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COMPUTER SCIENCE (1221 journals)                  1 2 3 4 5 6 7 | Last

Showing 1 - 200 of 872 Journals sorted alphabetically
3D Printing and Additive Manufacturing     Full-text available via subscription   (Followers: 21)
Abakós     Open Access   (Followers: 4)
ACM Computing Surveys     Hybrid Journal   (Followers: 29)
ACM Journal on Computing and Cultural Heritage     Hybrid Journal   (Followers: 8)
ACM Journal on Emerging Technologies in Computing Systems     Hybrid Journal   (Followers: 16)
ACM Transactions on Accessible Computing (TACCESS)     Hybrid Journal   (Followers: 3)
ACM Transactions on Algorithms (TALG)     Hybrid Journal   (Followers: 15)
ACM Transactions on Applied Perception (TAP)     Hybrid Journal   (Followers: 5)
ACM Transactions on Architecture and Code Optimization (TACO)     Hybrid Journal   (Followers: 9)
ACM Transactions on Autonomous and Adaptive Systems (TAAS)     Hybrid Journal   (Followers: 9)
ACM Transactions on Computation Theory (TOCT)     Hybrid Journal   (Followers: 12)
ACM Transactions on Computational Logic (TOCL)     Hybrid Journal   (Followers: 3)
ACM Transactions on Computer Systems (TOCS)     Hybrid Journal   (Followers: 18)
ACM Transactions on Computer-Human Interaction     Hybrid Journal   (Followers: 15)
ACM Transactions on Computing Education (TOCE)     Hybrid Journal   (Followers: 6)
ACM Transactions on Design Automation of Electronic Systems (TODAES)     Hybrid Journal   (Followers: 6)
ACM Transactions on Economics and Computation     Hybrid Journal   (Followers: 1)
ACM Transactions on Embedded Computing Systems (TECS)     Hybrid Journal   (Followers: 3)
ACM Transactions on Information Systems (TOIS)     Hybrid Journal   (Followers: 20)
ACM Transactions on Intelligent Systems and Technology (TIST)     Hybrid Journal   (Followers: 8)
ACM Transactions on Interactive Intelligent Systems (TiiS)     Hybrid Journal   (Followers: 3)
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)     Hybrid Journal   (Followers: 9)
ACM Transactions on Reconfigurable Technology and Systems (TRETS)     Hybrid Journal   (Followers: 6)
ACM Transactions on Sensor Networks (TOSN)     Hybrid Journal   (Followers: 8)
ACM Transactions on Speech and Language Processing (TSLP)     Hybrid Journal   (Followers: 9)
ACM Transactions on Storage     Hybrid Journal  
ACS Applied Materials & Interfaces     Hybrid Journal   (Followers: 32)
Acta Automatica Sinica     Full-text available via subscription   (Followers: 2)
Acta Informatica Malaysia     Open Access  
Acta Universitatis Cibiniensis. Technical Series     Open Access  
Ad Hoc Networks     Hybrid Journal   (Followers: 11)
Adaptive Behavior     Hybrid Journal   (Followers: 10)
Advanced Engineering Materials     Hybrid Journal   (Followers: 28)
Advanced Science Letters     Full-text available via subscription   (Followers: 11)
Advances in Adaptive Data Analysis     Hybrid Journal   (Followers: 7)
Advances in Artificial Intelligence     Open Access   (Followers: 15)
Advances in Calculus of Variations     Hybrid Journal   (Followers: 4)
Advances in Catalysis     Full-text available via subscription   (Followers: 5)
Advances in Computational Mathematics     Hybrid Journal   (Followers: 19)
Advances in Computer Engineering     Open Access   (Followers: 4)
Advances in Computer Science : an International Journal     Open Access   (Followers: 14)
Advances in Computing     Open Access   (Followers: 2)
Advances in Data Analysis and Classification     Hybrid Journal   (Followers: 55)
Advances in Engineering Software     Hybrid Journal   (Followers: 28)
Advances in Geosciences (ADGEO)     Open Access   (Followers: 14)
Advances in Human Factors/Ergonomics     Full-text available via subscription   (Followers: 22)
Advances in Human-Computer Interaction     Open Access   (Followers: 20)
Advances in Materials Science     Open Access   (Followers: 14)
Advances in Operations Research     Open Access   (Followers: 12)
Advances in Parallel Computing     Full-text available via subscription   (Followers: 7)
Advances in Porous Media     Full-text available via subscription   (Followers: 5)
Advances in Remote Sensing     Open Access   (Followers: 49)
Advances in Science and Research (ASR)     Open Access   (Followers: 6)
Advances in Technology Innovation     Open Access   (Followers: 6)
AEU - International Journal of Electronics and Communications     Hybrid Journal   (Followers: 8)
African Journal of Information and Communication     Open Access   (Followers: 9)
African Journal of Mathematics and Computer Science Research     Open Access   (Followers: 4)
AI EDAM     Hybrid Journal   (Followers: 1)
Air, Soil & Water Research     Open Access   (Followers: 12)
AIS Transactions on Human-Computer Interaction     Open Access   (Followers: 6)
Algebras and Representation Theory     Hybrid Journal   (Followers: 1)
Algorithms     Open Access   (Followers: 11)
American Journal of Computational and Applied Mathematics     Open Access   (Followers: 5)
American Journal of Computational Mathematics     Open Access   (Followers: 4)
American Journal of Information Systems     Open Access   (Followers: 6)
American Journal of Sensor Technology     Open Access   (Followers: 4)
Anais da Academia Brasileira de Ciências     Open Access   (Followers: 2)
Analog Integrated Circuits and Signal Processing     Hybrid Journal   (Followers: 7)
Analysis in Theory and Applications     Hybrid Journal   (Followers: 1)
Animation Practice, Process & Production     Hybrid Journal   (Followers: 5)
Annals of Combinatorics     Hybrid Journal   (Followers: 4)
Annals of Data Science     Hybrid Journal   (Followers: 12)
Annals of Mathematics and Artificial Intelligence     Hybrid Journal   (Followers: 13)
Annals of Pure and Applied Logic     Open Access   (Followers: 3)
Annals of Software Engineering     Hybrid Journal   (Followers: 13)
Annals of West University of Timisoara - Mathematics and Computer Science     Open Access  
Annual Reviews in Control     Hybrid Journal   (Followers: 8)
Anuario Americanista Europeo     Open Access  
Applicable Algebra in Engineering, Communication and Computing     Hybrid Journal   (Followers: 2)
Applied and Computational Harmonic Analysis     Full-text available via subscription   (Followers: 1)
Applied Artificial Intelligence: An International Journal     Hybrid Journal   (Followers: 12)
Applied Categorical Structures     Hybrid Journal   (Followers: 2)
Applied Computational Intelligence and Soft Computing     Open Access   (Followers: 13)
Applied Computer Systems     Open Access   (Followers: 2)
Applied Informatics     Open Access  
Applied Mathematics and Computation     Hybrid Journal   (Followers: 33)
Applied Medical Informatics     Open Access   (Followers: 10)
Applied Numerical Mathematics     Hybrid Journal   (Followers: 5)
Applied Soft Computing     Hybrid Journal   (Followers: 16)
Applied Spatial Analysis and Policy     Hybrid Journal   (Followers: 5)
Applied System Innovation     Open Access  
Architectural Theory Review     Hybrid Journal   (Followers: 3)
Archive of Applied Mechanics     Hybrid Journal   (Followers: 5)
Archive of Numerical Software     Open Access  
Archives and Museum Informatics     Hybrid Journal   (Followers: 145)
Archives of Computational Methods in Engineering     Hybrid Journal   (Followers: 5)
arq: Architectural Research Quarterly     Hybrid Journal   (Followers: 8)
Artifact     Hybrid Journal   (Followers: 2)
Artificial Life     Hybrid Journal   (Followers: 7)
Asia Pacific Journal on Computational Engineering     Open Access  
Asia-Pacific Journal of Information Technology and Multimedia     Open Access   (Followers: 1)
Asian Journal of Computer Science and Information Technology     Open Access  
Asian Journal of Control     Hybrid Journal  
Assembly Automation     Hybrid Journal   (Followers: 2)
at - Automatisierungstechnik     Hybrid Journal   (Followers: 1)
Australian Educational Computing     Open Access   (Followers: 1)
Automatic Control and Computer Sciences     Hybrid Journal   (Followers: 5)
Automatic Documentation and Mathematical Linguistics     Hybrid Journal   (Followers: 5)
Automatica     Hybrid Journal   (Followers: 13)
Automation in Construction     Hybrid Journal   (Followers: 7)
Autonomous Mental Development, IEEE Transactions on     Hybrid Journal   (Followers: 8)
Basin Research     Hybrid Journal   (Followers: 5)
Behaviour & Information Technology     Hybrid Journal   (Followers: 52)
Big Data and Cognitive Computing     Open Access   (Followers: 2)
Biodiversity Information Science and Standards     Open Access  
Bioinformatics     Hybrid Journal   (Followers: 308)
Biomedical Engineering     Hybrid Journal   (Followers: 16)
Biomedical Engineering and Computational Biology     Open Access   (Followers: 13)
Biomedical Engineering, IEEE Reviews in     Full-text available via subscription   (Followers: 20)
Biomedical Engineering, IEEE Transactions on     Hybrid Journal   (Followers: 35)
Briefings in Bioinformatics     Hybrid Journal   (Followers: 49)
British Journal of Educational Technology     Hybrid Journal   (Followers: 149)
Broadcasting, IEEE Transactions on     Hybrid Journal   (Followers: 12)
c't Magazin fuer Computertechnik     Full-text available via subscription   (Followers: 1)
CALCOLO     Hybrid Journal  
Calphad     Hybrid Journal   (Followers: 2)
Canadian Journal of Electrical and Computer Engineering     Full-text available via subscription   (Followers: 15)
Capturing Intelligence     Full-text available via subscription  
Catalysis in Industry     Hybrid Journal   (Followers: 1)
CEAS Space Journal     Hybrid Journal   (Followers: 2)
Cell Communication and Signaling     Open Access   (Followers: 2)
Central European Journal of Computer Science     Hybrid Journal   (Followers: 5)
CERN IdeaSquare Journal of Experimental Innovation     Open Access   (Followers: 3)
Chaos, Solitons & Fractals     Hybrid Journal   (Followers: 3)
Chemometrics and Intelligent Laboratory Systems     Hybrid Journal   (Followers: 15)
ChemSusChem     Hybrid Journal   (Followers: 7)
China Communications     Full-text available via subscription   (Followers: 8)
Chinese Journal of Catalysis     Full-text available via subscription   (Followers: 2)
CIN Computers Informatics Nursing     Hybrid Journal   (Followers: 11)
Circuits and Systems     Open Access   (Followers: 15)
Clean Air Journal     Full-text available via subscription   (Followers: 1)
CLEI Electronic Journal     Open Access  
Clin-Alert     Hybrid Journal   (Followers: 1)
Clinical eHealth     Open Access  
Cluster Computing     Hybrid Journal   (Followers: 2)
Cognitive Computation     Hybrid Journal   (Followers: 4)
COMBINATORICA     Hybrid Journal  
Combinatorics, Probability and Computing     Hybrid Journal   (Followers: 4)
Combustion Theory and Modelling     Hybrid Journal   (Followers: 14)
Communication Methods and Measures     Hybrid Journal   (Followers: 12)
Communication Theory     Hybrid Journal   (Followers: 23)
Communications Engineer     Hybrid Journal   (Followers: 1)
Communications in Algebra     Hybrid Journal   (Followers: 3)
Communications in Computational Physics     Full-text available via subscription   (Followers: 2)
Communications in Information Science and Management Engineering     Open Access   (Followers: 4)
Communications in Partial Differential Equations     Hybrid Journal   (Followers: 3)
Communications of the ACM     Full-text available via subscription   (Followers: 51)
