI and the need for further research. Consequently, new research avenues leading to a new era of intelligent interoperable logistics are outlined. This paper intends to contribute to two main areas of research: the design and implementation of smart PSS in PI, and functional and conceptual research on PI and intelligent interoperable logistics.
  • High-quality as-is 3D thermal modeling in MEP systems using a deep
           convolutional network
    • Abstract: Publication date: October 2019Source: Advanced Engineering Informatics, Volume 42Author(s): Hyojoo Son, Changwan Kim, Hyunchul ChoiAbstractWith the growing need for automated condition monitoring and analysis in existing buildings, significant effort has been spent on the development of three-dimensional (3D) thermal models. However, little attention has been paid to ensuring the quality of these 3D thermal models, which can directly impact the accuracy of condition monitoring and analysis results. This study aims to propose a method to generate a high-quality 3D thermal model for mechanical, electrical, and plumbing (MEP) systems by bridging the quality discrepancy between high-resolution laser scan data and low-resolution thermal images using a deep convolutional neural network. The proposed method consists of two main parts: (1) improving the resolution of thermal images based on a deep convolutional network and (2) generating a high-quality 3D thermal model by mapping improved thermal images. The performance of the thermal image resolution improvement was validated using a dataset consisting of 312 thermal images. The results demonstrated that the quality of the improved thermal images based on a deep convolutional network was higher than conventional bicubic interpolation in terms of root mean square error (RMSE), peak signal-to-noise ratio (PSNR), and structural similarity (SSIM). Qualitative analysis of a 3D thermal model utilizing the resolution-improved thermal images was also conducted. This was further qualitatively analyzed to have resulted in improved overall quality of the 3D thermal model. The ability to generate a high-quality 3D thermal model can help auditors to perform automated condition monitoring and analysis in buildings based on objective and accurate data.
  • Unsupervised extraction of patterns and trends within highway systems
           condition attributes data
    • Abstract: Publication date: October 2019Source: Advanced Engineering Informatics, Volume 42Author(s): Leslie Titus-GloverHighway agencies combine expert opinions and basic regression modeling techniques to process vast amounts of time series condition attributes data to define highway network health. The health rating exhibit high variability and lack adequate detail for executive-level maintenance planning and resource allocation. This paper presents a new methodology for data abstraction, analysis, and clustering for pattern recognition of highway network health. The methodology describes mathematical and statistical data abstraction algorithms for data preprocessing (smoothening (unweighted moving average), scaling (normalization), and weights derivation (entropy) to compute a composite health index (CHI)), and salient features extraction. Data analysis involved cluster analysis to identify patterns in asset current health and future outlook. The outcome is a characterization of highway network health for executive-level decision making. The algorithms included in this methodology have been successfully applied in the fields of biology, finance, econometrics, bioinformatics, marketing, and social science for pattern recognition. The accuracy of the new methodology is illustrated with an experiment using 463 in-service pavement assets and internal/external metrics (including the degree to which methodology performance classification outcomes conform to national expert opinion). The results from the experiment confirm an accurate and computationally inexpensive methodology, which provides outcomes that compare to real-world pavement condition rating metrics.
  • Smart robotic mobile fulfillment system with dynamic conflict-free
           strategies considering cyber-physical integration
    • Abstract: Publication date: October 2019Source: Advanced Engineering Informatics, Volume 42Author(s): C.K.M. Lee, Bingbing Lin, K.K.H. Ng, Yaqiong Lv, W.C. TaiAbstractSmart mobile robots are deployed to the warehouse environments to improve the efficiency, because of its characteristics of high automation and flexibility characteristics. However, the trajectory planning is a great challenge especially when a number of mobile robotics operates in the warehouse simultaneously. This paper proposes a cyber-physical system model for smart robotic warehouse to implement the workflow data collection and procedure monitor. A decoupled method is presented to find a conflict-free path for the mobile vehicles in the warehouses, after distributing destinations to mobile robots to minimize the total travel distance. The improved A* algorithm is applied to find paths from the source node to the destination node for single mobile vehicle in the domain of smart logistics. Collisions are detected by comparing the occupying time window of each mobile vehicle. Three collision avoidance strategies are developed to solve the conflicts and the candidate path with the minimal completion time is selected as the final determined route. The contribution of the paper is to propose a CPS-enabled robotic warehouse with dynamic conflict-free strategy to self-configure the path to optimize warehouse operation efficiency.
  • Cloud based cyber-physical systems: Network evaluation study
    • Abstract: Publication date: October 2019Source: Advanced Engineering Informatics, Volume 42Author(s): Erik Kajati, Peter Papcun, Chao Liu, Ray Y. Zhong, Jiri Koziorek, Iveta ZolotovaAbstractIn recent years, the Industry 4.0 concept brings new demands and trends in different areas; one of them is distributing computational power to the cloud. This concept also introduced the Reference Architectural Model for Industry 4.0 (RAMI 4.0). The efficiency of data communications within the RAMI 4.0 model is a critical issue. Aiming to evaluate the efficiency of data communication in the Cloud Based Cyber-Physical Systems (CB-CPS), this study analyzes the periods and data amount required to communicate with individual hierarchy levels of the RAMI 4.0 model. The evaluation of the network properties of the communication protocols eligible for CB-CPS is presented. The network properties to different cloud providers and data centers’ locations have been measured and interpreted. To test the findings, an architecture for cloud control of laboratory model was proposed. It was found that the time of the day; the day of the week; and data center utilization have a negligible impact on latency. The most significant impact lies in the data center distance and the speed of the communication channel. Moreover, the communication protocol also has impact on the latency. The feasibility of controlling each level of RAMI 4.0 through cloud services was investigated. Experimental results showed that control is possible in many solutions, but these solutions mostly cannot depend just on cloud services. The intelligence on the edge of the network will play a significant role. The main contribution is a thorough evaluation of different cloud providers, locations, and communication protocols to provide recommendations sufficient for different levels of the RAMI 4.0 architecture.
