Journal
AUTOMATION IN CONSTRUCTION
Volume 125, Issue -, Pages -Publisher
ELSEVIER
DOI: 10.1016/j.autcon.2021.103620
Keywords
Offsite construction; Construction automation; Computer vision; Productivity; Machine learning; Task efficiency
Funding
- Natural Sciences and Engineering Research Council of Canada [IRCPJ 41914515]
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Offsite construction is a method focused on improving productivity by moving construction tasks to manufacturing facilities. This paper presents a novel approach that combines deep learning algorithms and finite state machines to automatically detect and track the progress of construction operations.
Offsite construction is an approach focused on moving construction tasks from traditional jobsites to manufacturing facilities. Improved productivity of construction tasks is paramount in terms of competitiveness and is achieved through the continuous improvement of operations and planning, which often relies on historical data obtained from previous projects. Despite being a common practice, current methods, such as time studies, are not able to capture the changing scenarios resulting from improvements to production. This paper presents a novel approach to automatically detect and track the progress of construction operations by applying a method that combines deep learning algorithms and finite state machines to existing footage captured by closed-circuit television (CCTV) security cameras. Applied in the context of floor panel manufacturing stations, the proposed method examines entire production days recorded by CCTV cameras, while providing the durations of each task, its required resources, and the task efficiency per panel with high accuracy.
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