Abstract
The rapid evolution of cameras and drones in the past few years has paved a way for image-based inspection and monitoring of buildings and other structures. This study presents a framework for the development of an automated image-based building inspection and monitoring system. Images acquired from multiple locations of the building can be used to construct a 3D model or a 2D elevation view which is then matched to its BIM (Building Information Modeling) model. The image of each structural member and its dimensions obtained from the matched model is fed to an image processing algorithm which detects cracks in concrete surfaces and measures crack parameters. A machine learning algorithm trained on several synthetic crack scenarios automatically predicts severity of each crack and the corrective action to be taken for maintenance. The detected cracks are color coded and the severity is mapped back to the BIM model so that the current structural state can be effectively visualized. Using several images of real structural members, it is demonstrated that the crack analysis system shows fairly accurate results. Apart from being a smart and convenient tool for structural inspection, the developed framework also results in better operations, planning and facility management.
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Mishra, E., Anwar, N., Izhar, M.A., Supprasert, S. (2021). Image Based Inspection and Monitoring of Buildings. In: Ahmed, S.M., Hampton, P., Azhar, S., D. Saul, A. (eds) Collaboration and Integration in Construction, Engineering, Management and Technology. Advances in Science, Technology & Innovation. Springer, Cham. https://doi.org/10.1007/978-3-030-48465-1_18
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DOI: https://doi.org/10.1007/978-3-030-48465-1_18
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