Communications of the Association for Information Systems     Open Access   (Followers: 16)
COMPEL: The International Journal for Computation and Mathematics in Electrical and Electronic Engineering     Hybrid Journal   (Followers: 3)
Complex & Intelligent Systems     Open Access   (Followers: 1)
Complex Adaptive Systems Modeling     Open Access  
Complex Analysis and Operator Theory     Hybrid Journal   (Followers: 2)
Complexity     Hybrid Journal   (Followers: 6)
Complexus     Full-text available via subscription  
Composite Materials Series     Full-text available via subscription   (Followers: 8)
Computación y Sistemas     Open Access  
Computation     Open Access   (Followers: 1)
Computational and Applied Mathematics     Hybrid Journal   (Followers: 3)
Computational and Mathematical Biophysics     Open Access   (Followers: 1)
Computational and Mathematical Methods in Medicine     Open Access   (Followers: 2)
Computational and Mathematical Organization Theory     Hybrid Journal   (Followers: 2)
Computational and Structural Biotechnology Journal     Open Access   (Followers: 2)
Computational and Theoretical Chemistry     Hybrid Journal   (Followers: 9)
Computational Astrophysics and Cosmology     Open Access   (Followers: 1)
Computational Biology and Chemistry     Hybrid Journal   (Followers: 12)
Computational Chemistry     Open Access   (Followers: 2)
Computational Cognitive Science     Open Access   (Followers: 2)
Computational Complexity     Hybrid Journal   (Followers: 4)
Computational Condensed Matter     Open Access  
Computational Ecology and Software     Open Access   (Followers: 9)
Computational Economics     Hybrid Journal   (Followers: 9)
Computational Geosciences     Hybrid Journal   (Followers: 17)
Computational Linguistics     Open Access   (Followers: 23)
Computational Management Science     Hybrid Journal  
Computational Mathematics and Modeling     Hybrid Journal   (Followers: 8)
Computational Mechanics     Hybrid Journal   (Followers: 5)
Computational Methods and Function Theory     Hybrid Journal  
Computational Molecular Bioscience     Open Access   (Followers: 2)
Computational Optimization and Applications     Hybrid Journal   (Followers: 8)
Computational Particle Mechanics     Hybrid Journal   (Followers: 1)
Computational Research     Open Access   (Followers: 1)
Computational Science and Discovery     Full-text available via subscription   (Followers: 2)
Computational Science and Techniques     Open Access  
Computational Statistics     Hybrid Journal   (Followers: 14)
Computational Statistics & Data Analysis     Hybrid Journal   (Followers: 30)
Computer     Full-text available via subscription   (Followers: 99)
Computer Aided Surgery     Open Access   (Followers: 6)
Computer Applications in Engineering Education     Hybrid Journal   (Followers: 8)
Computer Communications     Hybrid Journal   (Followers: 16)
Computer Journal     Hybrid Journal   (Followers: 9)

        1 2 3 4 5 6 7 | Last

Journal Cover
Automation in Construction
Journal Prestige (SJR): 1.613
Citation Impact (citeScore): 5
Number of Followers: 7  
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 0926-5805
Published by Elsevier Homepage  [3162 journals]
  • BIM semantic-enrichment for built heritage representation
    • Abstract: Publication date: January 2019Source: Automation in Construction, Volume 97Author(s): Davide Simeone, Stefano Cursi, Marta Acierno In the built heritage context, BIM has shown difficulties in representing and managing the large and complex knowledge related to non-geometrical aspects of the heritage. Within this scope, this paper focuses on a domain-specific semantic-enrichment of BIM methodology, aimed at fulfilling semantic representation requirements of built heritage through Semantic Web technologies. To develop this semantic-enriched BIM approach, this research relies on the integration of a BIM environment with a knowledge base created through information ontologies. The result is knowledge base system - and a prototypal platform - that enhances semantic representation capabilities of BIM application to architectural heritage processes. It solves the issue of knowledge formalization in cultural heritage informative models, favouring a deeper comprehension and interpretation of all the building aspects. Its open structure allows future research to customize, scale and adapt the knowledge base different typologies of artefacts and heritage activities.
  • Data analytics to improve building performance: A critical review
    • Abstract: Publication date: January 2019Source: Automation in Construction, Volume 97Author(s): H. Burak Gunay, Weiming Shen, Guy Newsham The data inherent in building automation systems, computerized maintenance management systems, security and access control systems, and IT networks represent an untapped opportunity to improve the operation and maintenance (O&M) of buildings. This paper reports the findings of a critical review of the literature regarding the use of data analytics in building O&M applications, and a two-day stakeholder's workshop titled Big Data in Building Operations. Building on the discussions at the workshop and the literature survey, the current state of the O&M related decision-making process was identified: the data availability in existing buildings was discussed; the challenges related with accessing and processing these datasets were examined; and emerging sensing technologies were presented. Further, the research fields applying data analytics in O&M were introduced, the barriers to their widespread use in practice were discussed, future work recommendations were developed; and the need for semantic models of O&M data and comprehensive open O&M datasets was identified for the development and assessment of data analytics-driven energy and comfort management algorithms.Graphical abstractUnlabelled Image
  • Compaction quality assessment of rockfill materials using
           roller-integrated acoustic wave detection technique
    • Abstract: Publication date: January 2019Source: Automation in Construction, Volume 97Author(s): Qinglong Zhang, Tianyun Liu, Zhaosheng Zhang, Zehua Huangfu, Qingbin Li, Zaizhan An During conventional earth-rock dam construction, quality control and acceptance (QC/QA) are based on limited spot tests of material density at random locations which may not be representative of the compacted area and may consist of potential bias. Based on roller-integrated acoustic wave detection technique with real-time kinematic global positioning systems, this research adopted sound compaction value (SCV) as a real-time monitoring index for the dam compaction quality, and subsequently proposed an SCV-based assessment method to estimate the compaction quality of rockfill (RF) materials. Additionally, a geostatistical method (Kriging) was adopted to obtain estimates for both SCVs and compaction quality at any location on the work area, thereby calculating the qualified rate of compaction quality for the entire work area. A case study on the Qianping project in China indicates a strong linear correlation between the SCV and the compaction parameters as well as the dry density of RF materials; therefore, it can serve as a reliable index for monitoring compaction quality of RF materials. Assessment of compaction quality on the entire work area can be quickly and continuously achieved using the proposed method, which can effectively overcome the above limitation of conventional testing, timely feedback compaction information to avoid quality defects, and availably improve the construction quality of earth-rock dams.
  • Comparison and utilization of point cloud generated from photogrammetry
           and laser scanning: 3D world model for smart heavy equipment planning
    • Abstract: Publication date: Available online 10 November 2018Source: Automation in ConstructionAuthor(s): Daeyoon Moon, Suwan Chung, Soonwook Kwon, Jongwon Seo, Joonghwan Shin Inaccurate information regarding the terrain in construction projects represents a major challenge to the earthwork process. Both construction quality and productivity have to be addressed by means of efficient construction information management in large earthwork projects in order to ultimately improve the cost-effectiveness of such projects. Research into the technologies for creating precise three-dimensional data and maps of earthwork sites is progressing steadily. These technologies aim to make it possible to conduct unmanned operations, leading to the effective management of earth working equipment. In recent years, as the importance of three-dimensional (3D) shape information management has grown in the construction industry, the research and application of 3D point cloud acquisition methods has likewise increased. The current method for acquiring point cloud data through laser scanning renders it difficult to acquire point clouds in large construction projects, especially in earthwork projects, due to the topographic conditions of the site as well as the physical and material limitations of the laser scanning equipment. In order to overcome and compensate for the limitations of laser scanning, image-processing technology involving unmanned aerial vehicles (UAVs) has been used to acquire point cloud data, although its application has been limited due to its low accuracy. Therefore, this study proposed a method for generating and merging hybrid point cloud data acquired from laser scanning and UAV-based image processing. In addition, a comparison was conducted between the datasets acquired from laser scanning and image processing, using examples from some case studies. Finally, an analytical comparison was performed to verify the accuracy of the UAV-based image processing technology for earthwork projects.
  • Framework for automated UAS-based structural condition assessment of
    • Abstract: Publication date: January 2019Source: Automation in Construction, Volume 97Author(s): Guido Morgenthal, Norman Hallermann, Jens Kersten, Jakob Taraben, Paul Debus, Marcel Helmrich, Volker Rodehorst This paper presents a coherent framework for automated unmanned aircraft system based inspections of large bridges to facilitate an automated condition assessment. Modern camera equipped unmanned aircraft systems are used to generate high-resolution digital image data of the structural surface. The flight path is automatically computed from a basic 3D model and ensures that the image set will satisfy defined quality parameters according to the desired information extraction. State-of-the-art photogrammetry and machine learning based feature detection methods are employed to automatically compute high-resolution geo-referenced 3D structural geometries and to identify typical damage patterns such as cracks. Further framework components dedicated to condition assessment allow the mapping of damages to structural parts and the calibration of mechanical numerical simulation models used to compute the internal structural demand under design loads. Data models are proposed that allow a consistent data storage and management to serve as a basis for all algorithmic components. The application of the framework to a large bridge structure showcases how the integration of digital systems and algorithms forms the basis for an intelligent and potentially autonomous safety assessment of very large infrastructures.
