Integrating BIM and Multiple Construction Monitoring Technologies for Acquisition of Project Status Information
Publication: Journal of Construction Engineering and Management
Volume 149, Issue 7
Abstract
Although novel construction tech systems have been developed to monitor construction sites, each collects data in isolation. We present an event processing model (EPM) that applies multiple monitoring technologies in parallel and a set of algorithms to interpret data from the multiple streams for operations control. In a full-scale lab experiment, multiple monitoring systems were applied to monitor workers as they built drywall partitions with a variety of systems and finishes. Data from the different streams were merged to establish what work was done, by whom, where, and for how long. The results suggest that collecting, merging, and interpreting diverse data streams using a multiple monitoring technologies system on-site can provide more complete and more accurate project status information than can be obtained from the use of any individual technology. This is essential for the automation of project progress monitoring, which is important in automating production planning and control tasks.
Practical Applications
Modern construction management approaches need more accurate, more reliable, and automated construction-progress monitoring. Manual monitoring is complicated and expensive in large, complex projects. It is difficult to perform the production control process successfully for whole projects with many related trades and resources. Automating data monitoring, data collection, and computing can reduce errors and increase efficiency compared to manual approaches. This paper presents a fully integrated system to manage production information through the construction life cycle. Different types of construction technologies could be integrated, along with a building information modeling (BIM) environment, into one common data-management system to report actual work progress and events completed on-site. The proposed system would support the decision-making process as part of construction planning in large and complex projects by providing accurate real-time information defining what work was done, by whom, in which location, and for how long. The information can be tailored to suit planners’ needs at four different levels: (1) work package, (2) task, (3) daily recording, and (4) building element. Broadly speaking, this paper presents a digital twin construction (DTC) framework to integrate data transfer and monitoring from field-to-BIM and from BIM-to-field for automated, accurate, and intelligent production control. This system can help planners improve look-ahead planning (LAP) and decision-making processes.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
This research was supported in part by the Israel Ministry of Science and Technology under Grant No. 3-15651. The presented audio detection model in the “Audio Data Stream” section is an outcome of the research cooperation between the Virtual Construction Lab (VCLab) and the S.M.A.R.T. Construction Research Group from the Israel Institute Technology (Technion) and New York University Abu Dhabi (NYUAD), respectively. Thanks to Dr. Borja García de Soto from S.M.A.R.T. Construction Research Group, Division of Engineering (NYUAD), Mr. Pi Ko from S.M.A.R.T. lab (NYUAD) for his efforts in audio data labeling, and Mr. Karunakar Mannem from the Data science department (NYUAD) for his efforts in model coding. The authors thank Buildots, VisiLean, and Genda for their support for data collection. Also, thanks to Mr. Shammas Sulaiman from DSI Solutions (UAE) and Mr. Hazem Kahla from AskBIM (US) for their efforts in BIM model preparation.
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Journal of Construction Engineering and Management
Volume 149 • Issue 7 • July 2023
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© 2023 American Society of Civil Engineers.
History
Received: Jun 22, 2022
Accepted: Nov 16, 2022
Published online: Apr 29, 2023
Published in print: Jul 1, 2023
Discussion open until: Sep 29, 2023
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