for Journals by Title or ISSN
for Articles by Keywords
help

Publisher: Elsevier   (Total: 3161 journals)

 A  B  C  D  E  F  G  H  I  J  K  L  M  N  O  P  Q  R  S  T  U  V  W  X  Y  Z  

        1 2 3 4 5 6 7 8 | Last   [Sort by number of followers]   [Restore default list]

Showing 1 - 200 of 3161 Journals sorted alphabetically
A Practical Logic of Cognitive Systems     Full-text available via subscription   (Followers: 9)
AASRI Procedia     Open Access   (Followers: 15)
Academic Pediatrics     Hybrid Journal   (Followers: 33, SJR: 1.655, CiteScore: 2)
Academic Radiology     Hybrid Journal   (Followers: 23, SJR: 1.015, CiteScore: 2)
Accident Analysis & Prevention     Partially Free   (Followers: 95, SJR: 1.462, CiteScore: 3)
Accounting Forum     Hybrid Journal   (Followers: 25, SJR: 0.932, CiteScore: 2)
Accounting, Organizations and Society     Hybrid Journal   (Followers: 35, SJR: 1.771, CiteScore: 3)
Achievements in the Life Sciences     Open Access   (Followers: 5)
Acta Anaesthesiologica Taiwanica     Open Access   (Followers: 7)
Acta Astronautica     Hybrid Journal   (Followers: 410, SJR: 0.758, CiteScore: 2)
Acta Automatica Sinica     Full-text available via subscription   (Followers: 2)
Acta Biomaterialia     Hybrid Journal   (Followers: 27, SJR: 1.967, CiteScore: 7)
Acta Colombiana de Cuidado Intensivo     Full-text available via subscription   (Followers: 2)
Acta de Investigación Psicológica     Open Access   (Followers: 3)
Acta Ecologica Sinica     Open Access   (Followers: 10, SJR: 0.18, CiteScore: 1)
Acta Haematologica Polonica     Free   (Followers: 1, SJR: 0.128, CiteScore: 0)
Acta Histochemica     Hybrid Journal   (Followers: 3, SJR: 0.661, CiteScore: 2)
Acta Materialia     Hybrid Journal   (Followers: 250, SJR: 3.263, CiteScore: 6)
Acta Mathematica Scientia     Full-text available via subscription   (Followers: 5, SJR: 0.504, CiteScore: 1)
Acta Mechanica Solida Sinica     Full-text available via subscription   (Followers: 9, SJR: 0.542, CiteScore: 1)
Acta Oecologica     Hybrid Journal   (Followers: 12, SJR: 0.834, CiteScore: 2)
Acta Otorrinolaringologica (English Edition)     Full-text available via subscription  
Acta Otorrinolaringológica Española     Full-text available via subscription   (Followers: 2, SJR: 0.307, CiteScore: 0)
Acta Pharmaceutica Sinica B     Open Access   (Followers: 1, SJR: 1.793, CiteScore: 6)
Acta Poética     Open Access   (Followers: 4, SJR: 0.101, CiteScore: 0)
Acta Psychologica     Hybrid Journal   (Followers: 27, SJR: 1.331, CiteScore: 2)
Acta Sociológica     Open Access   (Followers: 1)
Acta Tropica     Hybrid Journal   (Followers: 6, SJR: 1.052, CiteScore: 2)
Acta Urológica Portuguesa     Open Access  
Actas Dermo-Sifiliograficas     Full-text available via subscription   (Followers: 3, SJR: 0.374, CiteScore: 1)
Actas Dermo-Sifiliográficas (English Edition)     Full-text available via subscription   (Followers: 2)
Actas Urológicas Españolas     Full-text available via subscription   (Followers: 3, SJR: 0.344, CiteScore: 1)
Actas Urológicas Españolas (English Edition)     Full-text available via subscription   (Followers: 1)
Actualites Pharmaceutiques     Full-text available via subscription   (Followers: 6, SJR: 0.19, CiteScore: 0)
Actualites Pharmaceutiques Hospitalieres     Full-text available via subscription   (Followers: 3)
Acupuncture and Related Therapies     Hybrid Journal   (Followers: 6)
Acute Pain     Full-text available via subscription   (Followers: 14, SJR: 2.671, CiteScore: 5)
Ad Hoc Networks     Hybrid Journal   (Followers: 11, SJR: 0.53, CiteScore: 4)
Addictive Behaviors     Hybrid Journal   (Followers: 16, SJR: 1.29, CiteScore: 3)
Addictive Behaviors Reports     Open Access   (Followers: 8, SJR: 0.755, CiteScore: 2)
Additive Manufacturing     Hybrid Journal   (Followers: 9, SJR: 2.611, CiteScore: 8)
Additives for Polymers     Full-text available via subscription   (Followers: 22)
Advanced Drug Delivery Reviews     Hybrid Journal   (Followers: 148, SJR: 4.09, CiteScore: 13)
Advanced Engineering Informatics     Hybrid Journal   (Followers: 11, SJR: 1.167, CiteScore: 4)
Advanced Powder Technology     Hybrid Journal   (Followers: 16, SJR: 0.694, CiteScore: 3)
Advances in Accounting     Hybrid Journal   (Followers: 8, SJR: 0.277, CiteScore: 1)
Advances in Agronomy     Full-text available via subscription   (Followers: 12, SJR: 2.384, CiteScore: 5)
Advances in Anesthesia     Full-text available via subscription   (Followers: 28, SJR: 0.126, CiteScore: 0)
Advances in Antiviral Drug Design     Full-text available via subscription   (Followers: 2)
Advances in Applied Mathematics     Full-text available via subscription   (Followers: 10, SJR: 0.992, CiteScore: 1)
Advances in Applied Mechanics     Full-text available via subscription   (Followers: 11, SJR: 1.551, CiteScore: 4)
Advances in Applied Microbiology     Full-text available via subscription   (Followers: 22, SJR: 2.089, CiteScore: 5)
Advances In Atomic, Molecular, and Optical Physics     Full-text available via subscription   (Followers: 14, SJR: 0.572, CiteScore: 2)
Advances in Biological Regulation     Hybrid Journal   (Followers: 4, SJR: 2.61, CiteScore: 7)
Advances in Botanical Research     Full-text available via subscription   (Followers: 2, SJR: 0.686, CiteScore: 2)
Advances in Cancer Research     Full-text available via subscription   (Followers: 32, SJR: 3.043, CiteScore: 6)
Advances in Carbohydrate Chemistry and Biochemistry     Full-text available via subscription   (Followers: 8, 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: 3)
Advances in Cellular and Molecular Biology of Membranes and Organelles     Full-text available via subscription   (Followers: 12)
Advances in Chemical Engineering     Full-text available via subscription   (Followers: 27, SJR: 0.156, CiteScore: 1)
Advances in Child Development and Behavior     Full-text available via subscription   (Followers: 10, SJR: 0.713, CiteScore: 1)
Advances in Chronic Kidney Disease     Full-text available via subscription   (Followers: 10, SJR: 1.316, CiteScore: 2)
Advances in Clinical Chemistry     Full-text available via subscription   (Followers: 29, SJR: 1.562, CiteScore: 3)
Advances in Colloid and Interface Science     Full-text available via subscription   (Followers: 19, SJR: 1.977, CiteScore: 8)
Advances in Computers     Full-text available via subscription   (Followers: 14, SJR: 0.205, CiteScore: 1)
Advances in Dermatology     Full-text available via subscription   (Followers: 15)
Advances in Developmental Biology     Full-text available via subscription   (Followers: 11)
Advances in Digestive Medicine     Open Access   (Followers: 9)
Advances in DNA Sequence-Specific Agents     Full-text available via subscription   (Followers: 5)
Advances in Drug Research     Full-text available via subscription   (Followers: 24)
Advances in Ecological Research     Full-text available via subscription   (Followers: 44, SJR: 2.524, CiteScore: 4)
Advances in Engineering Software     Hybrid Journal   (Followers: 28, SJR: 1.159, CiteScore: 4)
Advances in Experimental Biology     Full-text available via subscription   (Followers: 7)
Advances in Experimental Social Psychology     Full-text available via subscription   (Followers: 44, SJR: 5.39, CiteScore: 8)
Advances in Exploration Geophysics     Full-text available via subscription   (Followers: 1)
Advances in Fluorine Science     Full-text available via subscription   (Followers: 9)
Advances in Food and Nutrition Research     Full-text available via subscription   (Followers: 57, SJR: 0.591, CiteScore: 2)
Advances in Fuel Cells     Full-text available via subscription   (Followers: 16)
Advances in Genetics     Full-text available via subscription   (Followers: 16, SJR: 1.354, CiteScore: 4)
Advances in Genome Biology     Full-text available via subscription   (Followers: 8, SJR: 12.74, CiteScore: 13)
Advances in Geophysics     Full-text available via subscription   (Followers: 6, SJR: 1.193, CiteScore: 3)
Advances in Heat Transfer     Full-text available via subscription   (Followers: 21, SJR: 0.368, CiteScore: 1)
Advances in Heterocyclic Chemistry     Full-text available via subscription   (Followers: 12, SJR: 0.749, CiteScore: 3)
Advances in Human Factors/Ergonomics     Full-text available via subscription   (Followers: 22)
Advances in Imaging and Electron Physics     Full-text available via subscription   (Followers: 2, SJR: 0.193, CiteScore: 0)
Advances in Immunology     Full-text available via subscription   (Followers: 36, SJR: 4.433, CiteScore: 6)
Advances in Inorganic Chemistry     Full-text available via subscription   (Followers: 8, SJR: 1.163, CiteScore: 2)
Advances in Insect Physiology     Full-text available via subscription   (Followers: 2, SJR: 1.938, CiteScore: 3)
Advances in Integrative Medicine     Hybrid Journal   (Followers: 6, SJR: 0.176, CiteScore: 0)
Advances in Intl. Accounting     Full-text available via subscription   (Followers: 3)
Advances in Life Course Research     Hybrid Journal   (Followers: 8, SJR: 0.682, CiteScore: 2)
Advances in Lipobiology     Full-text available via subscription   (Followers: 1)
Advances in Magnetic and Optical Resonance     Full-text available via subscription   (Followers: 9)
Advances in Marine Biology     Full-text available via subscription   (Followers: 16, SJR: 0.88, CiteScore: 2)
Advances in Mathematics     Full-text available via subscription   (Followers: 11, SJR: 3.027, CiteScore: 2)
Advances in Medical Sciences     Hybrid Journal   (Followers: 6, SJR: 0.694, CiteScore: 2)
Advances in Medicinal Chemistry     Full-text available via subscription   (Followers: 5)
Advances in Microbial Physiology     Full-text available via subscription   (Followers: 4, SJR: 1.158, CiteScore: 3)
Advances in Molecular and Cell Biology     Full-text available via subscription   (Followers: 21)
Advances in Molecular and Cellular Endocrinology     Full-text available via subscription   (Followers: 8)
Advances in Molecular Toxicology     Full-text available via subscription   (Followers: 7, SJR: 0.182, CiteScore: 0)
Advances in Nanoporous Materials     Full-text available via subscription   (Followers: 3)
Advances in Oncobiology     Full-text available via subscription   (Followers: 1)
Advances in Organ Biology     Full-text available via subscription   (Followers: 1)
Advances in Organometallic Chemistry     Full-text available via subscription   (Followers: 17, SJR: 1.875, CiteScore: 4)
Advances in Parallel Computing     Full-text available via subscription   (Followers: 7, SJR: 0.174, CiteScore: 0)
Advances in Parasitology     Full-text available via subscription   (Followers: 5, SJR: 1.579, CiteScore: 4)
Advances in Pediatrics     Full-text available via subscription   (Followers: 24, SJR: 0.461, CiteScore: 1)
Advances in Pharmaceutical Sciences     Full-text available via subscription   (Followers: 10)
Advances in Pharmacology     Full-text available via subscription   (Followers: 16, SJR: 1.536, CiteScore: 3)
Advances in Physical Organic Chemistry     Full-text available via subscription   (Followers: 8, SJR: 0.574, CiteScore: 1)
Advances in Phytomedicine     Full-text available via subscription  
Advances in Planar Lipid Bilayers and Liposomes     Full-text available via subscription   (Followers: 3, SJR: 0.109, CiteScore: 1)
Advances in Plant Biochemistry and Molecular Biology     Full-text available via subscription   (Followers: 9)
Advances in Plant Pathology     Full-text available via subscription   (Followers: 5)
Advances in Porous Media     Full-text available via subscription   (Followers: 5)
Advances in Protein Chemistry     Full-text available via subscription   (Followers: 18)
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: 62)
Advances in Quantum Chemistry     Full-text available via subscription   (Followers: 6, SJR: 0.371, CiteScore: 1)
Advances in Radiation Oncology     Open Access   (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: 5)
Advances in Space Research     Full-text available via subscription   (Followers: 398, SJR: 0.569, CiteScore: 2)
Advances in Structural Biology     Full-text available via subscription   (Followers: 5)
Advances in Surgery     Full-text available via subscription   (Followers: 10, SJR: 0.555, CiteScore: 2)
Advances in the Study of Behavior     Full-text available via subscription   (Followers: 31, SJR: 2.208, CiteScore: 4)
Advances in Veterinary Medicine     Full-text available via subscription   (Followers: 17)
Advances in Veterinary Science and Comparative Medicine     Full-text available via subscription   (Followers: 13)
Advances in Virus Research     Full-text available via subscription   (Followers: 5, SJR: 2.262, CiteScore: 5)
Advances in Water Resources     Hybrid Journal   (Followers: 47, SJR: 1.551, CiteScore: 3)
Aeolian Research     Hybrid Journal   (Followers: 6, SJR: 1.117, CiteScore: 3)
Aerospace Science and Technology     Hybrid Journal   (Followers: 343, SJR: 0.796, CiteScore: 3)
AEU - Intl. J. of Electronics and Communications     Hybrid Journal   (Followers: 8, SJR: 0.42, CiteScore: 2)
African J. of Emergency Medicine     Open Access   (Followers: 6, SJR: 0.296, CiteScore: 0)
Ageing Research Reviews     Hybrid Journal   (Followers: 11, SJR: 3.671, CiteScore: 9)
Aggression and Violent Behavior     Hybrid Journal   (Followers: 445, SJR: 1.238, CiteScore: 3)
Agri Gene     Hybrid Journal   (Followers: 1, SJR: 0.13, CiteScore: 0)
Agricultural and Forest Meteorology     Hybrid Journal   (Followers: 17, SJR: 1.818, CiteScore: 5)
Agricultural Systems     Hybrid Journal   (Followers: 31, SJR: 1.156, CiteScore: 4)
Agricultural Water Management     Hybrid Journal   (Followers: 43, SJR: 1.272, CiteScore: 3)
Agriculture and Agricultural Science Procedia     Open Access   (Followers: 2)
Agriculture and Natural Resources     Open Access   (Followers: 3)
Agriculture, Ecosystems & Environment     Hybrid Journal   (Followers: 57, SJR: 1.747, CiteScore: 4)
Ain Shams Engineering J.     Open Access   (Followers: 5, SJR: 0.589, CiteScore: 3)
Air Medical J.     Hybrid Journal   (Followers: 6, SJR: 0.26, CiteScore: 0)
AKCE Intl. J. of Graphs and Combinatorics     Open Access   (SJR: 0.19, CiteScore: 0)
Alcohol     Hybrid Journal   (Followers: 11, SJR: 1.153, CiteScore: 3)
Alcoholism and Drug Addiction     Open Access   (Followers: 9)
Alergologia Polska : Polish J. of Allergology     Full-text available via subscription   (Followers: 1)
Alexandria Engineering J.     Open Access   (Followers: 1, SJR: 0.604, CiteScore: 3)
Alexandria J. of Medicine     Open Access   (Followers: 1, SJR: 0.191, CiteScore: 1)
Algal Research     Partially Free   (Followers: 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: 9, SJR: 0.201, CiteScore: 1)
Alzheimer's & Dementia     Hybrid Journal   (Followers: 50, SJR: 4.66, CiteScore: 10)
Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring     Open Access   (Followers: 4, SJR: 1.796, CiteScore: 4)
Alzheimer's & Dementia: Translational Research & Clinical Interventions     Open Access   (Followers: 4, SJR: 1.108, CiteScore: 3)
Ambulatory Pediatrics     Hybrid Journal   (Followers: 6)
American Heart J.     Hybrid Journal   (Followers: 50, SJR: 3.267, CiteScore: 4)
American J. of Cardiology     Hybrid Journal   (Followers: 54, SJR: 1.93, CiteScore: 3)
American J. of Emergency Medicine     Hybrid Journal   (Followers: 45, SJR: 0.604, CiteScore: 1)
American J. of Geriatric Pharmacotherapy     Full-text available via subscription   (Followers: 10)
American J. of Geriatric Psychiatry     Hybrid Journal   (Followers: 14, SJR: 1.524, CiteScore: 3)
American J. of Human Genetics     Hybrid Journal   (Followers: 34, SJR: 7.45, CiteScore: 8)
American J. of Infection Control     Hybrid Journal   (Followers: 28, SJR: 1.062, CiteScore: 2)
American J. of Kidney Diseases     Hybrid Journal   (Followers: 34, SJR: 2.973, CiteScore: 4)
American J. of Medicine     Hybrid Journal   (Followers: 46)
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: 205, SJR: 2.7, CiteScore: 4)
American J. of Ophthalmology     Hybrid Journal   (Followers: 62, 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: 27, SJR: 2.139, CiteScore: 4)
American J. of Preventive Medicine     Hybrid Journal   (Followers: 28, SJR: 2.164, CiteScore: 4)
American J. of Surgery     Hybrid Journal   (Followers: 38, SJR: 1.141, CiteScore: 2)
American J. of the Medical Sciences     Hybrid Journal   (Followers: 12, SJR: 0.767, CiteScore: 1)
Ampersand : An Intl. J. of General and Applied Linguistics     Open Access   (Followers: 6)
Anaerobe     Hybrid Journal   (Followers: 4, SJR: 1.144, CiteScore: 3)
Anaesthesia & Intensive Care Medicine     Full-text available via subscription   (Followers: 62, SJR: 0.138, CiteScore: 0)
Anaesthesia Critical Care & Pain Medicine     Full-text available via subscription   (Followers: 17, SJR: 0.411, CiteScore: 1)
Anales de Cirugia Vascular     Full-text available via subscription  
Anales de Pediatría     Full-text available via subscription   (Followers: 3, SJR: 0.277, CiteScore: 0)
Anales de Pediatría (English Edition)     Full-text available via subscription  
Anales de Pediatría Continuada     Full-text available via subscription  
Analytic Methods in Accident Research     Hybrid Journal   (Followers: 5, SJR: 4.849, CiteScore: 10)
Analytica Chimica Acta     Hybrid Journal   (Followers: 43, SJR: 1.512, CiteScore: 5)
Analytical Biochemistry     Hybrid Journal   (Followers: 176, SJR: 0.633, CiteScore: 2)
Analytical Chemistry Research     Open Access   (Followers: 11, SJR: 0.411, CiteScore: 2)
Analytical Spectroscopy Library     Full-text available via subscription   (Followers: 11)
Anesthésie & Réanimation     Full-text available via subscription   (Followers: 2)
Anesthesiology Clinics     Full-text available via subscription   (Followers: 23, SJR: 0.683, CiteScore: 2)
Angiología     Full-text available via subscription   (SJR: 0.121, CiteScore: 0)
Angiologia e Cirurgia Vascular     Open Access   (Followers: 1, SJR: 0.111, CiteScore: 0)
Animal Behaviour     Hybrid Journal   (Followers: 190, SJR: 1.58, CiteScore: 3)

