Computer Vision–Based Disruption Management for Prefabricated Building Construction Schedule
Publication: Journal of Computing in Civil Engineering
Volume 35, Issue 6
Abstract
Prefabricated building construction (PBC) projects are prone to schedule delays and budget overruns due to disruptions. Hence it is necessary to timely detect and evaluate disruptions and respond in order to put a disrupted construction project back on track. This study focused on computer vision–based (CVB) disruption management for PBC schedules. First, real-time and nonintrusive detection of four types of disruptions on PBC jobsite was achieved using CVB technology. Second, the detected disruptions of the PBC schedule were evaluated by quantifying their impacts on the project. Third, repair of the disrupted schedule was achieved based on the principle of disruption management by developing an optimization model with the objectives of minimizing the repair cost and deviation from the original schedule. The proposed disruption management system was illustrated and justified through a field application. This study is expected to provide a digital methodology to achieve digital disruption management for the PBC schedule.
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Data Availability Statement
The models and codes for schedule repair and object detection of construction trucks, prefabricated components, and worker posture that support the findings of this study are available from the corresponding author upon reasonable request. Other processed data used in this study cannot be shared at this time because the data form part of an ongoing study.
Acknowledgments
This project is supported by the National Natural Science Foundation of China (Project No. 71972167). The authors also acknowledge the assistances of Zhejiang Construction Engineering Group and Zhongtian Construction Group in taking the photographs used in the paper.
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Journal of Computing in Civil Engineering
Volume 35 • Issue 6 • November 2021
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© 2021 American Society of Civil Engineers.
History
Received: Jan 5, 2021
Accepted: Jun 22, 2021
Published online: Sep 7, 2021
Published in print: Nov 1, 2021
Discussion open until: Feb 7, 2022
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