  • A survey of automation-enabled human-in-the-loop systems for
           infrastructure visual inspection
    • Abstract: Publication date: January 2019Source: Automation in Construction, Volume 97Author(s): Sruthy Agnisarman, Snowil Lopes, Kapil Chalil Madathil, Kalyan Piratla, Anand Gramopadhye Routine inspection and maintenance are critical for the proper functioning of civil infrastructures such as bridges, pavements and underground structures. Civil infrastructures are being inspected less frequently because of the high cost and long duration of current inspection procedures. Furthermore, conventional inspection procedures often interrupt the routine functioning of the infrastructure, are inspector-dependent and expose the inspectors to complex and unsafe working environments. Visual inspection technologies play a crucial role in the inspection and maintenance of civil infrastructures. Automation-assisted technologies such as drones and underwater vehicles equipped with multiple imaging and sensing systems have been developed to address some of these issues with the conventional visual inspection processes. This paper reviews peer-reviewed research publications investigating automated visual inspection technologies following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Specifically, 53 publications satisfying a set of inclusion criteria were reviewed, its results highlighting the application domain, the level of autonomy of the automated systems, the sensor technologies used for the inspection process and navigation, the navigation and control technologies and the algorithms used. The review of the articles revealed that the data collected by automation is used to augment the qualitative assessment. Several types of algorithms such as target detection and image enhancing have been developed to reduce the inspector bias in these automated technologies. Path planning algorithms reduce the workload on the inspector by automating the navigation and control tasks. Remotely operated systems reduce the risk to the inspectors by minimizing their exposure to the inspection environment. However, only a limited number of studies investigated the human factors aspects of the automation-assisted inspection process. It is important to understand the cognitive, physical, and temporal demands these technologies place on inspectors to improve the design of systems assisting in the inspection process. Moreover, factors such as automation bias, trust in the system and communication between the automation and the operator need to be investigated. Furthermore, it is important to incorporate appropriate decision aids that support adequate situation awareness in the interface design. Based on these findings this review proposes directions for future research. This review concludes by highlighting the need for human-centered research to develop better solutions for infrastructure inspection problems.
  • Professional development of the BIM actor role
    • Abstract: Publication date: January 2019Source: Automation in Construction, Volume 97Author(s): Petra M. Bosch-Sijtsema, Pernilla Gluch, Ahmet Anil Sezer The implementation of building information modeling (BIM) has resulted in the development of new roles for BIM actors, but few empirical studies have been conducted on how these roles develop professionally. The present study investigates the professional development of the BIM actor and how this role is perceived by BIM actors and non-BIM actors in Sweden. The study uses a questionnaire (N = 342) in eight companies, comprised of contractors, architects, and clients. The BIM and non-BIM actors were compared on similarities and significant differences in their characteristics, tasks, experience, education, and barriers to the role's development. We found that BIM actors perceive their role, characteristics, tasks and education as coordinating and driving change. However, non-BIM actors perceive the BIM actor role as focusing more on technical skills than on softer skills. The perceptions of the two groups indicate possible tensions toward the future professional development of the BIM actor role.
  • An agent-based modeling approach for understanding the effect of
           worker-management interactions on construction workers' safety-related
    • Abstract: Publication date: January 2019Source: Automation in Construction, Volume 97Author(s): Peiyao Zhang, Nan Li, Zhongming Jiang, Dongping Fang, Chimay J. Anumba Pervasive unsafe behaviors of construction workers are the primary cause of accidents on construction job sites. The workers' safety-related behaviors are subject to a variety of factors, such as interactions with coworkers and interventions by management teams. The impacts of these factors have attracted considerable attention in academia but are yet to be fully examined. To provide a quantitative assessment of these impacts and their managerial implications in practice, an agent-based modeling approach of construction safety-related behaviors, which adopts a bottom-up architecture, is proposed in this research. This approach regards safety performance as an emergent property of the behaviors and interactions of construction workers and management teams. The development of the agent-based model is based on a range of theoretical and empirical evidence, including a cognitive analysis model for modeling worker behaviors, and site observations and surveys for the design of simulated working environment and tasks, as well as the modeling of individual behaviors and interactions. Four managerial scenarios are simulated using the proposed approach. Based on the simulation results, the effects of a number of managerial factors on the safety performance of construction workers are examined. These factors include duties of supervisors, the competency and inspection strategy of safety officers, the frequency of safety training, senior managers' involvement in safety activities and emphasis on safety goals. The effects of these factors are examined in a quantitative manner, and relevant implications for construction safety management practice are discussed. The findings prove that agent-based modeling is an effective approach for analyzing the characteristics and patterns of construction safety-related behaviors and assessing possible safety management strategies.
  • Field test of neural-network based automatic bucket-filling algorithm for
    • Abstract: Publication date: January 2019Source: Automation in Construction, Volume 97Author(s): Siddharth Dadhich, Fredrik Sandin, Ulf Bodin, Ulf Andersson, Torbjörn Martinsson Automation of earth-moving industries (construction, mining and quarry) require automatic bucket-filling algorithms for efficient operation of front-end loaders. Autonomous bucket-filling is an open problem since three decades due to difficulties in developing useful earth models (soil, gravel and rock) for automatic control. Operators make use of vision, sound and vestibular feedback to perform the bucket-filling operation with high productivity and fuel efficiency. In this paper, field experiments with a small time-delayed neural network (TDNN) implemented in the bucket control-loop of a Volvo L180H front-end loader filling medium coarse gravel are presented. The total delay time parameter of the TDNN is found to be an important hyperparameter due to the variable delay present in the hydraulics of the wheel-loader. The TDNN network successfully performs the bucket-filling operation after an initial period (100 examples) of imitation learning from an expert operator. The demonstrated solution show only 26% longer bucket-filling time, an improvement over manual tele-operation performance.
  • ND BIM-integrated knowledge-based building management: Inspecting
           post-construction energy efficiency
    • Abstract: Publication date: January 2019Source: Automation in Construction, Volume 97Author(s): Ali GhaffarianHoseini, Tongrui Zhang, Nicola Naismith, Amirhosein GhaffarianHoseini, Dat Tien Doan, Attiq Ur Rehman, Okechukwu Nwadigo, John Tookey Inspection of sustainable performance during post-construction has become increasingly essential. However, conventional operation and maintenance processes are limited with a high probability of inaccurate manual building inspections and the lack of real-time input of dynamic factors. In this regard, engagement of an Integrated Knowledge-based Building Management System using nD BIM applications (nD BIM-IKBMS) is anticipated to promote promising resolutions. The proposed system is expected to provide simulation-based supervisory control while automatically detecting and diagnosing operational faults. Following the literature-based conceptual model developed in our past research, this study aims to generate and verify the proposed framework theoretically, as the second step of nD BIM-IKBMS research and development series, concentrating on functional modeling. Notwithstanding which, it is difficult to describe a cross-disciplinary system following a single framework. As the technology evolves, the framework can not be generalized into new scenarios. Towards this end, the study is followed through the complexity theory lens. A highly flexible framework, considering that each part wherein has an opportunity to evolve, was developed. Following the principles of axiomatic design, the proposed framework was established. Ultimately, this research generates a range of detailed 3-hierarchical Icam DEFinitions for Function Modeling 0 (IDEF0) diagrams outlining the proposed nD BIM-IKBMS structure. This study identifies the interoperability of the proposed framework by standardizing communication protocols, data formats, naming convention, evaluation systems, and modulization. While this research is limited to implementing our nD BIM-IKBMS framework, real-world projects will be utilized in our upcoming steps of R&D series for validation purposes.
  • Geographic capabilities and limitations of Industry Foundation Classes
    • Abstract: Publication date: December 2018Source: Automation in Construction, Volume 96Author(s): Gustaf Uggla, Milan Horemuz Infrastructure design is conducted in a 3D Cartesian coordinate system with the assumption that the Earth is flat and that the scale is constant over the entire project area. Map projections are commonly used to georeference the designed geometries before constructing them on the surface of the Earth. The scale in a map projection varies depending on the position in the map plane, which leads to scale distortions between the designed geometries and the geometries staked out for construction. These distortions are exaggerated for large longitudinal projects such as the construction of roads and railroads because the construction site spans a larger area. Building Information Modeling (BIM) is increasing in popularity as a way to manage information within a construction project. Its use is more widespread in the building industry, but it is currently being adopted by the infrastructure industry as well. The open BIM standard IFC (Industry Foundation Classes) has recently developed support for alignment geometries, and full support for disciplines such as road and railroad construction is underway. This study tests whether the current IFC standard can facilitate georeferencing with sufficiently low distortion for the construction of infrastructure. This is done by performing georeferencing using three different methods, all using the information provided in the IFC schema, and by calculating the scale distortions caused by the different methods. It is concluded that the geographic capabilities of the IFC schema could be improved by adding a separate scale factor for the horizontal plane and support for object-specific map projections.
  • Planning and executing construction inspections with unmanned aerial
    • Abstract: Publication date: December 2018Source: Automation in Construction, Volume 96Author(s): Henk Freimuth, Markus König Unmanned aerial vehicles (UAV) are increasingly recognised as a utility for inspection applications in construction. In order to create measurable benefit over traditional inspection methods, an inspection concept for UAVs must be integrated and automated. This work is the result of an ongoing effort to create a workflow for the structured planning, simulation and execution of inspection tasks. An application was developed that allows the operator to plan inspections in a 3D environment. The application automatically generates collision-free flight paths based on Building Information Modelling (BIM) data. A realistic simulation environment provides a good understanding of the flight dynamics caused by inhibiting factors, such as disturbances of the positioning system. A case study confirms the hypothesis of the inspection concept and hints at technical limitations of autonomous UAVs that need to be overcome in subsequent development efforts.
  • Automatic window detection in facade images
    • Abstract: Publication date: December 2018Source: Automation in Construction, Volume 96Author(s): Marcel Neuhausen, Markus König City models play a major role in urban planning and are indispensable in nowadays civil engineering. The ongoing automation of simulations and analyses demand for increasingly detailed models. Especially windows are of high interest for several tasks. As city models commonly lack any relevant details, these have to be complemented by information about windows from other data sources. In this paper, we propose a pipeline to detect windows in ground view facade images which are rectified before detection. A postprocessing is applied to refine the detections made by a soft cascaded classifier and infer further windows. In experiments we compare our approach to previous work and evaluate the processing steps of our pipeline. Moreover, we show that our entire system yields a detection rate of 95% and a precision of 97% which is satisfying for a proper advancement of existing 3D city models.