        1 2 3 4 5 6 7 8 | Last   [Sort by number of followers]   [Restore default list]

Journal Cover
Advanced Engineering Informatics
Journal Prestige (SJR): 1.167
Citation Impact (citeScore): 4
Number of Followers: 11  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1474-0346
Published by Elsevier Homepage  [3161 journals]
  • Visual analysis of asphalt pavement for detection and localization of
           potholes
    • Abstract: Publication date: October 2018Source: Advanced Engineering Informatics, Volume 38Author(s): Muhammad Haroon Yousaf, Kanza Azhar, Fiza Murtaza, Fawad Hussain Identifying and restoring distresses in asphalt pavement have key significance in durability and long life of roads and highways. A vast number of accidents occurs on the roads and highways due to the pavement distresses. This paper aims to detect and localize one of the critical roadway distresses, the potholes, using computer vision. We have processed images of asphalt pavement for experimentation containing the pothole and non-pothole regions. We proposed a top-down scheme for the detection and localization of potholes in the pavement images. First, we classified pothole/non-pothole images using a bag of words (BoW) approach. We employed and computed famous scale-invariant feature transform (SIFT) features to establish the visual vocabulary of words to represent pavement surface. Support vector machine (SVM) is employed for the training and testing of histograms of words of pavement images. Secondly, we proposed graph cut segmentation scheme to localize the potholes in the labelled pothole images. This paper presents both, subjective and objective evaluation of potholes localization results with the ground truth. We evaluated the proposed scheme on a pavement surface dataset containing the wide-ranging pavement images in different scenarios. Experimentation results show that we achieved an accuracy of 95.7% for the identification of pothole images with significant precision and recall. Subjective evaluation of potholes localization results in high recall with relatively good accuracy. However, the objective assessment shows the 91.4% accuracy for localization of potholes.
       