  • Construction automation and robotics for high-rise buildings over the past
           decades: A comprehensive review
    • Abstract: Publication date: October 2019Source: Advanced Engineering Informatics, Volume 42Author(s): Shiyao Cai, Zhiliang Ma, Miroslaw J. Skibniewski, Song BaoAbstractAutomation and robotics technology is expected to improve the productivity of the construction industry as well as to solve problems such as labor shortage and safety risks, especially for high-rise buildings. Substantial research efforts have been devoted to the field over the past decades, while the application rate at the construction sites is still limited. Although various reviews have summarized the research topics and future trends in this field, few research efforts have been made on a consideration of both academic research and practical application in the industry. Focusing on high-rise building construction, this study explores the development of both academic research and practical application of automation and robotics based on literature and market review. Scientometric and critical literature reviews were conducted to identify and analyze the development of key research areas based on academic publications from the 1980s to present. In the meantime, the development of basic technologies was summarized. The market review surveyed on existing products and developers of construction automation and robotics. By comparing the results of the literature review and market review, four development patterns of academic research and product application were identified, i.e., simultaneous development led by the same party, development at a similar pace with the two sides taking the lead in different aspects, academic research providing basic technologies for product development, and available technologies in academic research with no products found. Then three gaps in this field, i.e., the gap between academic research and products, the gap between products and application, and the gap between the construction industry and the robotics industry, were discussed with corresponding suggestions to narrow the gaps, followed by an outlook for future directions. This study contributes to the knowledge body by identifying and analyzing the key research areas and the development gaps systematically.
  • Proactive mental fatigue detection of traffic control operators using
           bagged trees and gaze-bin analysis
    • Abstract: Publication date: October 2019Source: Advanced Engineering Informatics, Volume 42Author(s): Fan Li, Chun-Hsien Chen, Gangyan Xu, Li Pheng Khoo, Yisi LiuAbstractMost of existing eye movement-based fatigue detectors utilize statistical analysis of fixations, saccades, and blinks as inputs. Nevertheless, these parameters require long recording time and heavily depend on eye trackers. In an effort to facilitate proactive detection of mental fatigue, we introduced a complemental fatigue indicator, named gaze-bin analysis, which simply presents the eye-tracking data with histograms. A method which engaged the gaze-bin analysis as inputs of semisupervised bagged trees was developed. A case study in a vessel traffic service center demonstrated that this approach can alleviate the burden of manual labeling as well as improve the performance of fatigue detection model. In addition, the results show that the approach can achieve an excellent accuracy of 89%, which outperformed other methods. In general, this study provided a complemental indicator for detecting mental fatigue as well as enabled the application of a low sampling rate eye tracker in the traffic control center.
  • A framework for brain learning-based control of smart structures
    • Abstract: Publication date: October 2019Source: Advanced Engineering Informatics, Volume 42Author(s): Hamid Radmard Rahmani, Geoffrey Chase, Marco Wiering, Carsten KönkeAbstractA novel framework for intelligent structural control is proposed using reinforcement learning. In this approach, a deep neural network learns how to improve structural responses using feedback control. The effectiveness of the framework is demonstrated in a case study for a moment frame subjected to earthquake excitations. The performance of the learning method was improved by proposing a state-selector function that prevented the neural network from forgetting key states. Results show that the controller significantly improves structural responses not only to earthquake records on which it was trained but also to earthquake records new to the controller. The controller also has stable performance under environmental uncertainties. This capability distinguishes the proposed approach and makes it more appropriate for the situations in which it is likely that the controller will be exposed to unpredictable external excitations and high degrees of uncertainties.
  • Template-based configuration and execution of decision workflows in design
           of complex engineered systems
    • Abstract: Publication date: October 2019Source: Advanced Engineering Informatics, Volume 42Author(s): Zhenjun Ming, Gehendra Sharma, Janet K. Allen, Farrokh MistreeAbstractThe research reported in the paper is from a decision-based design perspective wherein the principal role (but not only) role of a designer is to make decisions. Decision workflows are the processes by which the solutions pertaining to the design of complex systems are generated. Decision workflows are core to design processes, in which a set of decisions are connected (or interconnected) to generate shared and desired design outputs. Careful configuration of decision workflows is very important to ensure the generation of designs using available resources. Configuration of decision workflows is a process that requires a designer to use the basic elements to compose feasible workflows and then select an appropriate one for implementation in designing a product or a system. In this paper, we propose a template-based method for the design and execution of decision workflows associated with designing engineered systems. The value of the method is anchored in that it facilitates designers rapidly planning the processes, namely, the decision workflows, for designing products or systems. Moreover, due to the fact that these decision workflows are modeled in a computational manner, designers are able to execute decision workflows to explore the solution space and identify satisficing design solutions in early design stages. A gearbox with connected gears and shafts is a typical complex engineered system that can be partitioned into multiple levels of interacting subsystems. We illustrate the method and the decision workflows using a gear and shaft (within a gearbox) design example.