  • An auto-deployed model-based fault detection and diagnosis approach for
           Air Handling Units using BIM and Modelica
    • Abstract: Publication date: December 2018Source: Automation in Construction, Volume 96Author(s): Ando Andriamamonjy, Dirk Saelens, Ralf Klein The Air Handling Unit (AHU) is one of the most energy consuming devices in building systems. Fault Detection and Diagnosis (FDD) methods integrated into AHUs can help to ensure that they comply with the intended design, and their efficiency is maintained throughout the entire operational stage of the building. Nonetheless, the implementation and deployment of FDDs at the operational stage require an extensive effort. Especially, FDD approaches that rely on first principle models (model-based FDD) need to be manually implemented, and the information necessary for this process is scattered between several exchange formats and files, thus making it time-consuming, error-prone and subject to modellers' poor judgment.This study aims at facilitating and partially automating the implementation and deployment of model-based FDD. An automated tool-chain that combines a BIM (Building Information Model)-to-BEPS (Building Energy Performance Simulation) tool with a model-based FDD approach is developed. The contribution of this paper lies in the extension of an existing BIM to Modelica BEPS method with an automated calibration approach and a novel model-based FDD. These three elements are integrated in a framework (implemented using Python) to reduce experts' involvement in FDD implementation and deployment. The developed model-based FDD combines a parity relation procedure for fault detection and profile identification for fault diagnosis. The latter uses the robust multi-objective optimisation algorithm NSGA-2. An error is detected when the difference between prediction and measured data over a specific time window is superior to a predefined threshold. The origin of the error is subsequently identified by estimating the profile of the different controllable components' control signal.The developed tool-chain was applied to an actual AHU as well as on several numerical scenarios to identify typical AHU faults such as faulty dampers, valves and sensors. This study shows that the developed model-based FDD approach can identify some of the most common faults in AHUs, but more importantly that BIM can facilitate the deployment of model-based FDD in building systems.
  • Environmental impact assessment of Pinaceae airborne pollen and green
           infrastructure using BIM
    • Abstract: Publication date: December 2018Source: Automation in Construction, Volume 96Author(s): Santiago Fernández-Rodríguez, Juan Pedro Cortés-Pérez, Paloma Prieto Muriel, Rafael Tormo-Molina, José María Maya-Manzano Urban air quality is a parameter that plays a major role in human health at the local scale. Consequently, in urban planning, the behavior and potential risk of allergenicity for some pollen grains coming from ornamental trees and green spaces surrounding newly built buildings, should be considered. This paper aims to study how pollen exposure, influenced by weather parameters, can be assessed and integrated in the designing and building of constructions as other component of air quality assessment beforehand, by using BIM. Based on a comparative aerobiological study at the height over a building (sampled by two traps at ground and at 16 m), a 3D local dynamic parametric scenario was modelled using BIM, and hourly average Pinaceae pollen concentrations (due to the closeness of pine trees to the samplers). From continuous recording (2009–2011) influenced by height and the influence of wind direction and speed was analysed. Additionally, a map of pine trees geolocated around the studied building was produced and the hourly average Pinaceae pollen concentrations were represented by Revit. BIM together with aerobiology can be a novel and useful tool for the construction of buildings considering airborne biological particles. This represents a first step towards the integration of some unusual environmental parameters in urban planning. Pollen grains modelling as an environmental health criterion for the construction of new buildings will allow technicians to avoid possible future isolation points in the design of building envelopes, and high pollen exposure rates could be avoided, creating ‘allergy-free’ buildings.
  • Vision-based integrated mobile robotic system for real-time applications
           in construction
    • Abstract: Publication date: December 2018Source: Automation in Construction, Volume 96Author(s): Khashayar Asadi, Hariharan Ramshankar, Harish Pullagurla, Aishwarya Bhandare, Suraj Shanbhag, Pooja Mehta, Spondon Kundu, Kevin Han, Edgar Lobaton, Tianfu Wu To increase the degree of automation and frequency of data collection for monitoring construction sites, there has been a rapid increase in the number of studies, in the past few years, that developed and/or examined mobile robotic applications in construction. These vision-based platforms capable of autonomous navigation and scene understanding are becoming essential in many construction applications, namely construction sites surveying, work-in-progress monitoring, and existing structure inspection. Simultaneous Localization and Mapping (SLAM) and object recognition for proper context-aware motion planning are some of the core vision techniques that are driving innovation for these robotic systems. To characterize the limitations of current techniques on real-time performance and identify challenges in integration and implementation for construction applications, this paper proposes a mobile robotic platform that incorporates a stack of embedded platforms with integrated Graphical Processing Units (GPUs). This paper presents three case studies to evaluate the performance of the proposed system. The results demonstrate the robustness and feasibility of developing and deploying an autonomous system in the near future.
  • Image-based 3D reconstruction using traditional and UAV datasets for
           analysis of road pavement distress
    • Abstract: Publication date: December 2018Source: Automation in Construction, Volume 96Author(s): Laura Inzerillo, Gaetano Di Mino, Ronald Roberts On local and urban networks, the enduring issue of scarce resources for Maintenance, Rehabilitation, and Reconstruction strategies (MR&R) has led, in many cases, to using unadjusted or poor techniques for road pavement distress detection and analysis, yielding ineffective or even counterproductive results. Therefore, it is necessary to have tools that can carry out quick, reliable and low-cost assessment surveys. This paper aims at validating the use of innovative and low-cost technologies for road pavement analysis, assessing their potentialities for improving the automation and reliability of distress detection. A Structure from Motion (SfM) technique is analyzed at different altitudes. The technique was applied on a distressed road pavement inside the University Campus in Palermo. The models obtained were compared with a terrestrial laser scanned 3D model to analyze the technique's metric accuracy and reliability. The results have shown that the technique accurately replicates pavement distresses, inciting an integrated approach to optimize pavement management strategies.
  • Dynamic geometrical shape measurement and structural analysis of
           inflatable membrane structures using a low-cost three-camera system
    • Abstract: Publication date: December 2018Source: Automation in Construction, Volume 96Author(s): Bing Zhao, Jianhui Hu, Wujun Chen, Jianwen Chen, Zhongliang Jing Inflatable membrane structures have gained considerable popularity in recent years owing to the advantages of light weight, beautiful surface and durability. This paper concerns the dynamic geometrical shape in-situ measurement and structural analysis of inflatable membrane structures using a low-cost three-camera system. The measurement system was developed based on three-dimensional (3D) digital photogrammetry. In order to acquire the high-quality measurement results, accuracy influence factors of the system were evaluated. Based on these considerations, dynamic geometrical shapes of inflatable membrane structures can be real-timely measured through the data processing which is used to determine the 3D coordinates of the target points pasted on membrane surfaces from the two-dimensional (2D) photographs captured by three cameras. Following the in-situ measurement of geometrical shape, the stress distributions of inflatable membrane structures can be determined based on the force equilibriums of membrane surfaces between the in-plane forces and out-of-plane loads. For verifying the proposed method, a selected ethylene-tetrafluoroethylene (ETFE) inflated cushion structure model was manufactured and employed for the experimental study of pre-inflated forming and normal working. By carefully analyzing and comparing the experimental results, it is observed that the measurement accuracy was better than 1/11000. More importantly, the geometrical shape of ETFE cushion still deformed with +5.81% difference during the normal working process of 6 months, resulting in that the maximum stress decreased with −3.49% difference.In general, this paper could provide an efficient and accurate method to measure the dynamic geometrical shapes of inflatable membrane structures, which is the basic information for the further structural analysis.
  • Representing geographical uncertainties of utility location data in 3D
    • Abstract: Publication date: December 2018Source: Automation in Construction, Volume 96Author(s): Léon L. olde Scholtenhuis, Xander den Duijn, Sisi Zlatanova Three-dimensional (3D) uncertainty representations help to avoid ambiguity in the interpretation of utility data. Existing data models and 3D-solutions do not, however, facilitate this adequately yet. They store uncertainties only by means of textual attributes or require stochastic data and expert input to visualize uncertainties. Such data is difficult to obtain in practice. We address this issue by proposing an approach that integrates multiple available utility location datasets to represent geographical uncertainties. To this end, we identified four parameters that practitioners use to store location data – i.e. surveyed, standard, estimated and unknown; and used these to extend the existing CityGML Utility Network ADE model. Next, we calculated and visualized 3D uncertainty buffer shapes for three scenarios that were based on real data of a city district in the Netherlands. The approach may eventually enable engineers to avoid design errors and support utility localization in the field.
  • Wearable insole pressure system for automated detection and classification
           of awkward working postures in construction workers
    • Abstract: Publication date: December 2018Source: Automation in Construction, Volume 96Author(s): Maxwell Fordjour Antwi-Afari, Heng Li, Yantao Yu, Liulin Kong Awkward working postures are the main risk factor for work-related musculoskeletal disorders (WMSDs) causing non-fatal occupational injuries among construction workers. However, it remains a challenge to use existing risk assessment methods for detecting and classifying awkward working postures because these methods are either intrusive or rely on subjective judgment. Therefore, this study developed a novel and non-invasive method to automatically detect and classify awkward working postures based on foot plantar pressure distribution data measured by a wearable insole pressure system. Ten asymptomatic participants performed five different types of awkward working postures (i.e., overhead working, squatting, stooping, semi-squatting, and one-legged kneeling) in a laboratory setting. Four supervised machine learning classifiers (i.e., artificial neural network (ANN), decision tree (DT), K-nearest neighbor (KNN), and support vector machine (SVM)) were used for classification performance using a 0.32 s window size. Cross-validation results showed that the SVM classifier (i.e., the best classifier) obtained a classification performance with an accuracy of 99.70% and a sensitivity of each awkward working posture was above 99.00% at 0.32 s window size. The findings substantiated that it is feasible to use a wearable insole pressure system to identify risk factors for developing WMSDs, and could help safety managers to minimize workers' exposure to awkward working postures.