  • Automated ergonomic risk monitoring using body-mounted sensors and machine
           learning
    • Abstract: Publication date: October 2018Source: Advanced Engineering Informatics, Volume 38Author(s): Nipun D. Nath, Theodora Chaspari, Amir H. Behzadan Workers in various industries are often subject to challenging physical motions that may lead to work-related musculoskeletal disorders (WMSDs). To prevent WMSDs, health and safety organizations have established rules and guidelines that regulate duration and frequency of labor-intensive activities. In this paper, a methodology is introduced to unobtrusively evaluate the ergonomic risk levels caused by overexertion. This is achieved by collecting time-stamped motion data from body-mounted smartphones (i.e., accelerometer, linear accelerometer, and gyroscope signals), automatically detecting workers’ activities through a classification framework, and estimating activity duration and frequency information. This study also investigates various data acquisition and processing settings (e.g., smartphone’s position, calibration, window size, and feature types) through a leave-one-subject-out cross-validation framework. Results indicate that signals collected from arm-mounted smartphone device, when calibrated, can yield accuracy up to 90.2% in the considered 3-class classification task. Further post-processing the output of activity classification yields very accurate estimation of the corresponding ergonomic risk levels. This work contributes to the body of knowledge by expanding the current state in workplace health assessment by designing and testing ubiquitous wearable technology to improve the timeliness and quality of ergonomic-related data collection and analysis.
       
  • Game-based crowdsourcing to support collaborative customization of the
           definition of sustainability
    • Abstract: Publication date: October 2018Source: Advanced Engineering Informatics, Volume 38Author(s): Mazdak Nik Bakht, Tamer E. El-Diraby, Moein Hossaini Successful adoption and management of sustainable urban systems hinges on the community embracing these systems. Capturing citizens’ ideas, views, and assessments of the built environment will be essential to this goal. In collaborative city planning, these are qualified and valued forms of partial knowledge that should be collectively used to shape the decision making process of urban planning. Among other tools, social media and online social network analytics can provide means to capture elements of such a distributed knowledge. While a structured definition of sustainability (normally dictated in a top-down fashion) may not sufficiently respond well to the pluralist nature of such knowledge acquisition; dealing with the unstructured community inputs, assessments and contributions on social media can be confusing. We can detect fully relevant topics/ideas in community discussions; but they typically suffer from lack of coherence.In this paper, we advocate the use of a semi-structured approach for capturing, analyzing, and interpreting citizens’ inputs. Public officials and professionals can develop the main elements (topical aspects) of sustainability, which can act as the skeleton of a taxonomy. It is however, the community inputs/ideas (in our case collected via social media and parsed), that can shape-up that skeleton and augment those topical aspects with adding the required semantic depth. In more specific terms, we collected tweets for four urban infrastructure mega-projects in North America. Then we used a game-with-a-purpose to crowdsource the identification of topics for a training set of tweets. This was then used to train machine learning algorithms to cluster the rest of collected tweets. We studied the semantic (finding the topics) of tweets as well as their sentiment (in terms of being opposing or supportive of a project). Our classification tested different decision trees with different topic hierarchies. We considered/extracted eight different linguistic features in studying contents of a tweet. Finally, we examined the accuracy of three algorithms in classifying tweets according to the sequence in the tree, and based on the extracted features. These are: K-nearest neighbors, Naïve Bayes classifiers and Support Vector Machines (SVM).Respective to our data set, SVM outperformed other algorithms. Semantic analysis was insensitive to the depth/number of linguistic features considered. In contrast, sentiment analysis was enhanced when part of speech (PoS) was tracked. Interestingly, our work shows that considering the topic (semantic) of a tweet helped enhance the accuracy of sentiment analysis: including topical class as a feature in conducting sentiment analysis results in higher accuracies. This could be used as means to detect the evolution of community opinion: that topic-based social networks are evolving within the communities tweeting about urban projects. It could also be used to identify the topics of top priority to the community or the ones that have the widest spread of views. In our case, these were mainly the impacts of the design and engineering features on social issues.
       
  • Semi-automated site equipment selection and configuration through formal
           knowledge representation and inference
    • Abstract: Publication date: October 2018Source: Advanced Engineering Informatics, Volume 38Author(s): Katrin Jahr, André Borrmann The selection and configuration of site equipment is a fundamental part of construction preparation. Suitable site equipment supports the timely, cost-efficient and qualitative execution of the construction process. The use of planning tools based on formal knowledge management methods can both speed up the process of construction site planning and lead to better results. In this paper, we propose a rule-based knowledge inference system to support site equipment planners in a semi-automated manner using input data from building information models and working schedules. The knowledge-based system is built using the business rule management system Drools. Using a sample construction site, the feasibility of the proposed approach has been proven.
       
  • Reconstruction of edges in digital building models
    • Abstract: Publication date: October 2018Source: Advanced Engineering Informatics, Volume 38Author(s): Wolfgang Huhnt Traditionally, the geometry of building components or rooms is either described by their boundary or it is defined by constructive solid models. Modeling tools are available which are based on the principle of constructing building components and of composing a building by adding building components step by step. However, topological relations are relevant besides the shape of components and rooms. These topological relationships are not necessarily explicit information in boundary representation or constructive solid models. As a consequence, they must be computed. At present time, there exists to the best knowledge of the author only a single approach in this subject area that is able to reconstruct topological relations including their geometry. The objects which need to be considered are building components, built-in components and rooms. The challenge is to calculate the three relevant aspects of geometry in digital building models completely and in an efficient way. These three relevant aspects are clashes, voids and contact faces. The existing approach to calculate these aspects is based on space partitioning concepts. Space partitioning concepts store neighboring relations explicitly. The approach presented in this paper is also based on space partitioning. One basic and novel consideration of the approach presented in this paper is to execute the reconstruction procedure in a mesh. The mesh itself is not refined anymore at a certain point during the calculations to avoid uncontrollable refinements. The second basic and novel consideration is the way of avoiding topological inconsistencies. Integer values are chosen for coordinates, and a specific algorithm is presented that guarantees that topological inconsistencies cannot occur. The research presented in this paper addresses the first step on the way to compute clashes, voids and contact faces. This is the reconstruction of edges. This paper presents the theory and a pilot implementation for the reconstruction of straight edges. Examples show the benefits of the approach presented. Open questions are discussed.
       
  • Overlay Design Methodology for virtual environment design within digital
           games
    • Abstract: Publication date: October 2018Source: Advanced Engineering Informatics, Volume 38Author(s): Ikhwan Kim, SukJoo Hong, Ji-Hyun Lee, Jean-Charles Bazin Virtual landscapes in digital games have become more expressive and impressive. However, design methodologies that can efficiently implement them have rarely been developed. In real-world landscape architecture, a design methodology called Overlay Design Methodology has been commonly utilized that allows efficient spatial design by computing the hierarchy and the placement of the environmental resources. In this paper, we investigate how to apply Overlay Design Methodology for the creation of virtual environments within digital gaming contexts. Along with the establishment of the design methodology, we measure the effectiveness of the methodology with protocol analysis and survey to 30 game developers. As we observed, the Overlay Design Methodology doubled the collaboration among team members and reduced unnecessary time in the design process by over 98%.
       
  • Knowledge-driven intelligent quality problem-solving system in the
           automotive industry
    • Abstract: Publication date: October 2018Source: Advanced Engineering Informatics, Volume 38Author(s): Zhaoguang Xu, Yanzhong Dang, Peter Munro In the current automotive industry, quality management, especially quality problem-solving (QPS), plays an important role in fulfilling the expectations of demanding customers who seek high-quality products at low-cost. During the problem-solving process, various real-time and historical quality data are often not fully used, yet these data are of high value. This paper provides a comprehensive quality data mining process and method, as well as an intelligent quality problem-solving system (IQPSS). First, based on original quality problem data, an ontology library is constructed using the ontology generating module (OGM). Second, based on the generated ontology and the textual data of the original quality problem, this study builds a quality problem-solving knowledge base (QPSKB) by employing relevant algorithms in the knowledge transformation module (KTM). The component and fault relational matrix mining (CFRMM) algorithm is designed to extract the relationship matrix between the components and faults. The semi-supervised classification algorithm based on the K-nearest neighbor algorithm (KNN) is used to classify the immediate measures, causes and long-term measures into the corresponding ontology and express the ontology as their knowledge. Furthermore, the binary tree-based support vector machine (SVM) approach is applied to classify the cause texts into the factors of Man, Machine, Material, Method, and Environment (4M1E), which are the five factors in a fishbone diagram. In particular, the digital fishbone diagram is a brand-new type of fishbone diagram that subverts the traditional method of fishbone diagram analysis through brainstorming. A pilot run of the IQPSS has been undertaken in an automotive manufacturing company to demonstrate how quality management employees obtain this knowledge by searching in the IQPSS. The results show that the IQPSS contributes appreciably to the quality problem-solving in the manufacturing industry.
       
  • A comprehensive review on identification of the geomaterial constitutive
           model using the computational intelligence method
    • Abstract: Publication date: October 2018Source: Advanced Engineering Informatics, Volume 38Author(s): Wei Gao It is crucial to determine geomaterial constitutive models to analyze the mechanical behavior of geomaterials and geotechnical engineering stability. Thus, identification of a geomaterial constitutive model is a very important aspect of back analysis. Because the real mechanical behavior of geomaterials are very complicated, it is difficult to identify a suitable geomaterial constitutive model based on traditional methods. Therefore, some computational intelligence methods have been used to solve this problem, and many related studies have been performed. In this study, previous research is reviewed according to the following four aspects: constitutive model approach via an artificial neural network, constitutive model description via an artificial neural network, constitutive model selection via an evolutionary computation, and constitutive model construction via an evolutionary computation. Moreover, the state-of-the-art research advancement of the four research aspects is summarized. The merits and demerits of these research aspects have been comprehensively analyzed and discussed. Finally, possible research directions to identify a geomaterial constitutive model based on computational intelligence are also provided.
       
  • Quantifying the physical intensity of construction workers, a mechanical
           energy approach
    • Abstract: Publication date: October 2018Source: Advanced Engineering Informatics, Volume 38Author(s): Liulin Kong, Heng Li, Yantao Yu, Hanbin Luo, Martin Skitmore, Maxwell Fordjour Antwi-Afari Construction workers typically undertake highly demanding physical tasks involving various types of stresses from awkward postures, using excessive force, highly repetitive actions, and excessive energy expenditure, which increases the likelihood of unsafe actions, productivity loss, and human errors. Biomechanical models have been developed to estimate joint loadings, which can help avoid strenuous physical exertion, potentially enhancing construction workforce productivity, safety, and well-being. However, the models used are mainly in 2D, or to predict static strength ignored their velocity and acceleration or using marker-based method for dynamic motion data collection. To address this issue, this paper proposes a novel framework for investigating the mechanical energy expenditure (MEE) of workers using a 3D biomechanical model based on computer vision-based techniques. Human 3D Pose Estimation algorithm based on 2D videos is applied to approximate the coordinates of human joints for working postures, and smart insoles are used to collect foot pressures and plantar accelerations, as input data for the biomechanical analyses. The results show a detailed MEE rate for the whole body, at which joints the maximum and minimum values were obtained to avoid excessive physical exertion. The proposed method can approximate the total daily MEE of construction tasks by summing the assumed cost of individual tasks (such as walking, lifting, and stooping), providing suggestions for the design of a daily workload that workers can sustain without developing cumulative fatigue.
       