  • Edge-cloud orchestration driven industrial smart product-service systems
           solution design based on CPS and IIoT
    • Abstract: Publication date: October 2019Source: Advanced Engineering Informatics, Volume 42Author(s): Bufan Liu, Yingfeng Zhang, Geng Zhang, Pai ZhengAbstractThe rapid booming of advanced information and communication technologies (ICT) has promoted an encouraging smart, connected product (SCP) market that further triggers the development of manufacturing towards the servitization proposition, viz. smart product-service systems (PSS). Smart PSS aims to provide a solution (product-service) with high satisfaction and less environmental influence by leveraging SCP as the media tool. Its solution design should not just focus on the physical world nor only be enabled by the cloud side, while the cyber world and the edge side must be included in the Industry 4.0. However, only few current researches investigate about the smart PSS, let alone an overall cyber-physical and edge-cloud discussion to support its solution design. In order to fill this gap, this work proposes an edge-cloud orchestration driven solution design based on the cyber-physical systems (CPS) and industrial Internet of Things (IIoT). To make our ideas concrete, a real-life milling process was conducted as an illustrative example. It is hoped that this study can furnish industrial enterprises with meaningful sights in the process of servitization and value co-creation.
  • A novel data-driven graph-based requirement elicitation framework in the
           smart product-service system context
    • Abstract: Publication date: October 2019Source: Advanced Engineering Informatics, Volume 42Author(s): Zuoxu Wang, Chun-Hsien Chen, Pai Zheng, Xinyu Li, Li Pheng KhooAbstractNowadays, industrial companies are facing ever-increasing challenges to generate new value-in-use and maintain their high competitiveness in the market. With the rapid development of Information and Communication Technology (ICT), IT is embedded in the products themselves, i.e. smart, connected products (SCPs) to generate values. Hence, an emerging value proposition paradigm, smart product-service system (Smart PSS) was introduced, by leveraging both SCPs and its generated services as a solution bundle to meet individual customer needs. Unlike other types of PSS, in Smart PSS, massive user-generated data and product-sensed data are collected during the usage phase, where potential requirements can be elicited readily in a value co-creation manner with context-awareness. Nevertheless, only few scholars discuss any systematic manner to support requirement elicitation in such context. To fill the gaps, this research proposes a novel data-driven graph-based requirement elicitation framework in the Smart PSS, so as to assist engineering/designers make better design improvement or new design concept generation in a closed-loop manner. It underlines the informatics-based approach by integrating heterogeneous data sources into a holistic consideration. Moreover, by leveraging graph-based approach, context-product-service information can be linked by the edges and nodes in-between them to derive reliable requirements. To validate its feasibility and advantages, an illustrative example of smart bicycle design improvement is further adopted. As an explorative study, it is hoped that this work provides useful insights for the requirement elicitation process in today’s smart connected environment.
  • Deep convolutional learning for general early design stage prediction
    • Abstract: Publication date: October 2019Source: Advanced Engineering Informatics, Volume 42Author(s): Sundaravelpandian Singaravel, Johan Suykens, Philipp GeyerAbstractDesigners rely on performance predictions to direct the design toward appropriate requirements. Machine learning (ML) models exhibit the potential for rapid and accurate predictions. Developing conventional ML models that can be generalized well in unseen design cases requires an effective feature engineering and selection. Identifying generalizable features calls for good domain knowledge by the ML model developer. Therefore, developing ML models for all design performance parameters with conventional ML will be a time-consuming and expensive process. Automation in terms of feature engineering and selection will accelerate the use of ML models in design.Deep learning models extract features from data, which aid in model generalization. In this study, we (1) evaluate the deep learning model’s capability to predict the heating and cooling demand on unseen design cases and (2) obtain an understanding of extracted features. Results indicate that deep learning model generalization is similar to or better than that of a simple neural network with appropriate features. The reason for the satisfactory generalization using the deep learning model is its ability to identify similar design options within the data distribution. The results also indicate that deep learning models can filter out irrelevant features, reducing the need for feature selection.
  • Recognizing people’s identity in construction sites with computer
           vision: A spatial and temporal attention pooling network
    • Abstract: Publication date: October 2019Source: Advanced Engineering Informatics, Volume 42Author(s): Ran Wei, Peter E.D. Love, Weili Fang, Hanbin Luo, Shuangjie XuAbstractSeveral prototype vision-based approaches have been developed to capture and recognize unsafe behavior in construction automatically. Vision-based approaches have been difficult to use due to their inability to identify individuals who commit unsafe acts when captured using digital images/video. To address this problem, we applied a novel deep learning approach that utilizes a Spatial and Temporal Attention Pooling Network to remove redundant information contained in a video to enable a person’s identity to be automatically determined. The deep learning approach we have adopted focuses on: (1) extracting spatial feature maps using the spatial attention network; (2) extracting temporal information using the temporal attention networks; and (3) recognizing a person’s identity by computing the distance between features. To validate the feasibility and effectiveness of the adopted deep learning approach, we created a database of videos that contained people performing their work on construction sites, conducted an experiment, and then performed k-fold cross-validation. The results demonstrated that the approach could accurately identify a person’s identity from videos captured from construction sites. We suggest that our computer-vision approach can potentially be used by site managers to automatically recognize those individuals that engage in unsafe behavior and therefore be used to provide instantaneous feedback about their actions and possible consequences.