  • Building energy savings: Analysis of research trends based on text mining
    • Abstract: Publication date: December 2018Source: Automation in Construction, Volume 96Author(s): Zhikun Ding, Zongjie Li, Cheng Fan Building energy saving has become the top concern in achieving global sustainability. In the past decades, massive amounts of academic articles and engineering reports have been published, focusing on the energy conservation throughout the whole building life-cycle. From a macroscopic perspective, these articles provide a comprehensive description on the development of building energy saving measures and technologies. The knowledge discovered from such text data can be used to facilitate the decision-making for researchers and policymakers. Conventional approaches are neither effective nor efficient in analyzing massive text data. As a solution, this study proposes a text mining-based methodology to gain insights from relevant literature on building energy saving. In total, 1600 articles were collected and analyzed at different stages according to important timestamps identified. Various text mining techniques were adopted to identify and describe the research trends. The results present clear differences in research focuses at different stages. An emerging research trend has been identified in the building field, which is related to green buildings, intelligent buildings and low-carbon buildings. The methodology developed in this study can be used as a prototype to enable semi-automated knowledge discovery from massive text data in the building field.
  • Generation and evaluation of excavation schedules for hard rock tunnels in
           preconstruction and construction
    • Abstract: Publication date: December 2018Source: Automation in Construction, Volume 96Author(s): Jung In Kim, Martin Fischer, Calvin Kam Uncertain product characteristics in construction projects make it difficult for planners to develop schedules that reduce expected costs, durations, and associated risks. To overcome these challenges in hard rock tunnel projects, this research introduces a methodology that adapts stochastic programming and feedback control approaches for their excavation. Such approaches require rapid and consistent implementation using up-to-date information provided in a probabilistic manner throughout the entire excavation; therefore, the authors tailored dynamic programming and tunneling risk analysis methods for the methodology to address multiple sets of rock mass properties (RMPs), transitions among excavation methods at the excavation method level, decision-making times, and schedule adjustment policies (SAPs). In preconstruction and construction, the methodology allows construction planners of hard rock tunnels to generate a total-cost-optimal excavation schedule for each set of RMPs and evaluate the excavation costs and durations of schedules for multiple sets of RMPs in a timely and consistent manner by considering SAPs. Further research is required to take into account multiple advances of excavation methods for schedule generation and evaluation.Database subject headingsAutomated schedule generation, hard rock tunnel, uncertainties in rock mass properties, feedback control, stochastic programming, earthwork risk analysis.
  • Dynamic analysis of block offloading using self-propelled modular
    • Abstract: Publication date: December 2018Source: Automation in Construction, Volume 96Author(s): Seung-Ho Ham, Myung-Il Roh A self-propelled modular transporter (SPMT) is a platform vehicle with a large array of wheels that is used for transporting outsized objects. In a shipyard, a number of connected SPMTs are used to move outsized blocks transported from abroad to the floating dock to increase the productivity and reduce the operation time. This operation is called offloading. This study proposes a method of analyzing the offloading operation dynamically. To develop this method, multibody dynamics was used to analyze the motion of the transportation barge, and a floating dock connected by hinge joints was adopted. The modeling of the mechanical parts of the SPMT was also proposed, taking into consideration the axle compensation mechanism to maintain the level of the platform when the SPMT drives over an uneven roadway by lifting up the wheel. Furthermore, a non-interpenetration constraint method between a plane and the cylinder was derived for the collision between the wheels of the SPMT and the decks of the transportation barge and floating dock. The non-interpenetration constraint method was successfully applied to the dynamic analysis of block offloading using SPMTs. From the dynamic analysis, the safety criteria according to the given wave conditions were evaluated.
  • Automated specification of steel reinforcement to support the optimisation
           of RC floors
    • Abstract: Publication date: December 2018Source: Automation in Construction, Volume 96Author(s): S. Eleftheriadis, P. Duffour, B. Stephenson, D. Mumovic A Building Information Modelling (BIM)-enabled computational approach was presented in this paper for the automated specification of steel reinforcement to support the optimisation of reinforced concrete (RC) flat slabs. After importing slab geometries from BIM, the proposed procedure utilised internal forces output from Finite Element Model (FEM) to map required reinforcement in two stages. In the first stage, the reinforcement specifications matched the spatial resolution of the FEM. In the second, the reinforcement was adjusted by imposing constructability functions to limit the number of arrangements in terms of zones and bar spacing. The aim of the paper was to investigate the parametric capabilities of the proposed approach in the context of an optimisation model for the generation of material-efficient structural designs. Numerical examples were presented to demonstrate the efficiency of the automated specification procedure. The material efficiency and the design complexity of the developed reinforcement configurations were also assessed against a conventional solution under realistic design conditions.
  • Formal representation of cost and duration estimates for hard rock tunnel
    • Abstract: Publication date: December 2018Source: Automation in Construction, Volume 96Author(s): Jung In Kim, Martin Fischer, Min Jae Suh Due to the inherent uncertainties of rock mass properties, construction planners of hard rock tunnels have difficulty achieving on-time completion within budget. Despite the potential benefits of adapting stochastic programming and feedback control approaches for decision-making for excavation schedules, the lack of formal representations of the planners' rationales required to estimate the costs and durations of excavation schedules makes the implementation of these approaches extremely challenging. To address these limitations, the authors developed an ontology that represents the estimation rationales (e.g., transition costs and durations among excavation methods, multiple sets of rock mass properties, and schedule adjustment policies). This ontology enables planners to explicitly describe more the comprehensive information required to consistently estimate the costs and durations of excavation schedules for both preconstruction and construction compared to the current practices and the existing studies. Further research that accounts for learning effects resulting from transitions among excavation methods would make cost and duration estimations for excavation schedules more realistic.
  • Dimensional accuracy and structural performance assessment of spatial
           structure components using 3D laser scanning
    • Abstract: Publication date: December 2018Source: Automation in Construction, Volume 96Author(s): Jindian Liu, Qilin Zhang, Jie Wu, Yuchao Zhao Spatial structures have been constructed around the world, and their components have become increasingly complicated. Quality inspection is primarily manually performed and is a labor intensive approach which is prone to error. Developments in 3D laser scanning offer great opportunities to improve the precision and efficiency of quality control operations. This paper presents a new approach for assessing the dimensional accuracy and structural performance of spatial structure elements using 3D laser scanning. The proposed approach establishes a holistic assessment framework by considering the evaluation parameters, optimized scanning strategy, and data processing. A data processing method is developed to automatically calculate and compare the difference between the reverse model and design model based on point cloud processing, reverse modeling, and finite element analysis. A new algorithm is proposed to automatically extract the point cloud boundaries, and it is capable of streamlining the generation of reverse models. The assessment criteria and error control for the framework are quantitatively described in a systematic manner. The proposed approach is tested and demonstrated through an actual project. The results show that using 3D laser scanning for the quality control of spatial structure elements is suitable and validate that applicability of the proposed approach compared to traditional measurement methods.
  • A simulation and visualization-based framework of labor efficiency and
           safety analysis for prevention through design and planning
    • Abstract: Publication date: December 2018Source: Automation in Construction, Volume 96Author(s): Alireza Golabchi, SangUk Han, Simaan AbouRizk Considering the physically demanding nature of manual tasks in the construction industry, an effective approach to mitigating ergonomic risks is to prevent the unsafe working conditions proactively during design and planning, also known as Prevention through Design (PtD). However, there is a lack of approaches for identifying the potential ergonomic risks of a proposed design that can effectively address designers' lack of familiarity with ergonomic risks and understanding of the PtD concept and its implementation. Furthermore, it is difficult to evaluate the impact of ergonomic interventions on productivity and vice versa using available tools. Thus, an integrated approach to PtD is proposed by developing a comprehensive framework that uses simulation modeling, coupled with Predetermined Motion Time Systems (PMTS) and ergonomic and biomechanical assessment, as well as workplace visualization, in order to incorporate both productivity and safety analysis into the design process. The results of implementing the proposed approach indicate its effectiveness in achieving optimum designs in terms of efficiency and safety by evaluating different scenarios of carrying out construction manual operations. The proposed framework also enables evaluating the relationship between safety and productivity from a physical perspective.
  • Automatic pavement defect detection using 3D laser profiling technology
    • Abstract: Publication date: December 2018Source: Automation in Construction, Volume 96Author(s): Dejin Zhang, Qin Zou, Hong Lin, Xin Xu, Li He, Rong Gui, Qingquan Li Asphalt pavement defects e.g. cracks, potholes, rutting, often cause significant safety and economic problems, thus, to automatic detect these defects is vital for pavement maintaining and management. The fact that 3D defect detection methods is superior to traditional 2D methods and manual survey methods in terms of accuracy and comprehensiveness has been widely recognized. Based on 3D laser scanning pavement data, an automatic defect detection method is proposed to detect pavement cracks and pavement deformation defects information simultaneously in this paper. Specifically, a sparse processing algorithm for 3D pavement profiles is first designed to extract crack candidate points and deformations support points, these processing is based on the assumption that the cracks are microscopic local defects while deformations are macroscopic defects in profiles. Then, the crack information can be detected by combining the extracted candidate points and an improved minimum cost spanning tree algorithm. On the other hand, the deformation depth information is acquired based on the profile standard contours which are constructed by profile envelopes and deformation support points, the accurate location and classification information of deformation defects can be obtained by the regional depth property. Experimental tests were conducted using real measured 3D pavement data containing two categories of defects. The experimental results showed that, based on the 3D laser scanning data, the proposed method can effectively detect typical cracks under different road conditions and environments, with the detection accuracy above 98%. Furthermore, different types of deformation defects including potholes, rutting, shoving, subsidence, can also be accurately detected with location error less than 8.7%.
  • Data-driven vision-based inspection for reinforced concrete beams and
           slabs: Quantitative damage and load estimation
    • Abstract: Publication date: December 2018Source: Automation in Construction, Volume 96Author(s): Rouzbeh Davoudi, Gregory R. Miller, J. Nathan Kutz We show that computer-vision-based inspection can relate surface observations to quantitative damage and load level estimates in common reinforced concrete beams and slabs subjected to monotonic loading. This work is related to an earlier study focused on shear-critical beams and slabs (i.e., specimens lacking shear reinforcement), but here an expanded image database has been assembled to include specimens with both flexural and shear reinforcement such as would be found in practice. Using this expanded data set, a supervised machine learning algorithm builds cross-validated predictive models capable of estimating internal loads (i.e., shear and moment) and damage levels based on surface crack pattern images. The expanded data set contains a total of 127 specimens and 862 images captured in past studies across a range of load and damage levels. Textural and geometric attributes of surface crack patterns were used for feature engineering and tuning of predictive models. The expanded data set enables comparison of the estimation accuracy for shear-critical and shear-reinforced beams and slabs considered separately and in combined form. This includes the capability to categorize whether shear reinforcement is present or not. Estimation models based on surface observations for shear-reinforced elements are found to be comparable to those for shear-critical beams and slabs, with variability observed due to loading type range, member geometries, whether categorization is combined with regression, and the image feature sets used.