  • Capacitated closed-loop supply chain network design under uncertainty
    • Abstract: Publication date: October 2018Source: Advanced Engineering Informatics, Volume 38Author(s): Lu Zhen, Yiwei Wu, Shuaian Wang, Yi Hu, Wen Yi This study optimizes the design of a closed-loop supply chain network, which contains forward and reverse directions and is subject to uncertainty in demands for new & returned products. To address uncertainty in decision-making, we formulate a two-stage stochastic mixed-integer non-linear programming model to determine the distribution center locations and their corresponding capacity, and new & returned product flows in the supply chain network to minimize total design and expected operating costs. We convert our model to a conic quadratic programming model given the complexity of our problem. Then, the conic model is added with certain valid inequalities, such as polymatroid inequalities, and extended with respect to its cover cuts so as to improve computational efficiency. Furthermore, a tabu search algorithm is developed for large-scale problem instances. We also study the impact of inventory weight, transportation weight, and marginal value of time of returned products by the sensitivity analysis. Several computational experiments are conducted to validate the effectiveness of the proposed model and valid inequalities.
       
  • Selecting manufacturing partners in push and pull-type smart collaborative
           networks
    • Abstract: Publication date: October 2018Source: Advanced Engineering Informatics, Volume 38Author(s): Marko Mladineo, Stipo Celar, Luka Celent, Marina Crnjac The idea of Collaborative Manufacturing, also known as the Production Networks or Social Manufacturing, has been around for more than 25 years. It is a production concept based on non-hierarchical collaboration among enterprises often referred as Virtual Enterprise (VE). Despite many scientific research and projects with this topic, it is difficult to find an example of fully operational non-hierarchical production network anywhere in the world. However, that fact could be changed very soon. Namely, the new industrial revolution, called Industry 4.0, encourages industrial enterprises to adopt information-communication technology (ICT) and Internet of Things (IoT) into their production systems, thus creating Cyber-Physical Production System (CPPS). From the aspect of production networks, CPPS represents crucial infrastructure, or a missing link between enterprises. Now, with CPPS in place, non-hierarchical networking and collaboration becomes possible through Smart Collaborative Production Networks. In this research, the concept of information system for Smart Collaborative Production Networks was developed and called ‘VENTIS’. Although the idea of the concept is to manage the collaboration inside Virtual Enterprise, in this research, a special focus has been put on manufacturing planning phase in which optimization problem known as the Partner Selection Problem (PSP) occurs. Since the PSP in manufacturing phase is far more complex than partner selection during the collaborative product development phase, new research premises regarding the Virtual Enterprise type have been set. Two types of Virtual Enterprise business models – Push-type and Pull-type – have been defined in this research. If VE is Push-type, HUMANT algorithm is used to solve PSP that occurs in that case. If VE is Pull-type, a special procedure, inspired by phenomenological reduction, has been established in which set of a priori created VEs is compared with theoretically ‘the best’ VE and theoretically ‘the worst’ VE. Enterprises’ data of production network from Dalmatia (Split-Dalmatia County, Croatia) is used as a Case Study to present ‘VENTIS’ concept and to present the procedure for creation of sustainable Virtual Enterprise.
       
  • Modeling of transdisciplinary engineering assets using the design platform
           approach for improved customization ability
    • Abstract: Publication date: October 2018Source: Advanced Engineering Informatics, Volume 38Author(s): Samuel André, Fredrik Elgh Original equipment suppliers (OES) that develop unique products are continuously faced with changing requirements during both the quotation and product development processes. This challenge is a different reality from companies that develop off-the-shelf products for the end consumer, which use fixed specifications and where product platforms have been a strong enabler for efficient mass customization. However, product platforms cannot adequately support companies working as OES. The reason is that a high level of customization is required which means that interfaces cannot be standardized, the performance is not negotiable, requirements are not initially fixed, and the specific system interacts with, is affected by, or affects other systems that are simultaneously developed in a transdisciplinary environment. The design platform (DP) approach provides a coherent environment for heterogeneous and transdisciplinary design resources to be used in product development by supporting both designing and off-the-shelf solutions. This research describes the introduction, application and further development of the DP approach at an automotive supplier to support the development of customized solutions when traditional modularity or platform scalability do not suffice. A computer tool called Design Platform Manager has been developed to support the creation and visualization of the DP. The support tool has a connection to a product data management database to link the platform model to the various kinds of engineering assets needed or intended to support variant creation. Finally, the support tool was evaluated by the case company representatives showing promising results.
       
  • Collaborative engineering decision-making for building information
           channels and improving Web visibility of product manufacturers
    • Abstract: Publication date: October 2018Source: Advanced Engineering Informatics, Volume 38Author(s): Sylvain Sagot, Alain-Jérôme Fougères, Egon Ostrosi Product manufacturers have spent the last years improving productivity and process efficiency in order to face increasingly competitive markets. Today, the visibility of technological innovations has become essential to achieve the targeted market. It is now very difficult for a product manufacturer to reach customers without owning a website that is visible on search engine results pages. The goal of this paper is to build information channels between a company and its customers through improving both a company’s content of information on the Web and its website rank on the Internet through search engine results pages. Company information and knowledge are distributed through multiple stakeholders. The problem of building information channels between a company and customers is solved through a collaborative and distributed approach, on the one hand, and is supported by decision-making tools, on the other hand. The paper proposes an engineering model for building information channels and improving the visibility of the company on the Web. Agents are used for the implementation of the approach. The proposed model and its implementation handle the requirements, constraints, functions and solutions for improving Web visibility. The prototype tool, called CAWIS (Computer Aided Web Information Sharing), examines Web visibility in real time and evaluates the performance of the proposed content of information. CAWIS allows an exploratory and open way for building information channels and improving the visibility of product manufacturers on the Web.
       
  • Supporting connectivism in knowledge based engineering with graph theory,
           filtering techniques and model quality assurance
    • Abstract: Publication date: October 2018Source: Advanced Engineering Informatics, Volume 38Author(s): Joel Johansson, Manuel Contero, Pedro Company, Fredrik Elgh Mass-customization has forced manufacturing companies to put significant efforts to digitize and automate their engineering and production processes. When new products are to be developed and introduced the production is not alone to be automated. The application of knowledge regarding how the product should be designed and produced based on customer requirements also must be automated. One big academic challenge is helping industry to make sure that the background knowledge of the automated engineering processes still can be understood by its stakeholders throughout the product life cycle.The research presented in this paper aims to build an infrastructure to support a connectivistic view on knowledge in knowledge based engineering. Fundamental concepts in connectivism include network formation and contextualization, which are here addressed by using graph theory together with information filtering techniques and quality assurance of CAD-models. The paper shows how engineering knowledge contained in spreadsheets, knowledge-bases and CAD-models can be penetrated and represented as filtered graphs to support a connectivistic working approach. Three software demonstrators developed to extract filtered graphs are presented and discussed in the paper.
       
  • Enhanced predictive modelling process of broadband services adoption based
           on time series data
    • Abstract: Publication date: October 2018Source: Advanced Engineering Informatics, Volume 38Author(s): Višnja Križanović, Drago Žagar, Krešimir Grgić, Mario Vranješ In this paper, the importance of the predictive modelling process of broadband services adoption is described. A detailed overview of different analytical models used for prediction, i.e., fitting and forecasting processes of broadband services adoption are presented. Furthermore, a comparison of several analytical models commonly used for prediction of broadband adoption is conducted. In order to more accurately fit to the existing broadband adoption time series data, and to forecast the future broadband services adoption paths, the features of the most accurate common predictive models have been identified for different phases of broadband services adoption. Considering the given results, usage of additional models in the predictive modelling process is analyzed. The objective of these analyses is set to improve the accuracy of the existing predictive modelling process. The accuracy of the predictive modelling process using additional models is tested and compared in different phases of broadband adoption. The model which gives the most accurate results is identified. Finally, in order to enable the usage of this model within a whole broadband service life cycle, as well as to include a greater number of explanatory parameters in predictive modelling process, an enhanced predictive modelling process is proposed.
       
  • BIM-based modeling and management of design options at early planning
           phases
    • Abstract: Publication date: October 2018Source: Advanced Engineering Informatics, Volume 38Author(s): Hannah Mattern, Markus König At early planning phases, the investigation of possible design options helps to find a suitable design for the client despite complex boundary conditions. In this context, the use of BIM models offers new prospects by representing a valuable input for simulation and analysis tasks. This paper aims at enabling a model-based management of design options. An applicable data model needs to be defined that supports the generation and exchange of design options. The developed concept avoids the use of separate models for explicit designs and thus, prevents the creation of redundant information. Furthermore, managing possible design variants within a single model increases the consistency of the provided information. Option categories to represent design options are introduced to structure the complex possibilities which might evolve when proceeding with the design process. Graph Data Models (GDM) are proposed as a transparent approach to describe and manage the resulting models as they provide a structured overview on affected elements and interdependencies. The results of the developed concept are shown by a case study focusing on high-rise buildings. Following the presented approach, architects, designers, contractors and clients are provided with a transparent description of design options which supports the decision making process from the very beginning.
       
  • Crowdsourcing with online quantitative design analysis
    • Abstract: Publication date: October 2018Source: Advanced Engineering Informatics, Volume 38Author(s): David Birch, Alvise Simondetti, Yi-ke Guo Design is a balancing act between people’s competing concerns, design options and design performance. Recently collecting data on such concerns such as sustainability or aesthetics has become possible through online crowdsourcing, particularly in 3d. However, such systems rarely present more than a single design alternative or allow users to change the design and seldom provide quantitative design analysis to gauge design performance. This precludes a more participatory approach including a wider audience and their insight in the design process.To improve the design process we propose a system to assist the design team in exploring the balance of concerns, design options and their performance. We augment a 3d visualisation crowdsourcing environment with quantitative on-demand assessment of design variants run in the cloud. This enables crowdsourced exploration of the design space and its performance. Automated participant tracking and explicit submitted feedback on design options are collated and presented to aid the design team in balancing the demands of urban master planning. We report application of this system to an urban masterplan with Arup.
       
  • Machine learning and BIM visualization for maintenance issue
           classification and enhanced data collection
    • Abstract: Publication date: October 2018Source: Advanced Engineering Informatics, Volume 38Author(s): J.J. McArthur, Nima Shahbazi, Ricky Fok, Christopher Raghubar, Brandon Bortoluzzi, Aijun An Occupant-generated work orders are recognized as a good potential data to support Facility Management (FM) activities, however they are unstructured and rarely contain the specific information engineers require to resolve the reported issues. Instead, this often requires multiple trips are often needed to identify the required trade, identify the problem and required parts/tools, and resolve. A key challenge is data quality: free-form (unstructured) text is collected that frequently lacks necessary detail for problem diagnosis. Machine Learning provides new opportunities within the FM domain to improve the quality of information collected through online work order reporting systems by automatically classifying WOs and prompting building occupants with appropriate FM team-developed questions in real time to gather the required specific information in structured form. This paper presents the development, comparison, and application of two sets of supervised machine learning models to perform this classification for WOs generated from occupant complaints. A set of ∼150,000 historical WOs was used for model development and textual classification using with various term and itemset frequency approaches was tested. Classifier prediction accuracies ranged from 46.6% to 81.3% for classification by detailed subcategory; this increased to between 68% (simple term frequency) to 90% (random forest) when the dataset only included the ten most common (accounting for 70% of all WOs) subcategories. Hierarchical classification decreased performance. An FM-BIM integration approach is finally presented using the resultant classifiers to provide facilities management teams with spatio-temporal visualization of the work order categories across a series of buildings to help prioritize and streamline operations and maintenance task assignments.
       
