Carlos Tello-Gil, S. Jabari, Lloyd M. Waugh, Mark Masry, Jared McGinn
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引用次数: 0
Abstract
This paper addresses the crucial need for effective crack detection and dimensional assessment in civil infrastructure materials to ensure safety and functionality. It proposes a cost-effective solution for crack detection and dimensional assessment by applying state-of-the-art deep learning on smartphone sensor imagery and positioning data. The proposed methodology integrates 3D data from LiDAR sensors with Mask R-CNN and YOLOv8 object detection networks, for automated crack detection in concrete structures, allowing for accurate measurement of crack dimensions, including length, width, and area. The calculated crack-straight-length closely aligns with the ground-truth straight-length, with an average error of 1.5%. This research has the potential to advance concrete infrastructure inspection, bridge knowledge gaps, and contribute to innovative solutions for precise structural integrity assessment and maintenance.
期刊介绍:
The Canadian Journal of Civil Engineering is the official journal of the Canadian Society for Civil Engineering. It contains articles on environmental engineering, hydrotechnical engineering, structural engineering, construction engineering, engineering mechanics, engineering materials, and history of civil engineering. Contributors include recognized researchers and practitioners in industry, government, and academia. New developments in engineering design and construction are also featured.