  • Optimal and near-optimal indoor temperature and humidity controls for
           direct load control and proactive building demand response towards smart
    • Abstract: Publication date: December 2018Source: Automation in Construction, Volume 96Author(s): Rui Tang, Shengwei Wang, Kui Shan Shutting down part of operating chillers directly in central air-conditioning systems of buildings to meet the urgent demand reduction needs of power grids has received increasing attention recently. However, due to limited cooling supply during above demand response (DR) events, the indoor air temperature and particularly relative humidity would often increase to unacceptable levels, resulting in the failures of DR controls. Considering the restriction on power use during DR events, rational use of limited cooling supply turns out to be the inevitable choice. The feedback control strategies commonly-used today cannot properly deal with the environment and system control issues under limited cooling supply during DR events. However, no study on this problem can be found in the research literature. As the first effort, two control strategies (i.e., optimal and near-optimal) are developed to address the environment control issues (concerning both indoor temperature and humidity controls) under a pre-determined power limiting threshold during DR events. The optimal control strategy optimizes the air flow set-points of individual AHUs (air handling units) using model-based prediction and genetic algorithm to achieve the best possible indoor environment control. The near-optimal control strategy approaches such best environment control using a simple empirical method. Case studies are conducted and the results show that the air flow settings have significant impacts on the indoor environment controlled under limited cooling supply. Both control strategies can achieve significant improvements in the indoor temperature and humidity controls as well as significant fan power saving.
  • Spatial compactness metrics and Constrained Voxel Automata development for
           analyzing 3D densification and applying to point clouds: A synthetic
    • Abstract: Publication date: December 2018Source: Automation in Construction, Volume 96Author(s): Sara Shirowzhan, Samad M.E. Sepasgozar, Heng Li, John Trinder A construction operation is known as a complex system whose complicated components can be understood by applying spatial metrics to massive point-based data. Two and three-dimensional compactness metrics are critically reviewed based on the scale of urban modeling, application in urban studies and architecture, and the capability to model spatial and temporal urban changes. This review indicates that there is a lack of a uniform definition of compactness in urban, building and construction studies and a lack of 3D metrics to model spatial and temporal patterns of vertical building developments. To fill these gaps, a new definition of compactness for vertical building developments was developed based on elements of the compactness concept in the literature of urban form in highly dense urban areas by developments of high rise buildings. In addition, spatial data mining methods are suggested for deriving a spatial distribution pattern of building height; a new metric of A* was developed based on 3D Discrete Compactness for comparison of various 3D configurations of the buildings; and Constrained Voxel Automata and volumetric metrics were developed theoretically and proposed for future studies in characterizing spatial and temporal patterns of vertical building developments. It is found that there is a lack of appropriate methodologies to derive the patterns of vertical building development using 3D data such as airborne lidar to meet future needs.Graphical abstractUnlabelled Image
  • BIM-based benchmarking system for healthcare projects: Feasibility study
           and functional requirements
    • Abstract: Publication date: December 2018Source: Automation in Construction, Volume 96Author(s): Jiyong Choi, Fernanda Leite, Daniel P. de Oliveira While project benchmarking based on key performance indicators is regarded as a crucial technique for mature project delivery in the construction industry, incorporating it into an organization's routine is a cumbersome and time-consuming endeavor as it entails considerable time and human efforts for collecting and providing project information, and validating the quality of collected data. To overcome this challenge, this paper introduces an approach that leverages Building Information Modeling (BIM), which allows for a more streamlined benchmarking process. The approach presented in this paper focuses on healthcare projects which have been benchmarked using a comprehensive set of cost, schedule, dimension, and planning performance metrics through a mature sector-specific benchmarking program at Construction Industry Institute (CII). As an initial step in the formulation of such a tool, this paper investigates the potential of leveraging BIM for benchmarking through close scrutiny of contents embedded in real-world models collected from six healthcare projects. Functional requirements were, then, established to realize a BIM-based benchmarking tool for healthcare projects by developing conceptual process flow, use cases, and data flow diagrams. The requirements are further illustrated in mock-ups of a prototype system.
  • Flexible double-cage hoist for high operational efficiency in tall
           building construction
    • Abstract: Publication date: December 2018Source: Automation in Construction, Volume 96Author(s): Taehoon Kim, Ung-Kyun Lee, Seung Woo Kim, Hyunsu Lim, Chang-Won Kim, Hunhee Cho, Kyung-In Kang High operational efficiency in the use of hoists is crucial for the successful completion of high-rise building projects. Simply increasing the number of hoists may not be possible because of insufficient space or budget limits. However, adopting the “double-deck” concept can be an effective approach to improving the operational efficiency of hoists in projects with such constraints. This study proposes a flexible double-cage hoist for use in such projects. In a case study, we found that combining the proposed hoist with existing single-cage hoists enabled a smaller number of hoists to achieve significantly higher efficiency at lower cost than a group of single-cage hoists. The optimal combination showed a 16.6% decrease in total operational costs and a 7.9% increase in operational efficiency. Using the proposed hoist could therefore enable the successful completion of high-rise building projects at lower cost.
  • Optimal facility layout planning for AGV-based modular prefabricated
           manufacturing system
    • Abstract: Publication date: Available online 4 October 2018Source: Automation in ConstructionAuthor(s): Chen Chen, Duc Tran Huy, Lee Kong Tiong, I-Ming Chen, Yiyu Cai Cross-industry learning of the Toyota production system has inspired the precast factories in the construction industry to adopt an automated guided vehicle (AGV)-based flow production system for the manufacturing of their modular prefabricated products. Compared to the production process of automobiles, the manufacturing process of modular prefabricated products is very unbalanced leading to a large pool of queues. And additionally, after some operations, settling is needed. Hence, due to these unique features, facility layout is a crucial element that needs to be well planned in order to achieve a feasible and efficient system. In this regard, this paper proposes an approach to plan the facility layout of the investigated AGV-based modular prefabricated manufacturing system. The paper firstly gives an optimization method for the size arrangement of the workstation area and the storage area. There are two conflicting objectives in the optimization model: one is to minimize the production time and the other is to maximize the workstation utilization. A simulation based non-dominated sorting genetic algorithm is developed to solve the model. Then, the paper proposes a heuristic method to guide the placement, reshuffle, and retrieval of the modular prefabricated products in the storage area. According to the heuristic, there is no need of dedicated paths for AGVs. The storage area can be fully occupied by the work-in-progress and the AGV traveling paths are dynamically generated. And thirdly, the paper is also able to provide a suitable size of the AGV fleet which is able to accomplish the moving tasks in time. The experimental test on an industrial case shows the potential of the proposed planning approach to guide the real practice.
  • From the generation of layouts to the production of construction
           documents: An application in the customization of apartment plans
    • Abstract: Publication date: December 2018Source: Automation in Construction, Volume 96Author(s): Pedro Veloso, Gabriela Celani, Rodrigo Scheeren This paper describes a design customization system that integrates two aspects of Computer-Aided Architectural Design (CAAD) that are usually developed in separate workflows: the algorithmic generation of designs and the detailed representation of the building. The system's workflow starts with the definition of shape grammar rules by an architect. The rules are then automatically imported into a user interface that allows future owners to interactively custom-design their apartment plans. Finally, the plans are automatically converted into detailed Building Information Models (BIM), which allow the architect to add custom finishes, estimate building costs, and automatically generate construction drawings. We conclude that our workflow could contribute to the real customization of houses and other simple architectural programmes, assuring the quality of the outcomes through shape grammars rules and at the same time reducing the cost of production drawings through automation. The paper ends with some suggestions of improvements in BIM software that would allow its integration with shape grammars and the implementation of our workflow in a simpler way.
  • Prioritizing object types for modelling existing industrial facilities
    • Abstract: Publication date: December 2018Source: Automation in Construction, Volume 96Author(s): Eva Agapaki, Graham Miatt, Ioannis Brilakis The cost of modelling existing industrial facilities currently counteracts the benefits these models provide. 90% of the modelling cost is spent on converting point cloud data to 3D models due to the sheer number of Industrial Objects (IOs) of each plant. Hence, cost reduction is only possible by automating modelling. However, automatically classifying millions of IOs is a very hard classification problem due to the very large number of classes and the strong similarities between them. This paper tackles this challenge by (1) discovering the most frequent IOs and (2) measuring the man-hours required for modelling them in a state of the art software, EdgeWise. This allows to measure (a) the Total Labor Hours (TLH) spent per object type and (b) the performance of EdgeWise. We discovered that pipes, electrical conduit and circular hollow sections require 80% of the TLH needed to build the plant model. We showed that EdgeWise achieves cylinder detection with 75% recall and 62% precision. This paper is the first to discover the most laborious to model IOs and the first to evaluate state-of-the-art industrial modelling software. These findings help in better understanding the problem and serve as the foundation for researchers who are interested in solving it.
  • Detect and charge: Machine learning based fully data-driven framework for
           computing overweight vehicle fee for bridges
    • Abstract: Publication date: December 2018Source: Automation in Construction, Volume 96Author(s): Osman Erman Gungor, Imad L. Al-Qadi, Justan Mann This study develops a fully data-driven framework for computing overweight vehicle fee that combines historical bridge data from National Bridge Inventory (NBI) and weigh-in-motion (WIM) data. In this framework, information regarding vehicle weight distribution on bridges was obtained using Gaussian mixture model (GMM) based interpolation. Using this interpolation approach, the vehicle weight distribution on each bridge could be estimated from WIM data based on their location. Later, these estimated distributions were combined with the NBI for developing a machine learning-based prediction model that inputs bridge characteristics (e.g., age and traffic) and outputs deck condition. The model was employed to calculate the expected bridge service life under two scenarios to compute a bridge life reduction per damaging load. Finally, the bridge life cycle cost was conducted to convert the calculated service life difference into a fee. Integration of this framework with existing geographical information system based online permit issuing tools will allow for detection of bridges on vehicles' routes and charge them a fee considering their weight and the load capacity of the bridges they will pass over. Therefore, fees will be calculated more accurately and efficiently. Additionally, the proposed framework has the flexibility of being converted into a table for conforming to the conventional permit fee calculation scheme.