  • BIM-based investigation of total energy consumption in delivering building
           products
    • Abstract: Publication date: October 2018Source: Advanced Engineering Informatics, Volume 38Author(s): Cheng Zhang, Raja Shahmir Nizam, Lu Tian Considerable efforts have been made to reduce buildings’ operational energy use over the last decades, but little attention has been paid to reduce the material transportation and construction energy. Focusing only on the operation phase forgoes the opportunity to reduce other building-related energy consumption, and even if the environmental impacts arising from construction and transportation are small as compared to the operation phases, its cumulative impact at the national level is of concern.The energy consumed by a building is divided into two parts embodied energy and operation energy. Further, the embodied energy is constituted of energy intensity of materials, energy consumed during transportation and energy consumed for construction. This paper proposes a methodology to integrate embodied energy consumption into a BIM platform and provides a seamless analysis based on available information. Plug-ins are developed to fulfill a convenient linkage between the BIM model and external databases. Simulation models are created, which can be used as templates for energy optimization during transportation and construction. By analyzing different resource combination scenarios, lower energy consumption can be achieved.
       
  • A vision-based statistical methodology for automatically modeling
           continuous urban traffic flows
    • Abstract: Publication date: October 2018Source: Advanced Engineering Informatics, Volume 38Author(s): Hana Rabbouch, Foued Saâdaoui, Rafaa Mraihi We introduce an online video-based virtual sensor allowing to automatically estimate and forecast the number of vehicles passing through a road section over a continuous time interval. The strategy consists, in the first place, in defining a Motion Intensity Index (MII) whose role is to quantify the visual activity in a traffic video. A wavelet-based cause-and-effect statistical model is then used to match the actual number of vehicles to their respective motion scores. This leads to an efficient estimator of the urban traffic flow. The implementation is well optimized in such a way that the local sampling rate is directly proportional to the amount of visual activity in localized sub-shot units of the video. The procedure allows designing an autonomous sensor giving every moment a measure of the flow on a road section and an expectation of its future levels. The device can be very useful for optimizing transportation management, facilitating strategic decision-making, and analyzing networks with the purpose of optimizing transportation equipment efficiency.
       
  • A novel CRDT-based synchronization method for real-time collaborative CAD
           systems
    • Abstract: Publication date: October 2018Source: Advanced Engineering Informatics, Volume 38Author(s): Xiao Lv, Fazhi He, Yuan Cheng, Yiqi Wu CRDT (Conflict-free Replicated Data Type) has been proposed as an alternative synchronization mechanism for collaborative text editing systems in recent years. However, CRDT-based synchronization methods for collaborative systems with sophisticated objects, such as collaborative CAD (Co-CAD) systems, are rarely investigated in previous literatures. How well CRDT-based synchronization methods for Co-CAD systems could perform remains unknown. This paper presents a novel CRDT-based synchronization method to maintain eventual consistency for the feature-based CAD model. Firstly, three operation relations are defined as the dependency-conflict relation, the mutual exclusive relation and the compatible relation in context of the feature-based CAD systems. Secondly, a feature-based conflict detection mechanism is proposed to detect the three relations. Thirdly, a feature-based conflict resolution approach under the CRDT framework is presented to solve the conflicts. Fourthly, the time complexity and the space complexity are analyzed in theory. Finally, case studies of collaborative modeling procedures verify the correctness and feasibility of the proposed method.
       
  • Feature-based intelligent system for steam simulation using computational
           fluid dynamics
    • Abstract: Publication date: October 2018Source: Advanced Engineering Informatics, Volume 38Author(s): Lei Li, Carlos F. Lange, Zhen Xu, Pingyu Jiang, Yongsheng Ma In the development of products involving fluids, computational fluid dynamics (CFD) has been increasingly applied to investigate the flow associated with various product operating conditions or product designs. The batch simulation is usually conducted when CFD is heavily used, which is not able to respond to the changes in flow regime when the fluid domain changes. In order to overcome this defect, a rule-based intelligent CFD simulation system for steam simulation is proposed to analyze the specific product design and generate the corresponding robust simulation model with accurate results. The rules used in the system are based on physical knowledge and CFD best practices which make this system easy to be applied in other application scenarios by changing the relevant knowledge base. Fluid physics features and dynamic physics features are used to model the intelligent functions of the system. Incorporating CAE boundary features, the CFD analysis view is fulfilled, which maintains the information consistency in a multi-view feature modeling environment. The prototype software tool is developed by Python 3 with separated logics and settings. The effectiveness of the proposed system is proven by the case study of a disk-type gate valve and a pipe reducer in a piping system.
       
  • Cloud-based ubiquitous object sharing platform for heterogeneous logistics
           system integration
    • Abstract: Publication date: October 2018Source: Advanced Engineering Informatics, Volume 38Author(s): Ming Li, Peng Lin, Gangyan Xu, George Q. Huang The intelligence of infrastructure gradually becomes the straw for logistics enterprises to make data-based or date-driven optimization. The integration of heterogeneous logistics systems with existing enterprise information systems is one of the most critical steps to achieve the intelligent infrastructure. Unfortunately, the integration is always a time-consuming process with heavy investment, which suppresses the longings of enterprises, especially for small and medium enterprises (SMEs). Aiming at simplifying the system integration, this paper proposed a cloud-based ubiquitous object sharing platform (CUOSP) to share the integration across SMEs based on the concept of sharing economy. CUOSP acts as a middleware system to make heterogeneous logistics systems universal plug-and-play (UPnP) for enterprise information systems. A kernel-based agent (KBA) is designed as the sharing entity of physical systems. It maintains the features of physical systems and is scalable for different application scenarios. A series of cloud gateway services are emerged not only to provide the basic running and sharing environment, but also to remedy KBA’s weaknesses in computing capacity. A prototype system is developed and implemented based on the framework of CUOSP and a laboratory case according to the consolidation scenario in E-commerce logistics is demonstrated. Comparison experiments are also conducted to explore the real-time and multitasking capacity of KBAs with different kernel characteristics and different computing resources.
       
  • An integrated framework for multi-criteria optimization of thin concrete
           shells at early design stages
    • Abstract: Publication date: October 2018Source: Advanced Engineering Informatics, Volume 38Author(s): Carlos Gomes, Manuel Parente, Miguel Azenha, José Carlos Lino Thin shells are crucially dependent on their shape in order to obtain proper structural performance. In this context, the optimal shape will guarantee performance and safety requirements, while minimizing the use of materials, as well as construction/maintenance costs.Thin shell design is a team-based, multidisciplinary, and iterative process, which requires a high level of interaction between the various parties involved, especially between the Architecture and Engineering teams. As a result of technological development, novel concepts and tools become available to support this process. On the one hand, concepts like Integrated Project Delivery (IPD) show the potential to have a high impact on multidisciplinary environments such as the one in question, supporting the early decision-making process with the availability of as much information as possible. On the other hand, optimization techniques and tools should be highlighted, as they fit the needs and requirements of both the shell shape definition process and the IPD concept. These can be used not only to support advanced design stages, but also to facilitate the initial formulation of shape during the early interactions between architect and structural engineer from an IPD point of view.This paper proposes a methodology aimed at enhancing the interactive and iterative process associated with the early stages of thin shell design, supported by an integrated framework. The latter is based on several tools, namely Rhinoceros 3D, Grasshopper, and Robot Structural Analysis. In order to achieve full integration of the support tools, a custom devised module was developed, so as to allow interoperability between Grasshopper and Robot Structural Analysis. The system resorts to various technologies targeted at improving the shell shape definition process, such as formfinding techniques, parametric and generative models, as well as shape optimization techniques that leverage on multi criteria evolutionary algorithms. The proposed framework is implemented in a set of fictitious scenarios, in which the best thin reinforced concrete shell structures are sought according to given design requirements. Results stemming from this implementation emphasize its interoperability, flexibility, and capability to promote interaction between the elements of the design team, ultimately outputting a set of diverse and creative shell shapes, and thus supporting the pre-design process.
       
  • Community detection in national-scale high voltage transmission networks
           using genetic algorithms
    • Abstract: Publication date: October 2018Source: Advanced Engineering Informatics, Volume 38Author(s): Manuel Guerrero, Francisco G. Montoya, Raúl Baños, Alfredo Alcayde, Consolacíon Gil The large-scale interconnection of electricity networks has been one of the most important investments made by electric companies, and this trend is expected to continue in the future. One of the research topics in this field is the application of graph-based analysis to identify the characteristics of power grids. In particular, the application of community detection techniques allows for the identification of network elements that share valuable properties by partitioning a network into some loosely coupled sub-networks (communities) of similar scale, such that nodes within a community are densely linked, while connections between different communities are sparser. This paper proposes the use of competitive genetic algorithms to rapidly detect any number of community structures in complex grid networks. Results obtained in several national- scale high voltage transmission networks, including Italy, Germany, France, the Iberian peninsula (Spain and Portugal), Texas (US), and the IEEE 118 bus test case that represents a portion of the American Electric Power System (in the Midwestern US), show the good performance of genetic algorithms to detect communities in power grids. In addition to the topological analysis of power grids, the implications of these results from an engineering point of view are discussed, as well as how they could be used to analyze the vulnerability risk of power grids to avoid large-scale cascade failures.Graphical abstractGraphical abstract for this article
       
  • Automatic code compliance with multi-dimensional data fitting in a BIM
           context
    • Abstract: Publication date: October 2018Source: Advanced Engineering Informatics, Volume 38Author(s): P. Patlakas, A. Livingstone, R. Hairstans, G. Neighbour BIM-based tools can contribute to addressing some of the challenges faced by structural engineering practitioners. A BIM-based framework for the development of components that deliver Automatic Code Compliance (ACC) is presented. The structural design problems that such components solve are categorised as simple, where ACC can be implemented directly, or complex, where more advanced approaches are needed. The mathematical process of Multi-Dimensional Data Fitting (MDDF) is introduced in order for the latter, enabling the compression of complex engineering calculations to a single equation that can be easily implemented into a BIM software engineering package. Proof-of-concept examples are given for both cases: offsite-manufactured structural joists are utilised as a non-recursive example, implementing the results obtained in the manufacturer’s literature; the axial capacity of metal fasteners in axially loaded timber-to-timber connections are utilised as an example of recursive problems. The MDDF analysis and its implementation in a BIM package of those problems are presented. Finally, the concept is generalised for non-structural aspects at a framework level, and the challenges, implications, and prospects of ACC in a BIM context are discussed.
       