  • A user-centric smart product-service system development approach: A case
           study on medication management for the elderly
    • Abstract: Publication date: October 2019Source: Advanced Engineering Informatics, Volume 42Author(s): Danni Chang, Zhenyu Gu, Fan Li, Rong JiangAbstractWith the advancement in Internet implementations, computational intelligence and network technologies, smart product-service system (SPSS) has become an important research area. A lot of research effort has been devoted to construct the conceptual framework, identify the important elements and evaluate the effectiveness of SPSS. However, there is still no SPSS development approach from user-centric perspective. Therefore, this article aims to provide a novel understanding of user-centric SPSS (UC-SPSS), outline the conceptual framework of UC-SPSS and contribute a UC-SPSS development approach. Specifically, a multimodal user analysis module with S-E-T (society-economy-technology) analysis, user behavioral analysis and user segmentation is deployed. According to the user needs identified, a provider identification and integration network is established in the dimensions of material, data and value flows. Jointly considering the user needs and provider capability, the BCE (benefit-cost-expectation) model and Product Function Architecture are applied to assist in the realization of the smart, connected service. To illustrate, a UC-SPSS on medication management for the elderly was developed, and it has been evaluated from user experience and sustainable value aspects. The results showed that the developed medication service is interesting and helpful for the elderly to take their medication. However, the service is not simple enough, especially in data visualization. In terms of sustainable value, the developed service can achieve better performance in economic, material and energy costs, and can support the further regulation of medical industry. Based on the case illustration, the proposed approach appears effective to help with SPSS development.
  • Agent evaluation based on multi-source heterogeneous information table
           using TOPSIS
    • Abstract: Publication date: October 2019Source: Advanced Engineering Informatics, Volume 42Author(s): Libo Zhang, Tianxing Wang, Huaxiong Li, Bing Huang, Xianzhong ZhouAbstractIn agent evaluation, a specific role-playing may need more than one capabilities or the task execution process can be divided into several stages. The diverse perspectives to assess candidate agents are denoted as attributes, which is more practical than treating experts as attributes in many other works. In the evaluation table, the attribute values may come from different sources and the data types may not be the same. Therefore, we consider evaluation issues in a Multi-Source Heterogeneous Information System (MSHIS). Considering that grading, voting and marking are three common evaluation scenarios, linguistic variable, Intuitionistic Fuzzy Value (IFV) and real number are utilized to describe the corresponding evaluation results. To evaluate agents in MSHIS, a TOPSIS-based evaluation method is adopted in this work. In the proposed method, the range is utilized to nondimensionalize the distance between agents in each attribute. Then, a weighted Euclidean distance metric is adopted to measure the comprehensive distance. The relative closeness to the ideal agents reflects the agent’s capability on the concerned task. Finally, the illustrative example and comparative experiments are presented to illustrate the effectiveness of our method.
  • A survey of smart product-service systems: Key aspects, challenges and
           future perspectives
    • Abstract: Publication date: October 2019Source: Advanced Engineering Informatics, Volume 42Author(s): Pai Zheng, Zuoxu Wang, Chun-Hsien Chen, Li Pheng KhooAbstractThe rapid development of information and communication technologies (ICT) has enabled the prevailing digital transformation (i.e. digitalization), where physical products can be readily digitized in the virtual space and seamlessly interconnected. Meanwhile, industries are ever increasingly adopting service business models (i.e. servitization), so as to offer not only physical products but also services as a solution bundle to meet individual customer needs. Such convergence of both digitalization and servitization (i.e. digital servitization) has triggered an emerging IT-driven business paradigm, smart product-service systems (Smart PSS). As a novel paradigm coined in 2014, to the authors’ knowledge, only 2 conference papers have provided some literature review to date, and many issues remain uncovered or not comprehensively investigated. Aiming to fill this gap, this paper has conducted a systematic review of Smart PSS or related papers published ever since its first brought up to date (30/06/2019), and selected 97 representative items together with other 37 supplementary works to summarize the tendency towards Smart PSS, its business and technical aspects, current challenges, and future perspectives. From the survey, it is found that several hybrid concerns are the key challenges faced, and self-adaptiveness with sustainability, advanced IT infrastructure, human-centric perspectives, and circular lifecycle management are the core future perspectives to explore. It is hoped that this work can attract more open discussions and provide useful insights to both academics and industries in their exploration and implementation of Smart PSS.
  • Three-dimensional (3D) reconstruction of structures and landscapes: A new
           point-and-line fusion method
    • Abstract: Publication date: October 2019Source: Advanced Engineering Informatics, Volume 42Author(s): Ying Zhou, Lingling Wang, Peter E.D. Love, Lieyun Ding, Cheng ZhouAbstractThe technique of three-dimensional (3D) reconstruction is widely used to develop infrastructure and landscape models to manage cities and assets better. Accurately reconstructing 3D structures (e.g., planes or lines) is a core step in rebuilding a model, especially within a built environment, where piece-wise planar/linear structures predominately prevail. As high-resolution images of large areas have become increasingly accessible, this paper develops an improved 3D reconstruction pipeline using the combination of point and line features. By introducing a dense reconstruction algorithm, which is an improved patch based stereo matching algorithm, this paper presents a robust approach that can be used to overcome the inaccuracies, integrity and reconstruction inefficiencies associated with point clouds. A 3D line extraction method is added to reconstruct accurate edges of buildings. The experimental results demonstrate that the proposed method visually improves the reconstruction effect of a 3D structure and a model's visualization.