  • Automated detection and classification of construction workers' loss of
           balance events using wearable insole pressure sensors
    • Abstract: Publication date: December 2018Source: Automation in Construction, Volume 96Author(s): Maxwell Fordjour Antwi-Afari, Heng Li, JoonOh Seo, Arnold Yu Lok Wong Fall on the same level is the leading cause of non-fatal injuries in construction workers; however, identifying loss of balance events associated with specific unsafe surface conditions in a timely manner remain challenging. The objective of the current study was to develop a novel method to detect and classify loss of balance events that could lead to falls on the same level by using foot plantar pressure distributions data captured from wearable insole pressure sensors. Ten healthy volunteers participated in experimental trials, simulating four major loss of balance events (e.g., slip, trip, unexpected step-down, and twisted ankle) to collect foot plantar pressure distributions data. Supervised machine learning algorithms were used to learn the unique foot plantar pressure patterns, and then to automatically detect loss of balance events. We compared classification performance by varying window sizes, feature groups and types of classifiers, and the best classification accuracy (97.1%) was achieved when using the Random Forest classifier with all feature groups and a window size of 0.32 s. This study is important to researchers and site managers because it uses foot plantar pressure distribution data to objectively distinguish various potential loss of balance events associated with specific unsafe surface conditions. The proposed approach can allow practitioners to proactively conduct automated fall risk monitoring to minimize the risk of falls on the same level on sites.
  • Glass facade cleaning robot with passive suction cups and self-locking
           trapezoidal lead screw drive
    • Abstract: Publication date: December 2018Source: Automation in Construction, Volume 96Author(s): Thein Than Tun, Mohan Rajesh Elara, Manivannan Kalimuthu, Ayyalusami Vengadesh We report on the mechanism, design iteration, and performance of a new glass facade cleaning robot, vSlider. The passive suction cups, driven by self-locking lead screws, are used to engage the vSlider robot to the glass facade. This mechanism has higher efficiency, compared to active suction cups, and offers better power consumption and safety in the case of power disruption or power loss. Due to the self-locking leadscrews, the counter-moment in a static position is not transferred to the motor, and thus, the servos which drive the lead screws only consume the power needed for a typical free load. A DC motor with encoder generates the primary locomotion in vSlider which was tested both in position- and velocity-control modes. This paper also details the design iteration efforts and discusses the key findings from the experiments involving the first prototype, vSlider 1.x, and the application of these findings in the development of the second prototype, vSlider 2.x. Experiments were performed to validate the proposed design approach and to benchmark the performance of the two robot prototypes that were developed.
  • Automated detection and decomposition of railway tunnels from Mobile Laser
           Scanning Datasets
    • Abstract: Publication date: December 2018Source: Automation in Construction, Volume 96Author(s): A. Sánchez-Rodríguez, B. Riveiro, M. Soilán, L.M. González-deSantos Since the mid-19th century, the railway network has occupied a crucial place at the heart of the world's transport systems. Its infrastructure is often situated in harsh environments where an extreme event, or even daily use, could lead to a catastrophic accident. This is one of the main reasons why inspecting these constructions is so important. Despite the advances in this field, the human component continues to be part of the final inspection process. In order to improve on this, this paper shows the use of laser scanning as a leading technology in automating the inspection of railway infrastructures. The proposed methodologies provide the essential processed and classified data needed for the structural health monitoring of the various assets related to railways. It is divided into three main parts, which pre-process the point cloud, divide the cloud into ground and non-ground points, and detect the elements present in each of these clouds. The methods are validated in three case studies, each containing different railway tunnels. The results demonstrate that laser scanning technology, together with customized processing tools, can provide data for further structural operations with no requirement for either training in geomatics or high-performance computers for the data processing. Significant results are obtained for the developed classification methods: the classification of the tunnel elements returns a global F-Score of between 71 and 99% in a point-by-point comparison. With regard to the labelled rails classification, a global F-Score of 100% is achieved for the analyzed datasets, and between 56 and 73% for the point-by-point classification.
  • Enhancing perceived safety in human–robot collaborative construction
           using immersive virtual environments
    • Abstract: Publication date: December 2018Source: Automation in Construction, Volume 96Author(s): Sangseok You, Jeong-Hwan Kim, SangHyun Lee, Vineet Kamat, Lionel P. Robert Advances in robotics now permit humans to work collaboratively with robots. However, humans often feel unsafe working alongside robots. Our knowledge of how to help humans overcome this issue is limited by two challenges. One, it is difficult, expensive and time-consuming to prototype robots and set up various work situations needed to conduct studies in this area. Two, we lack strong theoretical models to predict and explain perceived safety and its influence on human–robot work collaboration (HRWC). To address these issues, we introduce the Robot Acceptance Safety Model (RASM) and employ immersive virtual environments (IVEs) to examine perceived safety of working on tasks alongside a robot. Results from a between-subjects experiment done in an IVE show that separation of work areas between robots and humans increases perceived safety by promoting team identification and trust in the robot. In addition, the more participants felt it was safe to work with the robot, the more willing they were to work alongside the robot in the future.
  • Camera marker networks for articulated machine pose estimation
    • Abstract: Publication date: December 2018Source: Automation in Construction, Volume 96Author(s): Chen Feng, Vineet R. Kamat, Hubo Cai The pose of an articulated machine includes the position and orientation of not only the machine base (e.g., tracks or wheels), but also its major articulated components (e.g., stick and bucket). To automatically estimate this pose is a crucial component of technical innovations aimed at improving both safety and productivity in many construction tasks. Based on computer vision, an automatic observation and analysis platform using a network of cameras and markers is designed to enable such a capability for articulated machines. To model such a complex system, a theoretical framework termed camera marker network is proposed. A graph abstraction of such a network is developed to both systematically manage observations and constraints, and efficiently find the optimal solution. An uncertainty analysis without time-consuming simulation enables optimization of network configurations to reduce estimation uncertainty, leading to several empirical rules for better camera calibration and pose estimation. Through extensive uncertainty analyses and field experiments, this approach is shown to achieve centimeter level bucket depth tracking accuracy from as far as 15 m away with only two ordinary cameras (1.1 megapixels each) and a few markers, providing a flexible and cost-efficient alternative to other commercial products that use infrastructure dependent sensors like GPS. A working prototype has been tested on several active construction sites confirming the method's effectiveness.Graphical abstractUnlabelled Image
  • Thrust force allocation method for shield tunneling machines under complex
           load conditions
    • Abstract: Publication date: December 2018Source: Automation in Construction, Volume 96Author(s): Kai Guo, Yapeng Xu, Jianfeng Li Thrust system is one of the most important subsystems in the shield tunneling machines. Multiple hydraulic cylinders in the thrust system provide the required thrust force and help to overcome the large load torque in the excavating process, which is a typical over-actuated mechanical system. Therefore, load distribution among thrust cylinders will be uneven in the face of large load torques, which is the primary cause of cracks in lining segments. To handle this problem, most of the existing works focus on thrust system layout optimization for a predetermined geological condition. Consequently, the optimization results cannot adapt to varying thrust load conditions. We aim to provide a more flexible solution for this problem by using a reconfiguration strategy based on a force-on-off principle, which can dynamically determine whether a cylinder in the thrust system outputs force or not. Illustrative examples show that the reconfigurable thrust system ensures more even force distribution among thrust cylinders under varying thrust load conditions compared with the traditional thrust system and does not require extra hardware cost, which verifies the flexibility and effectiveness of the proposed method.
  • Active control of a rod-net formwork system prototype
    • Abstract: Publication date: December 2018Source: Automation in Construction, Volume 96Author(s): A. Liew, Y.R. Stürz, S. Guillaume, T. Van Mele, R.S. Smith, P. Block A prototype rod-net for a fabric formwork system is described, including the fabrication, control of the geometry via turnbuckles, and the measurement of nodal co-ordinates via an image-based theodolite system. Such a net and fabric formwork system consists of a network of tie elements, either discrete or continuous, forming the main falsework structure, onto which is placed a fabric membrane acting as the flexible formwork for the pouring of wet concrete for the forming of a concrete shell. The fabrication of the plastic and steel net components of the prototype is described in detail, including the arrangement of the nodes, rods and boundary conditions. A control system was developed to determine the necessary adjustments at the boundary elements to move the rod-net to a target geometry to eliminate deviations that may arise from fabrication and construction tolerances. This control system showed that with minimal adjustments the rod-net could be directed effectively, resulting in deviations from the target surface reduced from up to 3–9 mm to below 1–2 mm for a 3D rod-net of approximate dimension 2.5 m × 4.5 m × 2.0 m. Additionally, the algorithm provided a more symmetric distribution of deviations around the target. The control system was coupled with 3D point-cloud measurements of markers placed on and around the rod-net by using a motorised image-assisted theodolite and specialised software for spherical and circular targets. This semi-automated process proved to be both efficient and accurate for determining the spatial co-ordinates of the markers and hence the node locations.
  • A unified BIM adoption taxonomy: Conceptual development, empirical
           validation and application
    • Abstract: Publication date: December 2018Source: Automation in Construction, Volume 96Author(s): Ahmed Louay Ahmed, Mohamad Kassem Building Information Modelling (BIM) is an innovation that is transforming practices within the Architectural, Engineering, Construction and Operation (AECO) sectors. Many studies have investigated the process of BIM adoption and diffusion and in particular, the drivers affecting adoption at different levels, ranging from individual and team through organisations and supply chains to whole market level. However, in-depth investigations of the stages of the BIM adoption process and the drivers, factors and determinants affecting such stages are still lacking. A comprehensive classification and integration of adoption drivers and factors is absent as these are disjointedly identified across disparate studies. There is also limited attention to the key terms and concepts (i.e. readiness, implementation, diffusion, adoption) in this area of study.This aim in this paper is twofold: (1) to develop and validate a Unified BIM Adoption Taxonomy (UBAT); and (2) to identify the taxonomy's constructs (i.e. three driver clusters and their 17 factors) that have influence on the first three stages of the BIM adoption process namely, awareness, interest, and decision stages, and compare their effects on each of the stages. The research uses: a systematic literature review and knowledge synthesisation to develop the taxonomy; a confirmatory factor analysis for its validation; and an ordinal logistic regression to test the effect of the UBAT's constructs on the BIM adoption process within the UK Architectural sector using a sample of 177 organisations.The paper is primarily intended to enhance the reader's understanding of the BIM adoption process and the constructs that influence its stages. The taxonomy and its sets of drivers and determinants can be used to perform various analyses of the BIM adoption process, delivering evidence and insights for decision makers within organisations and across whole market when formulating BIM diffusion strategies.