  • 3 D +reconstruction+based+on+a+robot+equipped+with+uncalibrated+infrared+stereovision+cameras&rft.title=Advanced+Engineering+Informatics&rft.issn=1474-0346&rft.date=&rft.volume=">Automated thermal 3 D reconstruction based on a robot equipped with
           uncalibrated infrared stereovision cameras
    • Abstract: Publication date: October 2018Source: Advanced Engineering Informatics, Volume 38Author(s): T. Sentenac, F. Bugarin, B. Ducarouge, M. Devy In many industrial sectors, Non Destructive Testing (NDT) methods are used for the thermomechanical analysis of parts in assemblies of engines or reactors or for the control of metal forming processes. This article suggests an automated multi-view approach for the thermal 3D reconstruction required in order to compute 3D surface temperature models. This approach is based only on infrared cameras mounted on a Cartesian robot.The low resolution of these cameras associated to a lack of texture to infrared images require to use a global approach based first on an uncalibrated rectification and then on the simultaneous execution, in a single step, of the dense 3D reconstruction and of an extended self-calibration.The uncalibrated rectification is based on an optimization process under constraints which calculates the homographies without prior calculation of the Fundamental Matrix and which minimizes the projective deformations between the initial images and the rectified ones.The extended self-calibration estimates both the parameters of virtual cameras that could provide the rectified images directly, and the parameters of the robot. It is based on two criteria evaluated according to the noise level of the infrared images. This global approach is validated through the reconstruction of a hot object against a reference reconstruction acquired by a 3D scanner.
       
  • Data based complex network modeling and analysis of shield tunneling
           performance in metro construction
    • Abstract: Publication date: October 2018Source: Advanced Engineering Informatics, Volume 38Author(s): C. Zhou, L.Y. Ding, Miroslaw J. Skibniewski, Hanbin Luo, H.T. Zhang Shield tunneling performance depends mainly on changes in geological conditions and machine working status. Understanding its characteristics is the key to operating and controlling shield machine during the metro construction. Despite the large set of shield tunneling data in having been a big challenge in interpreting the underlying meaning, a systematical view of the shield tunneling performance has not yet been identified. In this study, a methodology for the modeling and analysis of shield tunneling performance network is proposed which aims at integrating the high dimensional data mining and the complex network approaches for shield performance evaluation. It is tested by analyzing the heterogeneous data of shield tunneling performance acquired from in the first Yangtze river crossing metro tunnel project in China. Each segment ring tunneling cycle in the construction were considered to be nodes of the network mapped while edges are determined by nodes having the similarity greater than an optimal threshold value. The construct network exhibits high clustering coefficient combined with comparatively short path lengths, which demonstrates a small world topology feature. Communities in the performance network with different size based on the complex network are detected, which provides the vital decision information for geological conditions identification and shield tunneling performance risk evaluation.
       
  • A foundational ontology for the modelling of manufacturing systems
    • Abstract: Publication date: October 2018Source: Advanced Engineering Informatics, Volume 38Author(s): Viktor Zaletelj, Rok Vrabič, Elvis Hozdić, Peter Butala Models of distributed manufacturing systems cannot be consistent without a formal ontology. In this paper, the ontology formulation and maintenance are addressed in the scope of a collaborative modelling environment – in which concurrency, consistency, and model life cycle management should be supported. Thus, an extensible foundational ontology for manufacturing – system modelling is proposed in which the formal definitions of the modelling environment itself enable the definition of the manufacturing system’s elements. The presented approach ensures the consistency of ever-changing models. The ontology is integrated into a modelling framework through the concept of description layers that assist in the management of the model description’s complexity. The feasibility of the approaches is illustrated in an industrial case study that models of a manufacturing system for material processing.
       
  • Balancing homogeneity and heterogeneity in design exploration by
           synthesizing novel design alternatives based on genetic algorithm and
           strategic styling decision
    • Abstract: Publication date: October 2018Source: Advanced Engineering Informatics, Volume 38Author(s): Kyung Hoon Hyun, Ji-Hyun Lee Designers constantly and consistently draft and develop both general concepts and directions to identify the solution that best fits the styling objectives of the lead designer. Designers often confront design fixations that cognitively clash to explore different design combinations. As design teams explore the range of possible design spaces of a certain design strategy, there is an opportunity for computational approaches to improve the styling process. By implementing product appearance similarity and styling strategy in computational design synthesis, it is possible to discover combinations that would otherwise remain unexplored by human designers. Numerous studies on design synthesis have been conducted. However, there has been no focus on the morphological synthesis of designs with strategic styling decisions. Considering this, the proposed study develops a method to synthesize car styling based on product appearance similarity for effective design exploration in the concept generation phase. The similarities of products across different generations, product portfolios, and competitors’ products are calculated to evaluate the strategic styling decision. The results of the strategic styling decision are used to formulate a fitness function. Car styling is then synthesized with a genetic algorithm based on this fitness function to generate car styling in accordance with the target strategic styling decision. In this respect, designers can computationally synthesize novel design alternatives that consider both homogeneity (family look in design) and heterogeneity (design trend in the market) by pinpointing the desired design exploration area. Ultimately, the style synthesis methodology proposed in this research can help designers to utilize the gradual visualization of styling strategies for more effective and efficient managerial design decisions. To do this, we conduct five major tasks: first, car design data are collected for design synthesis; second, the product appearance similarity is calculated to measure the strategic styling decision; third, synthesis validation is conducted to test whether the proposed methodology can create outside-the-box designs; fourth, a genetic algorithm is used to synthesize car designs in consideration of the strategic styling decision; finally, a series of in-depth interviews with experts and validation experiments are conducted with in-house automobile designers to examine the impact of the proposed methodology. The results showed that designers can quantitatively measure and compare the styling strategies of each car brand, then implement design upgrades, while still maintaining that specific style. Correspondingly, computationally generated design alternatives improve the satisfaction in ease, time, objective reflection and novelty of design outcomes when formulating design strategies in the concept generation phase.
       
  • Fault diagnosis of rolling bearing based on optimized soft competitive
           learning Fuzzy ART and similarity evaluation technique
    • Abstract: Publication date: October 2018Source: Advanced Engineering Informatics, Volume 38Author(s): Xiao-Jin Wan, Licheng Liu, Zengbing Xu, Zhigang Xu, Qinglei Li, Fengxiang Xu In this work, a new classification method called Soft Competitive Learning Fuzzy Adaptive Resonance Theory (SFART) is proposed to diagnose bearing faults. In order to solve the misclassification caused by the traditional Fuzzy ART based on hard competitive learning, a soft competitive learning ART model is established using Yu’s norm similarity criterion and lateral inhibition theory. The proposed SFART is based on Yu’s norm similarity criterion and soft competitive learning mechanism. In SFART, Yu’s similarity criterion and the lateral inhibition theory were employed to measure the proximity and select winning neurons, respectively. To further improve the classification accuracy, a feature selection technique based on Yu’s norms is also proposed. In addition, Particle Swarm Optimization (PSO) is introduced to optimize the model parameters of SFART. Meanwhile, the validity of the feature selection technique and parameter optimization method is demonstrated. Finally, fuzzy ART/ ARTMAP (FAM) as well as the feasibility of the proposed SFART algorithm are validated by comparing the diagnosis effectiveness of the proposed algorithm with the classic Fuzzy c-means (FCM), Fuzzy ART and fuzzy ARTMAP (FAM).
       
  • Deep-learning neural-network architectures and methods: Using
           component-based models in building-design energy prediction
    • Abstract: Publication date: October 2018Source: Advanced Engineering Informatics, Volume 38Author(s): Sundaravelpandian Singaravel, Johan Suykens, Philipp Geyer Increasing sustainability requirements make evaluating different design options for identifying energy-efficient design ever more important. These requirements demand simulation models that are not only accurate but also fast. Machine Learning (ML) enables effective mimicry of Building Performance Simulation (BPS) while generating results much faster than BPS. Component-Based Machine Learning (CBML) enhances the capabilities of the monolithic ML model. Extending monolithic ML approach, the paper presents deep-learning architectures, component development methods and evaluates their suitability for space exploration in building design. Results indicate that deep learning increases the performance of models over simple artificial neural network models. Methods such as transfer learning and Multi-Task Learning make the component development process more efficient. Testing the deep-learning model on 201 new design cases indicates that its cooling energy prediction (R2: 0.983) is similar to BPS, while errors for heating energy predictions (R2: 0.848) are higher than BPS. Higher heating energy prediction error can be resolved by collecting heating data using better design space sampling methods that cover the heating demand distribution effectively. Given that the accuracy of the deep-learning model for heating predictions can be increased, the major advantage of deep-learning models over BPS is their high computation speed. BPS required 1145 s to simulate 201 design cases. Using the deep-learning model, similar results can be obtained in 0.9 s. High computation speed makes deep-learning models suitable for design space exploration.Graphical abstractGraphical abstract for this article
       
  • Real-time validation of vision-based over-height vehicle detection system
    • Abstract: Publication date: October 2018Source: Advanced Engineering Informatics, Volume 38Author(s): Bella Nguyen, Ioannis Brilakis Over-height vehicle strikes with low bridges and tunnels are an ongoing problem worldwide. While previous methods have used vision-based systems to address the over-height warning problem, such methods are sensitive to wind. In this paper, we perform a full validation of the system using a constraint-based approach to minimize the number of over-height vehicle misclassifications due to windy conditions. The dataset includes a total of 102 over-height vehicles recorded at frame rates of 25 and 30fps. An analysis is performed of wind and vehicle displacements to track over-height features using optical flow paired with SURF feature detectors. Motion captured within the region of interest was treated as a standard two-class binary linear classification problem with 1 indicating over-height vehicle presence and 0 indicating noise. The algorithm performed with 100% recall, 83.3% precision, false positive rate of 0.2% and warning accuracy of 96.6%.
       
  • BIMification: How to create and use BIM for retrofitting
    • Abstract: Publication date: October 2018Source: Advanced Engineering Informatics, Volume 38Author(s): Raimar J. Scherer, Peter Katranuschkov Building Information Modeling (BIM) is rapidly advancing as an efficient new approach to cooperative building design and construction. However, BIM methodology is still mainly developed and applied for new building projects. The strong societal needs to improve the quality and the overall performance of the existing building stock, especially with regard to energy use, are yet insufficiently supported by BIM. In this paper we propose a structured approach towards the creation of a building information model of an existing building and its use for the purpose of retrofitting or renovation, based on the standard IFC specification (ISO 16739). It implies a process we define as BIMification. This process undergoes two major stages: (1) Anamnesis, dedicated to the survey and collection of facts about the building, and (2) Diagnosis, dedicated to the analysis and interpretation of the collected facts to obtain the necessary understanding of the building and its performance and prepare for the retrofitting design. The paper outlines the broader research aim that triggered the development of the suggested approach and presents the overall concept and methodology, the ICT platform under implementation and the current state of the work. Discussed are also the scope of the approach, envisaged perspectives and further development efforts.Graphical abstractGraphical abstract for this article
       
  • Automated continuous construction progress monitoring using multiple
           workplace real time 3D scans
    • Abstract: Publication date: October 2018Source: Advanced Engineering Informatics, Volume 38Author(s): Zoran Pučko, Nataša Šuman, Danijel Rebolj In recent years, exponential growth has been detected in research efforts focused on automated construction progress monitoring. Despite various data acquisition methods and approaches, the success is limited. This paper proposes a new method, where changes are constantly perceived and as-built model continuously updated during the construction process, instead of periodical scanning of the whole building under construction. It turned out that low precision 3D scanning devices, which are closely observing active workplaces, are sufficient for correct identification of the built elements. Such scanning devices are small enough to fit onto workers’ protective helmets and on the applied machinery. In this way, workers capture all workplaces inside and outside of the building in real time and record partial point clouds, their locations, and time stamps. The partial point clouds are then registered and merged into a complete 4D as-built point cloud of a building under construction. Identification of as-designed BIM elements within the 4D as-built point cloud then results in the 4D as-built BIM. Finally, the comparison of the 4D as-built BIM and the 4D as-designed BIM enables identification of the differences between both models and thus the deviations from the time schedule. The differences are reported in virtual real-time, which enables more efficient project management.
       