  • A service-oriented multi-player maintenance grouping strategy for complex
           multi-component system based on game theory
    • Abstract: Publication date: October 2019Source: Advanced Engineering Informatics, Volume 42Author(s): Fengtian Chang, Guanghui Zhou, Wei Cheng, Chao Zhang, Changle TianAbstractThe development of smart product service system (PSS) urges the emergence of service-oriented maintenance grouping strategy for complex multi-component system. This strategy is designed from the data-driven performance-based service manner where the proactive original equipment manufacturer (OEM) and service providers are both involved. In this situation, the traditional maintenance grouping methods are incapable to determine the optimal grouping service time for each exactor due to the little consideration from their interaction relations in the grouping process. Thus, this paper proposes one OEM and multiple service provider’s multi-player maintenance grouping strategy. It is constructed from the multi-objective Stackelberg-Nash game model where the OEM is the upper-level leader and all the involved service providers are the lower-level followers. The serviced components, service time and paid prices are considered firstly by the leader. After that, the followers could compete to select the remaining serviced components and service time. In order to obtain the Stackelberg-Nash equilibrium solution, the bi-level nested parallel solution algorithm with the improved multi-fitness functions is also developed. Finally, a numerical example from wind turbine is studied. The evaluation and comparison results show that our method could provide a feasible and effective maintenance grouping strategy for each player under smart PSS.
  • Intelligent fault diagnosis for rotating machinery using deep Q-network
    • Abstract: Publication date: October 2019Source: Advanced Engineering Informatics, Volume 42Author(s): Yu Ding, Liang Ma, Jian Ma, Mingliang Suo, Laifa Tao, Yujie Cheng, Chen LuAbstractFault diagnosis methods for rotating machinery have always been a hot research topic, and artificial intelligence-based approaches have attracted increasing attention from both researchers and engineers. Among those related studies and methods, artificial neural networks, especially deep learning-based methods, are widely used to extract fault features or classify fault features obtained by other signal processing techniques. Although such methods could solve the fault diagnosis problems of rotating machinery, there are still two deficiencies. (1) Unable to establish direct linear or non-linear mapping between raw data and the corresponding fault modes, the performance of such fault diagnosis methods highly depends on the quality of the extracted features. (2) The optimization of neural network architecture and parameters, especially for deep neural networks, requires considerable manual modification and expert experience, which limits the applicability and generalization of such methods. As a remarkable breakthrough in artificial intelligence, AlphaGo, a representative achievement of deep reinforcement learning, provides inspiration and direction for the aforementioned shortcomings. Combining the advantages of deep learning and reinforcement learning, deep reinforcement learning is able to build an end-to-end fault diagnosis architecture that can directly map raw fault data to the corresponding fault modes. Thus, based on deep reinforcement learning, a novel intelligent diagnosis method is proposed that is able to overcome the shortcomings of the aforementioned diagnosis methods. Validation tests of the proposed method are carried out using datasets of two types of rotating machinery, rolling bearings and hydraulic pumps, which contain a large number of measured raw vibration signals under different health states and working conditions. The diagnosis results show that the proposed method is able to obtain intelligent fault diagnosis agents that can mine the relationships between the raw vibration signals and fault modes autonomously and effectively. Considering that the learning process of the proposed method depends only on the replayed memories of the agent and the overall rewards, which represent much weaker feedback than that obtained by the supervised learning-based method, the proposed method is promising in establishing a general fault diagnosis architecture for rotating machinery.
  • Spatial prediction of shallow landslide using Bat algorithm optimized
           machine learning approach: A case study in Lang Son Province, Vietnam
    • Abstract: Publication date: October 2019Source: Advanced Engineering Informatics, Volume 42Author(s): Dieu Tien Bui, Nhat-Duc Hoang, Hieu Nguyen, Xuan-Linh TranThis study develops a machine learning method that hybridizes the Least Squares Support Vector Classification (LSSVC) and Bat Algorithm (BA), named as BA-LSSVC, for spatial prediction of shallow landslide. To construct and verify the hybrid method, a Geographic Information System (GIS) database for the study area of Lang Son province (Vietnam) has been employed. LSSVC is used to separate data samples in the GIS database into two categories of non-landslide (negative class) and landslide (positive class). The BA metaheuristic is employed to assist the LSSVC model selection process by fine-tuning its hyper-parameters: the regularization coefficient and the kernel function parameter. Experimental results point out that the hybrid BA-LSSVC can help to achieve a desired prediction with an accuracy rate of more than 90%. The performance of BA-LSSVC is also better than those of benchmark methods, including the Convolutional Neural Network, Relevance Vector Machine, Artificial Neural Network, and Logistic Regression. Hence, the newly developed model is a capable tool to assist local authority in landslide hazard mitigation and management.Graphical abstractGraphical abstract for this article
  • Estimation of the degree of hydration of concrete through automated
           machine learning based microstructure analysis – A study on effect of
           image magnification
    • Abstract: Publication date: October 2019Source: Advanced Engineering Informatics, Volume 42Author(s): Srikanth Sagar Bangaru, Chao Wang, Marwa Hassan, Hyun Woo Jeon, Tarun AyiluriAbstractThe scanning electron microscopy (SEM) images are commonly used to understand the microstructure of the concrete. With the advancements in the field of computer vision, many researchers have adopted the image processing technique for the microstructure analysis. Most of the previous methods are not adaptable, non-reproducible, semi-automated, and most importantly all these methods are highly influenced by image magnification. Therefore, to overcome these challenges, this paper presents a machine learning based image segmentation method for microstructure analysis and degree of hydration measurement using SEM images. In addition, the authors looked into the impact of magnification of SEM images on the model accuracy and classifier training for the degree of hydration measurement considering two scenarios. First, the image segmentation was performed using a classifier of specific magnification, and then a common classifier is trained using the image of different magnification. The results show that the Random Forest classifier algorithm is suitable for microstructure analysis using SEM images. Through the statistical analysis, it has been proved that there is no significant effect of magnification on model training and accuracy for the degree of hydration measurement. So, a single classifier can be used to process the images of different magnification of a specimen which reduces the effort of training and computational time. The proposed method can generate highly accurate and reliable results in a shorter time and lower cost. Moreover, the findings in this research can be useful for researchers to determine the optimum magnification required for the microstructure analysis.