  • Tracked walking mechanism for large hydraulic excavators
    • Abstract: Publication date: December 2018Source: Automation in Construction, Volume 96Author(s): Zhixin Dong, Long Quan, Jing Yang The conventional design methods for tracked walking systems of large hydraulic excavators based on empirical formulas, does not take into account the dynamic load of the track. As such, a safety margin factor has to be adopted to ensure adequate working strength. However, the machine weight will be increased, and the hydraulic system will be overmatched. To address this design issue, an electromechanical–hydraulic design approach based on co-simulation is proposed in this study. The proposed design approach consists of four parts, namely, 1) a terramechanics model of the track that considers the pressure–sinkage relationship and soil shear stress of the individual tracked plate, 2) a tracked multibody dynamics (MBD) model that considers the intermittent transmission between the sprocket and the tracks, 3) the hydraulic systems model, and 4) the data communication interface. To demonstrate the proposed approach, it was used to design a large hydraulic excavator with a bucket capacity of 15 m3. Experimental results from the prototype showed that the proposed design principle can accurately reflect the impact load and periodic torque fluctuations on the track. The maximum error between the simulated and experimental results is 5.4% in forward walking and 12.7% in backward walking, thus demonstrating the effectiveness and accuracy of the proposed design approach.Graphical abstractUnlabelled Image
  • Self-correcting neural network in road pavement diagnostics
    • Abstract: Publication date: December 2018Source: Automation in Construction, Volume 96Author(s): Marcin Staniek, Piotr Czech The paper focuses on application of the self-correcting neural network in the process of road pavement diagnostics. It provides a discussion on a method of road inspection based on the proposed neural network solution and a measuring station which uses stereo vision of road pavement. The solution proposed was verified in real-life conditions, i.e. in a road with different types of pavement defects. With reference to a comparison of the results, a typical approach to estimation of disparity maps based on matching measures has been limited in favour of the solution in question. Both the results thus obtained and statistical analysis have confirmed legitimacy of the solution devised by the authors.
  • Learning method for knowledge retention in CBR cost models
    • Abstract: Publication date: December 2018Source: Automation in Construction, Volume 96Author(s): Sae-Hyun Ji, Joseph Ahn, Eul-Bum Lee, Yonggu Kim The case-based reasoning methodology fundamentally relies on historical cases to solve new problems. Supplementing insufficient data by the reproduction of appropriate values can mitigate the potential negative effects on the solutions resulting from sudden changes. However, CBR researchers have rarely examined this issue. To address this challenge, this research proposes a learning method for knowledge retention based on CBR by applying a data-mining approach to manage missing dataset values. A case study on a 164-apartment project was conducted to compare the estimation accuracy of the suggested learning method to that of past research with the same experiment conditions. The learning method with the CBR model achieved higher accuracy of the overall cost estimation and higher stability compared with the previous model. This research shows how cases can be generated and retained as learned cases to overcome the difficulties of continuous updates in a wide range of construction projects, as well as why the case bases need to be continuously updated. The research outcomes could support work related to cost estimation for decision makers ranging from beginners to experts in both academia and industry.
  • Assessing the impacts of mobile technology on public transportation
           project inspection
    • Abstract: Publication date: December 2018Source: Automation in Construction, Volume 96Author(s): Julian Yamaura, Stephen T. Muench Advancements in mobile technology capabilities and affordability allow many Departments of Transportation (DOT) the opportunity to use these technologies to improve the time-consuming nature of collecting, documenting, and distributing project inspection information. A mobile technology system for project inspection, called HeadLight, is piloted with the Washington, Minnesota, and Texas Departments of Transportation on 31 projects over a 3-month time span. Field measurements and interviews are used to quantify improvements offered by mobile technology over current practice. This empirical data is evaluated using standard software and process change evaluation metrics: time savings, data volume, data variety, data completeness, data timeliness, and data availability. Results indicate that project inspectors using the mobile technology system experienced productivity gains on the order of 25%, collected and shared twice as many observations, and improved the timeliness of daily reports and overall data availability. Additionally, the mobile technology solution is found to enable more complete and consistent data, improved accessibility throughout a project office and DOT. All these outcomes indicate mobile technology for project inspection allows the inspection workforce to work more efficiently. Further study into improved data quality and availability may identify more impacts within the construction inspection process and to a DOT's decision making processes.
  • Modeling and problem solving of building defects using point clouds and
           enhanced case-based reasoning
    • Abstract: Publication date: December 2018Source: Automation in Construction, Volume 96Author(s): Zhao Xu, Suhao Li, Heng Li, Qiming Li Many factors, including improper maintenance and material aging, may lead to the occurrence of defects during the operation of the various functions of buildings. Building defect information is normally stored in a discrete and unstructured way, and for this reason, building a case-based reasoning framework regarding building defects to enhance the level of building maintenance management has become an important field in the related research. At present, there is limited research available on the integration of geometric data models that are built by means of scanning and multi-attribute selection strategies. This study proposes an integrated information management framework for superficial defects in buildings, which is compatible with a point clouds model as a central data source. It features the attributes of defects used in multi-criteria decision analysis. A CBR (case-based reasoning) approach that considers case-based distance is used to enhance the performance of similarity calculations and case retrieval. A case-based distance model is utilized for the data processing stage and concentrates on a smaller case set that contains best alternatives. The potential benefit offered by this approach is that more efficient results can be obtained from classified cases during the retrieval phase process. A comparison of a CBR query with ungrouped sample data is performed to establish patterns to verify the effectiveness of the calculation method of determining case similarity, which is supported by the pre-processing of classified information about the building defects. The analytical results show that the proposed method performs well in solving the multi-attribute classification of building defects and avoiding ambiguous answers retrieved from unrelated subsets. This approach might be capable of investigating the practical problems involved in building maintenance in the AEC domains.
  • Automatic segmentation of 3D point clouds of rubble masonry walls, and its
           application to building surveying, repair and maintenance
    • Abstract: Publication date: December 2018Source: Automation in Construction, Volume 96Author(s): Enrique Valero, Frédéric Bosché, Alan Forster Changing climatic conditions are contributing to faster deterioration of building fabric. Increasing number of heavy rainfall events can particularly affect historic and Cultural Heritage (CH) buildings. These evolving and uncertain circumstances demand more frequent survey of building fabric to ensure satisfactory repair and maintenance. However, traditional fabric surveys have been shown to lack efficiency, accuracy and objectivity, hindering essential repair operations. The recent development of reality capture technologies, together with the development of algorithms to effectively process the acquired data, offers the promise of transformation of surveying methods.This paper presents an original algorithm for automatic segmentation of individual masonry units and mortar regions in digitised rubble stone constructions, using geometrical and colour data acquired by Terrestrial Laser Scanning (TLS) devices. The algorithm is based on the 2D Continuous Wavelet Transform (CWT), and uniquely it does not require the wall to be flat or plumb. This characteristic is important because historic structures, in particular, commonly present non-negligible levels of bow, waviness and out-of-verticality.The method is validated through experiments undertaken using data from two relevant and highly significant Scottish CH buildings. The value of such segmentation to building surveying and maintenance regimes is also further demonstrated with application in automated and accurate measurement of mortar recess and pinning. Overall, the results demonstrate the value of the automatic segmentation of masonry units towards more comprehensive and accurate surveys.
  • Sustainable Infrastructure Multi-Criteria Preference Assessment of
           Alternatives for Early Design
    • Abstract: Publication date: December 2018Source: Automation in Construction, Volume 96Author(s): Yasaman Shahtaheri, Madeleine M. Flint, Jesús M. de la Garza The tradeoffs between the economic, social, and environmental aspects of infrastructures are not easily evident to decision makers and stakeholders in the initial design phase. This lack of insight, often leads to designs that compromise the social and environmental aspects of designs in order to reduce the initial construction costs of infrastructure assets. In addition to the lack of insight, currently available methods for analyzing alternative infrastructure configurations with respect to decision maker preferences: require analysis on a case-by-case (e.g., pairwise) basis; are not appropriate for the initial design phase (e.g., are time-consuming); and are not adaptable to a range of alternative design solutions (e.g., adding and removing alternatives might require a re-ranking from the decision maker). This paper presents a modular preference function development strategy that aims to address these issues, termed Sustainable Infrastructure Multi-Criteria Preference assessment of aLternatives for Early Design (SIMPLE-Design). The proposed strategy develops utility functions (indifference curves) for assessing decision maker preferences with regard to various tradeoffs of alternative design options, and leverages available data to provide decision makers with a consistent frame of reference for assessing alternatives. An illustration presented for a decision support tool using the Simple-Design strategy assesses decision maker preferences for commercial buildings with respect to initial construction costs, building damage and business interruption costs, casualty costs (due to the occurrence of natural hazard events), and CO2 emission costs. The designed decision support tool provides streamlined information to support preference assessment with reasonably low cognitive load. Ten out of the twelve decision support tool users stated that allowing the decision makers to define alternatives of equal utility (value) in a systematic manner, and providing information on the various cost types (decision criteria), are the most essential elements of the assessment strategy. The presented modular preference assessment framework, as well as the decision support tool itself, are generalizable and can be adapted to other infrastructure types. The contribution to the body of knowledge is a holistic preference assessment framework that allows decision makers to make more informed decisions—and designers to better incorporate the preferences of the decision makers—during the early design process.
  • Supporting constructability analysis meetings with Immersive Virtual
           Reality-based collaborative BIM 4D simulation
    • Abstract: Publication date: December 2018Source: Automation in Construction, Volume 96Author(s): Conrad Boton Immersive Virtual Reality-based collaborative BIM 4D simulation can offer a unique, supportive environment for conducting constructability analysis meetings in the construction industry. While many research works have addressed various aspects of VR-based 4D simulation, there is still no comprehensive and neutral framework to help both practitioners and experts to identify the main challenges to address. This paper proposes four main complementary steps with which to define the VR environment, to develop the 4D model, to prepare and transfer the model in the VR system and to conduct constructability analysis meeting. In the current state of the framework, the 4D-based constructability analysis is more about the collaborative use of 4D rather than the collaborative generation and interaction with the 4D model. Each step of the framework is supported by appropriate methods and tools. A collaborative personas-based case study helps to evaluate the framework and to show how it can be used. Compared to recent related works, the proposed framework is more structured and comprehensive, providing a structured approach using concepts from multiple scientific areas.
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