  • Decentralized damage detection of seismically-excited buildings using
           multiple banks of Kalman estimators
    • Abstract: Publication date: October 2018Source: Advanced Engineering Informatics, Volume 38Author(s): Jau-Yu Chou, Chia-Ming Chang Natural hazards result in ill-conditioned structures with unfavorable damage. To early recognize damage existence, structures can be screened by damage detection methods after a critical hazard event. These damage detection methods are often developed based on a centralized acquiring and computing system that challenges the feasibility of deployment in a large-scale structure. Decentralized damage detection methods alter a single system to multiple subsystems that allow spatially distributing in a structure and yield comparable performance with the centralized approach. In this study, a decentralized damage detection method based on modal prediction errors via multiple banks of Kalman estimators is proposed. First, a sensor network is comprised of multiple subsystems over a structure of which the subsystems have overlapped sensing nodes. These subsystems are individually identified by an input–output frequency-domain system identification method under ambient vibrations. The identified models are then converted into several banks of Kalman estimators, and the estimators generate the estimation of structural modal responses. The prediction errors are calculated from the differentiation between measured and estimated modal responses, and the accumulated standard deviations of modal prediction errors serve as the damage indices for recognizing the damage occurrence, locations, and levels. A numerical example is introduced to demonstrate the proposed method as well as to evaluate the detection effectiveness. Moreover, the proposed method is also experimentally verified by a scaled twin-tower building using shake table testing. The experimental results indicate that the proposed method is quite effective to inform damage of structures in terms of damage occurrence, locations, and levels.
       
  • Semantic weldability prediction with RSW quality dataset and knowledge
           construction
    • Abstract: Publication date: October 2018Source: Advanced Engineering Informatics, Volume 38Author(s): Kyoung-Yun Kim, Fahim Ahmed This paper presents a semantic Resistance Spot Welding (RSW) weldability prediction framework. The framework constructs a shareable weldability knowledge database based on the regression rules from inconsistent RSW quality datasets. This research aims to effectively predict the weldability of RSW process for existing or new weldment design. A real welding test dataset collected from an automotive OEM is used to extract decision rules using a decision tree algorithm, Classification and Regression Trees (CART). The extracted decision rules are converted systematically into SWRL rules for capturing the semantics and to increase the shareability of the constructed knowledge. The experiments show that the RSW ontology, along with SWRL rules that contains weldability rules constructed from the datasets, successfully predicts the weldability (nugget width) values for RSW cases. The predicted nugget width values are found to be in-close proximity of the actual values. This paper shows that semantic prediction framework construes an intelligent way for constructing accurate and transparent predictive models for RSW weldability verification.
       
  • A methodology for brand feature establishment based on the decomposition
           and reconstruction of a feature curve
    • Abstract: Publication date: October 2018Source: Advanced Engineering Informatics, Volume 38Author(s): Shih-Wen Hsiao, Chu-Hsuan Lee, Rong-Qi Chen, Chien-Yu Lin For creative products, maintaining original brand elements and features in a new product is an important issue in the design process as brand features are conceived and generated for longevity. However, current methods rely on designers’ abilities, and the size of forms is easily affected when shape morphing is applied, causing limitations in computer-aided design. In order to focus on design while preserving key features, a systematic method for presenting brand features is proposed in this article. In this method, the feature curves of the brand features of a company are decomposed with defined feature parameters, which were then used to reconstruct the feature curve of the designed product in the design stage by using a residual modified gray prediction model. A classic vehicle configuration design is taken as an example to show the implementation procedure of the proposed method. With residual modification, this method can also assimilate other forms from the original form database, and generate new forms based on gray prediction. The results show that brand features can be retained in the newly designed product based on the proposed method. Though vehicle design is taken as the example, this method can also be used to develop designs for many other the brand features. For classic products with historical value, this method can generate new forms that maintain original brand features, thereby satisfying customers’ needs for brand authenticity.
       
  • An adaptive clustering-based genetic algorithm for the dual-gantry
           pick-and-place machine optimization
    • Abstract: Publication date: August 2018Source: Advanced Engineering Informatics, Volume 37Author(s): Tian He, Debiao Li, Sang Won Yoon This research proposes an adaptive clustering-based genetic algorithm (ACGA) to optimize the pick-and-place operation of a dual-gantry component placement machine, which has two independent gantries that alternately place components onto a printed circuit board (PCB). The proposed optimization problem consists of several highly interrelated sub-problems, such as component allocation, nozzle and feeder setups, pick-and-place sequences, etc. In the proposed ACGA, the nozzle and component allocation decisions are made before the evolutionary search of a genetic algorithm to improve the algorithm efficiency. First, the nozzle allocation problem is modeled as a nonlinear integer programming problem and solved by a search-based heuristic that minimizes the total number of the dual-gantry cycles. Then, an adaptive clustering approach is developed to allocate components to each gantry cycle by evaluating the gantry traveling distances over the PCB and the component feeders. Numerical experiments compare the proposed ACGA to another clustering-based genetic algorithm LCO and a heuristic algorithm mPhase in the literature using 30 industrial PCB samples. The experiment results show that the proposed ACGA algorithm reduces the total gantry moving distance by 5.71% and 4.07% on average compared to the LCO and mPhase algorithms, respectively.
       
  • Personalized method for self-management of trunk postural ergonomic
           hazards in construction rebar ironwork
    • Abstract: Publication date: August 2018Source: Advanced Engineering Informatics, Volume 37Author(s): Xuzhong Yan, Heng Li, Hong Zhang, Timothy M. Rose Construction rebar workers face postural ergonomic hazards that can lead to work-related Lower Back Disorders (LBDs), primarily due to their prolonged awkward working postures required by the job. In a previous study, Wearable Inertial Measurement Units (WIMUs)-based Personal Protective Equipment (PPE) was developed to alert workers when their trunk inclination holding time exceeded acceptable thresholds as defined in ISO standard 11226:2000. However, subsequent field testing identified PPE was ineffective for some workers because the adopted ISO thresholds were not personalized and did not consider differences in individual’s response to postural ergonomic hazards. To address this problem, this paper introduces a worker-centric method to assist in the self-management of work-related ergonomic hazards, based on data-driven personalized healthcare intervention. Firstly, personalized information is gathered by providing each rebar ironworker a WIMU-based personalized mobile health (mHealth) system to capture their trunk inclination angle and holding time data. Then, the captured individual trunk inclination holding times are analyzed by a Gaussian-like probability density function, where abnormal holding time thresholds can be generated and updated in response to incoming trunk inclination records of an individual during work time. These abnormal holding time thresholds are then adapted to be used as personalized trunk inclination holding time recommendations for an individual worker to self-manage their working postures, based on their own trunk inclination records. The proposed worker-centric method to assist in the self-management of ergonomic postural hazards leading to LBDs was field tested on a construction site over a three-month duration. The results of the paired t-tests indicate that posture scores evaluated by the Ovako Working Posture Analysis System (OWAS) significantly decrease when the personalized recommendation is applied, while increase again when the personalized recommendation is removed. Based on data-driven personalized healthcare intervention, the results demonstrate the significant potential of the proposed worker-centric self-management method for rebar workers in preventing and controlling postural ergonomic hazards during construction rebar ironwork.
       
  • A large-scale evaluation of automated metadata inference approaches on
           sensors from air handling units
    • Abstract: Publication date: August 2018Source: Advanced Engineering Informatics, Volume 37Author(s): Jingkun Gao, Mario Bergés Building automation systems provide abundant sensor data to enable the potential of using data analytics to, among other things, improve the energy efficiency of the building. However, deployment of these applications for buildings, such as, fault detection and diagnosis (FDD) on multiple buildings remains a challenge due to the non-trivial efforts of organizing, managing and extracting metadata associated with sensors (e.g., information about their location, function, etc.), which is required by applications. One of the reasons leading to the problem is that varying conventions, acronyms, and standards are used to define this metadata. To better understand the nature of the problem, as well as the performance and scalability of existing solutions, we implement and test 6 different time-series based metadata inference approaches on sensors from 614 air handling units (AHU) instrumented in 35 building sites accounting for more than 400 buildings distributed across United States of America. We infer 12 types of sensors and actuators in AHUs required by a rule-based FDD application: AHU performance and assessment rules (APAR). Our results show that: (1) the average performance of these approaches in terms of accuracy is similar across building sites, though there is significant variance; (2) the expected accuracy of classifying the type of points required by APAR for a new unseen building is, on average, 75%; (3) the performance of the model does not decrease as long as training data and testing data are extracted from adjacent months.Graphical abstractGraphical abstract for this article
       
  • Understanding building occupant activities at scale: An integrated
           knowledge-based and data-driven approach
    • Abstract: Publication date: August 2018Source: Advanced Engineering Informatics, Volume 37Author(s): Andrew J. Sonta, Perry E. Simmons, Rishee K. Jain Buildings are our homes and our workplaces. They directly affect our well-being, and they impact the natural global environment primarily through the energy they consume. Understanding the behavior of occupants in buildings has vital implications for improving the energy efficiency of building systems and for providing knowledge to designers about how occupants will utilize the spaces they create. However, current methods for inferring building occupant activity patterns are limited in two primary areas: First, they lack adaptability to new spaces and scalability to larger spaces due to the time and cost intensity of collecting ground truth data for training the embedded algorithms. Second, they do not incorporate explicit knowledge about occupant dynamics in their implementation, limiting their ability to uncover deep insights about activity patterns in the data. In this paper, we develop a methodology for classifying occupant activity patterns from plug load sensor data at the desk level. Our method makes us of a common unsupervised learning algorithm—the Gaussian mixture model—and, in addition, it incorporates explicit knowledge about occupant presence and absence in order to preserve adaptability and effectiveness. We validate our method using a pilot study in an academic office building and demonstrate its potential for scalability through a case study of an open-office building in San Francisco, CA. Our method offers key insights into spatially and temporally granular occupancy states and space utilization that could not otherwise be obtained.Graphical abstractGraphical abstract for this article
       
  • Scan-to-BIM for ‘secondary’ building components
    • Abstract: Publication date: August 2018Source: Advanced Engineering Informatics, Volume 37Author(s): Antonio Adán, Blanca Quintana, Samuel A. Prieto, Frédéric Bosché Works dealing with Scan-to-BIM have, to date, principally focused on 'structural' components such as floors, ceilings and walls (with doors and windows). But the control of new facilities and the production of their corresponding as-is BIM models requires the identification and inspection of numerous other building components and objects, e.g. MEP components, such as plugs, switches, ducts, and signs. In this paper, we present a new 6D-based (XYZ + RGB) approach that processes dense coloured 3D points provided by terrestrial laser scanners in order to recognize the aforementioned smaller objects that are commonly located on walls. This paper focuses on the recognition of objects such as sockets, switches, signs, extinguishers and others. After segmenting the point clouds corresponding to the walls of a building, a set of candidate objects are detected independently in the colour and geometric spaces, and an original consensus procedure integrates both results in order to infer recognition. Finally, the recognized object is positioned and inserted in the as-is semantically-rich 3D model, or BIM model. The assessment of the method has been carried out in simulated scenarios under virtual scanning providing high recognition rates and precise positioning results. Experimental tests in real indoors using our MoPAD (Mobile Platform for Autonomous Digitization) platform have also yielded promising results.
       