  • Detecting, locating, and characterizing voids in disaster rubble for
           search and rescue
    • Abstract: Publication date: October 2019Source: Advanced Engineering Informatics, Volume 42Author(s): Da Hu, Shuai Li, Junjie Chen, Vineet R. KamatAbstractAfter natural and man-made disasters such as earthquakes, hurricanes, and explosions, victims may survive in voids that are formed naturally in collapsed structures. First responders need to identify and locate these critical voids for rapid search and rescue operations. Due to the complex and unstructured occlusions in disaster areas, visual and manual search is time-consuming and error-prone. In this study, we proposed a novel method to automatically detect, locate, and characterize voids buried in disaster rubble using ground penetrating radar (GPR). After preprocessing the collected radargrams, the boundaries of potential voids are segmented based on radar signal patterns, and the 95% confidence intervals are constructed around the segmented boundaries to account for uncertainties. To improve the detection accuracy, the geometric relations of the detected boundaries and their signal characteristics are examined to confirm the void existence. Then, the void location and dimension are estimated based on calibrated velocity of radar wave and its travel time. The effectiveness and efficiency of the proposed method were manifested by its performance in laboratory and field experiments. The contribution of this study is twofold. First, the feasibility of using GPR to detect, locate, and characterize voids in collapsed structures is experimentally tested, innovatively extending the application of GPR to search and rescue operations. Second, algorithms are developed to process non-intuitive radargrams to provide first responders actionable information.
  • A methodological framework with rough-entropy-ELECTRE TRI to classify
           failure modes for co-implementation of smart PSS
    • Abstract: Publication date: October 2019Source: Advanced Engineering Informatics, Volume 42Author(s): Zhiwen Liu, Xinguo MingAbstractSmart PSS (product-service system) has been transcending the scope of traditional PSS through ICTs (information and communication technologies). The co-implementation is a specific way of co-creation to generate value for customer and supplier in smart PSS. Meanwhile, the service failures in co-implementation can bring about service paradox. As such, three key research questions, including the construction of co-implementation of smart PSS, the extension of risk factors in FMECA and the category of identified failure modes, are put forward and conquered by a proposed methodological framework. First, in order to construct the co-implementation of smart PSS involving customer and supplier, the PCN (process-chain-network) diagram with direct interaction, surrogate interaction and independent handling is employed to reflect the nature of interaction. Second, since that the highly complexity of co-implementation of smart PSS can lead to more huge risk, an extended-FMECA (failure mode effect and criticality analysis) sheet is formulated by considering the adaptation of autonomy. Third, to handle the uncertainty of expert judgments more reasonably and determine the weights of risk factors without additional information, a hybrid approach integrating rough-entropy-ELECTRE TRI is proposed to classify failure modes into ordinal classes, so that decision-makers can swiftly access them. This proposed methodological framework also is illustrated by the co-implementation of smart fridge-service system, in which the feasibility and superiority are verified by analysing comparative results.
  • An augmented self-adaptive parameter control in evolutionary computation:
           A case study for the berth scheduling problem
    • Abstract: Publication date: October 2019Source: Advanced Engineering Informatics, Volume 42Author(s): Masoud Kavoosi, Maxim A. Dulebenets, Olumide F. Abioye, Junayed Pasha, Hui Wang, Hongmei ChiAbstractThe demand for international seaborne trade has substantially increased over the last three decades and is predicted to continue increasing during the upcoming years. A marine container terminal, as an important node in supply chains, should be able to successfully cope with increasing demand volumes. Berth scheduling can significantly influence the general throughput of marine container terminals. In this study, a mixed-integer linear programming mathematical model is proposed for the berth scheduling problem, aiming to minimize the summation of waiting costs, handling costs, and late departure costs of the vessels that are to be served at a marine container terminal. An innovative Evolutionary Algorithm is designed to solve the developed mathematical model. The proposed solution algorithm relies on the augmented self-adaptive parameter control strategy, which is developed in order to effectively change the algorithmic parameters throughout the search process. Performance of the designed algorithm is evaluated against nine alternative state-of-the-art metaheuristic-based algorithms, which have been frequently used for berth scheduling in the marine container terminal operations literature. The results demonstrate that all the developed algorithms have a high level of stability and return competitive solutions at convergence. The computational experiments also prove superiority of the designed augmented self-adaptive Evolutionary Algorithm over the alternative algorithms in terms of different performance indicators.
