Abstracts – Browse Results
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Birgonul, Z (2021) A receptive-responsive tool for customizing occupant's thermal comfort and maximizing energy efficiency by blending BIM data with real-time information. Smart and Sustainable Built Environment, 10(3), 504-35.
Brandín, R and Abrishami, S (2021) Information traceability platforms for asset data lifecycle: blockchain-based technologies. Smart and Sustainable Built Environment, 10(3), 364-86.
Eiris, R, Albeaino, G, Gheisari, M, Benda, W and Faris, R (2021) InDrone: a 2D-based drone flight behavior visualization platform for indoor building inspection. Smart and Sustainable Built Environment, 10(3), 438-56.
Faris, E, Matarneh, S, Talebi, S, Kagioglou, M, Hosseini, M R and Abrishami, S (2021) Toward digitalization in the construction industry with immersive and drones technologies: a critical literature review. Smart and Sustainable Built Environment, 10(3), 345-63.
Hosseini, M R, Jupp, J, Papadonikolaki, E, Mumford, T, Joske, W and Nikmehr, B (2021) Position paper: digital engineering and building information modelling in Australia. Smart and Sustainable Built Environment, 10(3), 331-44.
Karsten Winther, J, Nielsen, R, Schultz, C and Teizer, J (2021) Automated activity and progress analysis based on non-monotonic reasoning of construction operations. Smart and Sustainable Built Environment, 10(3), 457-86.
- Type: Journal Article
- Keywords: abductive reasoning; constraint programming; building information modeling; real-time location sensing; personal protective equipment; artificial intelligence; radio frequency identification; unmanned aerial vehicles; automation; construction sites; lean
- ISBN/ISSN:
- URL: http://dx.doi.org/10.1108/SASBE-03-2021-0044
- Abstract:
Real-time location sensing (RTLS) systems offer a significant potential to advance the management of construction processes by potentially providing real-time access to the locations of workers and equipment. Many location-sensing technologies tend to perform poorly for indoor work environments and generate large data sets that are somewhat difficult to process in a meaningful way. Unfortunately, little is still known regarding the practical benefits of converting raw worker tracking data into meaningful information about construction project progress, effectively impeding widespread adoption in construction. The presented framework is designed to automate as many steps as possible, aiming to avoid manual procedures that significantly increase the time between progress estimation updates. The authors apply simple location tracking sensor data that does not require personal handling, to ensure continuous data acquisition. They use a generic and non-site-specific knowledge base (KB) created through domain expert interviews. The sensor data and KB are analyzed in an abductive reasoning framework implemented in Answer Set Programming (extended to support spatial and temporal reasoning), a logic programming paradigm developed within the artificial intelligence domain. This work demonstrates how abductive reasoning can be applied to automatically generate rich and qualitative information about activities that have been carried out on a construction site. These activities are subsequently used for reasoning about the progress of the construction project. Our framework delivers an upper bound on project progress (“optimistic estimates”) within a practical amount of time, in the order of seconds. The target user group is construction management by providing project planning decision support. The KB developed for this early-stage research does not encapsulate an exhaustive body of domain expert knowledge. Instead, it consists of excerpts of activities in the analyzed construction site. The KB is developed to be non-site-specific, but it is not validated as the performed experiments were carried out on one single construction site. The presented work enables automated processing of simple location tracking sensor data, which provides construction management with detailed insight into construction site progress without performing labor-intensive procedures common nowadays. While automated progress estimation and activity recognition in construction have been studied for some time, the authors approach it differently. Instead of expensive equipment, manually acquired, information-rich sensor data, the authors apply simple data, domain knowledge and a logical reasoning system for which the results are promising.
Lamptey, T, De-Graft, O-M, Acheampong, A, Adesi, M and Ghansah, F A (2021) A framework for the adoption of green business models in the Ghanaian construction industry. Smart and Sustainable Built Environment, 10(3), 536-53.
Mahmoudi, E, Stepien, M and König, M (2021) Optimisation of geotechnical surveys using a BIM-based geostatistical analysis. Smart and Sustainable Built Environment, 10(3), 420-37.
Oke, A E and Arowoiya, V A (2021) Evaluation of internet of things (IoT) application areas for sustainable construction. Smart and Sustainable Built Environment, 10(3), 387-402.
Xiong, R and Tang, P (2021) Machine learning using synthetic images for detecting dust emissions on construction sites. Smart and Sustainable Built Environment, 10(3), 487-503.