  • Crowd simulation-based knowledge mining supporting building evacuation
           design
    • Abstract: Publication date: August 2018Source: Advanced Engineering Informatics, Volume 37Author(s): Calin Boje, Haijiang Li Assessing building evacuation performance designs in emergency situations requires complex scenarios which need to be prepared and analysed using crowd simulation tools, requiring significant manual input. With current procedures, every design iteration requires several simulation scenarios, leading to a complicated and time-consuming process. This study aims to investigate the level of integration between digital building models and crowd simulation, within the scope of design automation. A methodology is presented in which existing ontology tools facilitate knowledge representation and mining throughout the process. Several information models are used to integrate, automate and provide feedback to the design decision-making processes. The proposed concept thus reduces the effort required to create valid simulation scenarios by applying represented knowledge, and provides feedback based on results and design objectives. To apply and test the methodology a system was developed, which is introduced here. The context of building performance during evacuation scenarios is considered, but additional design perspectives can be included. The system development section expands on the essential theoretical concepts required and the case study section shows a practical implementation of the system.
       
  • IFC Monitor – An IFC schema extension for modeling structural health
           monitoring systems
    • Abstract: Publication date: August 2018Source: Advanced Engineering Informatics, Volume 37Author(s): Michael Theiler, Kay Smarsly The pervasive emergence of sensing technologies for structural health monitoring (SHM) and the digitalization ubiquitous in engineering (“Industry 4.0”) pose increasing demands on information modeling concepts in civil engineering. While in building information modeling (BIM) conventional building information (such as geometry, material, or cost) can precisely be described using current modeling standards, information about SHM systems, referred to as “monitoring-related information”, cannot be fully described on a well-defined, formal basis. In this paper, a BIM-based approach towards describing monitoring-related information is proposed, using the Industry Foundation Classes (IFC), an open BIM standard facilitating the interoperability of BIM models, as a formal basis. First, possibilities and constraints of describing monitoring-related information with the IFC schema are discussed. Then, information necessary to describe SHM systems is integrated into a semantic model serving as a technology-independent metamodel. Next, the IFC schema is extended to enable BIM-based descriptions of SHM systems in compliance with IFC modeling capabilities, which is referred to as “IFC Monitor” schema. The IFC Monitor schema is verified with test software used in the official IFC certification program. For validation, a prototype SHM system is formally described using the IFC Monitor schema. The validation aims at checking if the IFC Monitor schema is capable of precisely describing monitoring-related information. As will be shown in this paper, the description of the prototype SHM system meets the requirements of a well-defined IFC model as specified in the official IFC certification program. As a result, the IFC Monitor schema proposed in this study advances BIM-based descriptions of SHM systems in association with structural systems being monitored on a well-defined, formal basis.
       
  • A review of 3D reconstruction techniques in civil engineering and their
           applications
    • Abstract: Publication date: August 2018Source: Advanced Engineering Informatics, Volume 37Author(s): Zhiliang Ma, Shilong Liu Three-dimensional (3D) reconstruction techniques have been used to obtain the 3D representations of objects in civil engineering in the form of point cloud models, mesh models and geometric models more often than ever, among which, point cloud models are the basis. In order to clarify the status quo of the research and application of the techniques in civil engineering, literature retrieval is implemented by using major literature databases in the world and the result is summarized by analyzing the abstracts or the full papers when required. First, the research methodology is introduced, and the framework of 3D reconstruction techniques is established. Second, 3D reconstruction techniques for generating point clouds and processing point clouds along with the corresponding algorithms and methods are reviewed respectively. Third, their applications in reconstructing and managing construction sites and reconstructing pipelines of Mechanical, Electrical and Plumbing (MEP) systems, are presented as typical examples, and the achievements are highlighted. Finally, the challenges are discussed and the key research directions to be addressed in the future are proposed. This paper contributes to the knowledge body of 3D reconstruction in two aspects, i.e. summarizing systematically the up-to-date achievements and challenges for the applications of 3D reconstruction techniques in civil engineering, and proposing key future research directions to be addressed in the field.
       
  • Distress classification of class-imbalanced inspection data via
           correlation-maximizing weighted extreme learning machine
    • Abstract: Publication date: August 2018Source: Advanced Engineering Informatics, Volume 37Author(s): Keisuke Maeda, Sho Takahashi, Takahiro Ogawa, Miki Haseyama This paper presents distress classification of class-imbalanced inspection data via correlation-maximizing weighted extreme learning machine (CMWELM). For distress classification, it is necessary to extract semantic features that can effectively distinguish multiple kinds of distress from a small amount of class-imbalanced data. In recent machine learning techniques such as general deep learning methods, since effective feature transformation from visual features to semantic features can be realized by using multiple hidden layers, a large amount of training data are required. However, since the amount of training data of civil structures becomes small, it becomes difficult to perform successful transformation by using these multiple hidden layers. On the other hand, CMWELM consists of two hidden layers. The first hidden layer performs feature transformation, which can directly extract the semantic features from visual features, and the second hidden layer performs classification with solving the class-imbalanced problem. Specifically, in the first hidden layer, the feature transformation is realized by using projections obtained by maximizing the canonical correlation between visual and text features as weight parameters of the hidden layer without designing multiple hidden layers. Furthermore, the second hidden layer enables successful training of our classifier by using weighting factors concerning the class-imbalanced problem. Consequently, CMWELM realizes accurate distress classification from a small amount of class-imbalanced data.
       
  • A BIM-based visualization and warning system for fire rescue
    • Abstract: Publication date: August 2018Source: Advanced Engineering Informatics, Volume 37Author(s): Xiu-Shan Chen, Chi-Chang Liu, I-Chen Wu Structural fires are common disasters. In Taiwan, about 100 firefighters die during fire rescues each year, primarily because they are unaware of the causes of the fire and unfamiliar with the location’s environment. Meanwhile, evacuees often die in the panic of evacuation. To solve these problems, this research proposes a Building Information Modeling (BIM)-based visualization and warning system for fire rescue. A fire dynamics simulator (FDS) simulates various conditions of structural fires in conjunction with the visualization and integration properties of BIM, and the simulation results for temperature, carbon monoxide, and visibility can be integrated and presented in the BIM model for briefing purposes before rescue operations begin. In addition, this research integrates Internet of Things (IoT) technology, which allows real-time situation monitoring. In the event of a fire, the BIM model will immediately display the situation of the fire scene and control LED escape route pointers according to the actual situation. The primary objective of this system is to provide useful information to firefighters such that they can be aware of the fire’s environment and create an effective rescue plan. Moreover, the automated LED escape route pointer may assist the building’s occupants to escape, provide the firefighters with valuable information, and allow them quickly to discover hazards so that the number of casualties can be minimized.
       
  • Detecting healthy concrete surfaces
    • Abstract: Publication date: August 2018Source: Advanced Engineering Informatics, Volume 37Author(s): Philipp Hüthwohl, Ioannis Brilakis Teams of engineers visually inspect more than half a million bridges per year in the US and EU. There is clear evidence to suggest that they are not able to meet all bridge inspection guideline requirements due to a combination of the level of detail expected, the limited time available and the large area of bridge surfaces to be inspected. Methods have been proposed to address this problem through damage detection in visual data, yet the inspection load remains high. This paper proposes a method to tackle this problem by detecting (and disregarding) healthy concrete areas that comprise over 80–90% of the total area. The originality of this work lies in the method’s slicing and merging to enable the sequential processing of high resolution bridge surface textures with a state of the art classifier to distinguish between healthy and potentially unhealthy surface texture. Morphological operators are then used to generate an outline mask to highlight the classification results in the surface texture. The training and validation set consists of 1028 images taken from multiple Department of Transportation bridge inspection databases and data collection from ten highway bridges around Cambridge. The presented method achieves a search space reduction for an inspector of 90.1% with a risk of missing a defect patch of 8.2%. This work is of great significance for bridge inspectors as they are now able to spend more time on assessing potentially unhealthy surface regions instead of searching for these needles in a mainly healthy concrete surface haystack.
       
  • Automated detection of workers and heavy equipment on construction sites:
           A convolutional neural network approach
    • Abstract: Publication date: August 2018Source: Advanced Engineering Informatics, Volume 37Author(s): Weili Fang, Lieyun Ding, Botao Zhong, Peter E.D. Love, Hanbin Luo Detecting the presence of workers, plant, equipment, and materials (i.e. objects) on sites to improve safety and productivity has formed an integral part of computer vision-based research in construction. Such research has tended to focus on the use of computer vision and pattern recognition approaches that are overly reliant on the manual extraction of features and small datasets (
       
  • Integrating multi-granularity model and similarity measurement for
           transforming process data into different granularity knowledge
    • Abstract: Publication date: August 2018Source: Advanced Engineering Informatics, Volume 37Author(s): Yubin Fan, Chuang Liu, Junbiao Wang The core of intelligent manufacturing is to incorporate the expert knowledge in manufacturing process, and knowledge transformation is the key to knowledge accumulation and application. In this paper, the research carried on transformation for different granularity knowledge from the cases of sheet metal parts in process planning. First of all, this paper analyzes the difference of organization structure between process data and knowledge in the base. The multi-granularity model of process knowledge is established in the form of tuple, which helps to clarify the hierarchy structure and internal relations. Thereafter, the concrete process is presented to transform single granularity process data into multi-granularity process knowledge, i.e., process data extraction, state determination and knowledge construction. With respect to state determination, similarity measure methods for different granularity knowledge are established to reduce the redundancy in the transformation process. As a novel approach, sequence alignment based on edit distance is proposed to calculate similarity exactly between two process flows. Finally, the knowledge transformation tool for different granularity knowledge is developed to enhance knowledge acquisition and improve the strength of knowledge reuse in fabrication order design for sheet metal parts through application of the above method. Also an example is given to illustrate the usefulness of the proposed method.
       
 
 
JournalTOCs
School of Mathematical and Computer Sciences
Heriot-Watt University
Edinburgh, EH14 4AS, UK
Email: journaltocs@hw.ac.uk
Tel: +00 44 (0)131 4513762
Fax: +00 44 (0)131 4513327
 
Home (Search)
Subjects A-Z
Publishers A-Z
Customise
APIs
Your IP address: 54.196.13.210
 
About JournalTOCs
API
Help
News (blog, publications)
JournalTOCs on Twitter   JournalTOCs on Facebook

JournalTOCs © 2009-