  • Risk-aware supply chain intelligence: AI-enabled supply chain and
           logistics management considering risk mitigation
    • Abstract: Publication date: October 2019Source: Advanced Engineering Informatics, Volume 42Author(s): Wei Yan, Junliang He, Amy J.C. Trappey
  • Dynamic modelling of customer preferences for product design using DENFIS
           and opinion mining
    • Abstract: Publication date: October 2019Source: Advanced Engineering Informatics, Volume 42Author(s): Huimin Jiang, C.K. Kwong, G.E. Okudan Kremer, W.-Y. ParkAbstractPrevious studies mainly employed customer surveys to collect survey data for understanding customer preferences on products and developing customer preference models. In reality, customer preferences on products could change over time. Thus, the time series data of customer preferences under different time periods should be collected for the modelling of customer preferences. However, it is difficult to obtain the time series data based on customer surveys because of long survey time and substantial resources involved. In recent years, a large number of online customer reviews of products can be found on various websites, from which the time series data of customer preferences can be extracted easily. Some previous studies have attempted to analyse customer preferences on products based on online customer reviews. However, two issues were not addressed in previous studies which are the fuzziness of the sentiment expressed by customers existing in online reviews and the modelling of customer preferences based on the time series data obtained from online reviews. In this paper, a new methodology for dynamic modelling of customer preferences based on online customer reviews is proposed to address the two issues which mainly involves opinion mining and dynamic evolving neural-fuzzy inference system (DENFIS). Opinion mining is adopted to analyze online reviews and perform sentiment analysis on the reviews under different time periods. With the mined time series data and the product attribute settings of reviewed products, a DENFIS approach is introduced to perform the dynamic modelling of customer preferences. A case study is used to illustrate the proposed methodology. The results of validation tests indicate that the proposed DENFIS approach outperforms various adaptive neuro-fuzzy inference system (ANFIS) approaches in the dynamic modelling of customer preferences in terms of the mean relative error and variance of errors. In addition, the proposed DENFIS approach can provide both crisp and fuzzy outputs that cannot be realized by using existing ANFIS and conventional DENFIS approaches.
  • Differential received signal strength based RFID positioning for
           construction equipment tracking
    • Abstract: Publication date: October 2019Source: Advanced Engineering Informatics, Volume 42Author(s): Changzhi Wu, Xiangyu Wang, Mengcheng Chen, Mi Jeong KimAbstractA novel differential received signal strength (RSS) positioning algorithm is proposed in the paper. Different from traditional methods to find the relationship between RSS and distance, this new positioning approach is based on linear regression of angle and differential received signal strengths. The advantages of this positioning algorithm is robust to heterogeneity of RFID tags as well as direction between tag and reader. Several experiments are firstly carried out in open environment and then a further experiment is conducted in a LNG training centre to validate our proposed algorithm. The results show that our proposed algorithm can achieve better accuracy than existing RFID positioning approaches for equipment tracking.
  • BIM reconstruction from 3D point clouds: A semantic registration approach
           based on multimodal optimization and architectural design knowledge
    • Abstract: Publication date: October 2019Source: Advanced Engineering Informatics, Volume 42Author(s): Fan Xue, Weisheng Lu, Ke Chen, Christopher J. WebsterAbstractReconstructing semantically rich building information model (BIM) from 2D images or 3D point clouds represents a research realm that is gaining increasing popularity in architecture, engineering, and construction. Researchers have found that architectural design knowledge, such as symmetry, planarity, parallelism, and orthogonality, can be utilized to improve the effectiveness of such BIM reconstruction. Following this line of enquiry, this paper aims to develop a novel semantic registration approach for complicated scenes with repetitive, irregular-shaped objects. The approach first formulates the architectural repetition as the multimodality in mathematics. Thus, the reconstruction of repetitive objects becomes a multimodal optimization (MMO) problem of registering BIM components which have accurate geometries and rich semantics. Then, the topological information about repetition and symmetry in the reconstructed BIM is recognized and regularized for BIM semantic enrichment. A university lecture hall case, consisting of 1.9 million noisy points of 293 chairs, was selected for an experiment to validate the proposed approach. Experimental results showed that a BIM was satisfactorily created (achieving about 90% precision and recall) automatically in 926.6 s; and an even more satisfactory BIM achieved 99.3% precision and 98.0% recall with detected semantic and topological information under the minimal effort of human intervention in 228.4 s. The multimodality model of repetitive objects, the repetition detection and regularization for BIM, and satisfactory reconstruction results in the presented approach can contribute to methodologies and practices in multiple disciplines related to BIM and smart city.
  • Onsite video mining for construction hazards identification with visual
    • Abstract: Publication date: October 2019Source: Advanced Engineering Informatics, Volume 42Author(s): Ruoxin Xiong, Yuanbin Song, Heng Li, Yuxuan WangAbstractWidely-used video monitoring systems provide a large corpus of unstructured image data on construction sites. Although previous developed vision-based approaches can be used for hazards recognition in terms of detecting dangerous objects or unsafe operations, such detection capacity is often limited due to lack of semantic representation of visual relationships between/among the components or crews in the workplace. Accordingly, the formal representation of textural criteria for checking improper relationships should also be improved. In this regard, an Automated Hazards Identification System (AHIS) is developed to evaluate the operation descriptions generated from site videos against the safety guidelines extracted from the textual documents with the assistance of the ontology of construction safety. In particular, visual relationships are modeled as a connector between site components/operators. Moreover, both visual descriptions of site operations and semantic representations of safety guidelines are coded in the three-tuple format and then automatically converted into Horn clauses for reasoning out the potential risks. A preliminary implementation of the system was tested on two separate onsite video clips. The results showed that two types of crucial hazards, i.e., failure to wear a helmet and walking beneath the cane, were successfully identified with three rules from Safety Handbook for Construction Site Workers. In addition, the high-performance results of Recall@50 and Recall@100 demonstrated that the proposed visual relationship detection method is promising in enriching the semantic representation of operation facts extracted from site videos, which may lead to better automation in the detection of construction hazards.
