Automated Construction Progress Monitoring of Partially Completed Building Elements Leveraging Geometry Modeling and Appearance Detection with Deep Learning
Publication: Construction Research Congress 2022
ABSTRACT
The exponential growth of on-site visual data and the advent of computer vision techniques have created a unique opportunity to improve automated construction progress monitoring methods. To date, the state-of-the-art vision-based methods are capable of reporting the progress of a building element in terms of binary function. However, for better schedule control and micro-level monitoring, it is necessary to report the partial completion of tasks associated with an element. This research proposes a novel approach for computing and reporting the partial progress of tasks in terms of completion percentage using the on-site visual data, 4D BIM, and deep-learning-based computer vision algorithms. The approach leverages geometry modeling and appearance detection to automatically calculate the percentage completion of tasks associated with each element. The proposed approach is applied to a building construction project, and the preliminary results demonstrate its applicability to generate completion percentage per task in the lookahead schedule for accurate daily progress report generation.
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Construction Research Congress 2022
Pages: 708 - 717
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Published online: Mar 7, 2022
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Cited by
- Seau Chen Houng, Aritra Pal, Jacob J. Lin, 4D BIM and Reality Model–Driven Camera Placement Optimization for Construction Monitoring, Journal of Construction Engineering and Management, 10.1061/JCEMD4.COENG-14600, 150, 6, (2024).