  • A genuine smile is indeed in the eyes – The computer aided non-invasive
    • Abstract: Publication date: October 2019Source: Advanced Engineering Informatics, Volume 42Author(s): Hassan Ugail, Ahmad Al-dahoudAbstractUnderstanding the detailed differences between posed and spontaneous smiles is an important topic with a range of applications such as in human-computer interaction, automatic facial emotion analysis and in awareness systems. During the past decade or so, there have been very promising solutions for accurate automatic recognition and detailed facial emotion analysis. To this end, many methods and techniques have been proposed for distinguishing between spontaneous and posed smiles. Our aim here is to go beyond the present state of the art in this field. Hence, in this work, we are concerned with understanding the exact distribution of a smile – both spontaneous and posed – across the face. To do this, we utilise a lightweight computational framework which we have developed to analyse the dynamics of human facial expressions. We utilise this framework to undertake a detailed study of the smile expression. Based on computing the optical flow across the face – especially across key parts of the face such as the mouth, the cheeks and around the eyes – we are able to accurately map the dynamic weight distribution of the smile expression. To validate our computational model, we utilise two publicly available datasets, namely the CK + dataset in which the subjects express posed smiles and the MUG dataset in which the subjects express genuine smiles. Our results not only confirm what already exists in the literature – i.e. that the spontaneous genuine smile is truly in the eyes – but it also gives further insight into the exact distribution of the smile across the face.
  • Automatic generation of fabrication drawings for façade mullions and
           transoms through BIM models
    • Abstract: Publication date: October 2019Source: Advanced Engineering Informatics, Volume 42Author(s): Min Deng, Vincent J.L. Gan, Yi Tan, Ajay Joneja, Jack C.P. ChengAbstractFabrication drawings are essential for manufacturing, design evaluation and inspection of building components, especially for building façade structural components. In order to clearly represent the physical characteristics of the façade structural components, a large number of section views need to be produced, which is very time-consuming and labor intensive. Therefore, automatic generation of fabrication drawings for building façade components (such as mullions and transoms) is of paramount importance. In this paper, attempts have been made to develop an efficient framework in order to automatically generate fabrication drawings for building façade structural components, including mullions and transoms. To represent the complex physical characteristics (such as holes and notches) on mullions and transoms using minimum number of drawing views, a computational algorithm based on graph theory is developed to eliminate duplicated section views. Another methodology regarding the generation of breaks for top views is also proposed to further improve the quality of drawing layouts. The obtained drawing views are then automatically arranged using a developed approach. In addition, primary dimensions of the drawing views focusing on the physical features are also generated. Furthermore, in order to maintain the consistency of drawing formats across multiple drawings, a methodology is proposed to determine the scaling factors of the drawings by using clustering technique. In an illustrative example, the proposed framework is used to generate the fabrication drawings for a typical BIM model containing façade structural components, and saving in time is observed.
  • State of the art in big data applications in microgrid: A review
    • Abstract: Publication date: October 2019Source: Advanced Engineering Informatics, Volume 42Author(s): Karim MoharmAbstractThe prospering Big data era is emerging in the power grid. Multiple world-wide studies are emphasizing the big data applications in the microgrid due to the huge amount of produced data. Big data analytics can impact the design and applications towards safer, better, more profitable, and effective power grid. This paper presents the recognition and challenges of the big data and the microgrid. The construction of big data analytics is introduced. The data sources, big data opportunities, and enhancement areas in the microgrid like stability improvement, asset management, renewable energy prediction, and decision-making support are summarized. Diverse case studies are presented including different planning, operation control, decision making, load forecasting, data attacks detection, and maintenance aspects of the microgrid. Finally, the open challenges of big data in the microgrid are discussed.
  • 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 LiAbstract3D 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. ChanAbstractDesign 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 ShapiroAbstractBehaviors 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 LuoWith 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. TrappeyAbstractAn 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 EmamiAbstractEmploying 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 ZhaoAbstractWith 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
    • Abstract: Publication date: October 2019Source: Advanced Engineering Informatics, Volume 42Author(s): Juite Wang, Yi-Jing ChenAbstractEarly 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 LouisAbstractAutomated, 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 YuAbstractInteractions 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 WangAbstractThis 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 ZhengAbstractConvolutional 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 GarzaAbstractInfrastructure 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 BorsatoAbstractCurrent 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 HuangAbstractComplex 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 KhooAbstractVigilance 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.
  • 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 LiConstraints 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 MengAbstractThe 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 HuAbstractVision-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
    • Abstract: Publication date: October 2019Source: Advanced Engineering Informatics, Volume 42Author(s): Abdul Mujeeb, Wenting Dai, Marius Erdt, Alexei SourinAbstractDetecting 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 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. HoggAbstractDesign 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.
School of Mathematical and Computer Sciences
Heriot-Watt University
Edinburgh, EH14 4AS, UK
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