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• W4387167989 abstract "Ancient bridges lack adequate maintenance strategies and public attention compared to modern bridges. The current bridge maintenance standards are tailored for modern bridges and cannot be directly applied to ancient bridge maintenance because of differences in structure designs and construction materials. Besides, due to the urban development and the evolution of traffic, the frequency of using the ancient bridges has tapered off; people gradually elided the maintenance of ancient bridges. Nevertheless, some ancient bridges still serve as integral hubs in the transportation network and require more inspection due to their common features of aging structures and complex damage history. Previous studies have mainly applied sensor-based analysis for structural deformation problems in ancient bridge health monitoring. The mainstream inspection technologies include sonic transmission, radiography, infrared thermography, and ground-penetrating radar (GPR). However, these methods can only partially depict the interior condition of the bridge, and are time-consuming and complicated to implement in practice; their feasibility on ancient bridge maintenance is debatable. This paper proposes an image-based detection method to provide an effective solution for the maintenance of ancient bridges using Deep Neural Networks (DNNs). A masonry arch bridge in Hong Kong, built in the 1880s, was investigated. Unmanned Aerial Vehicles (UAVs) were deployed to collect the bridge surface information, and a 3D model generated with Structure from Motion (SfM) was preserved for further bridge health monitoring. In addition, an assessment criterion was purposed to evaluate the ancient bridge health condition, which is beneficial for the decision-making on ancient bridge maintenance." @default.
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• W4387167989 date "2023-09-30" @default.
• W4387167989 modified "2023-09-30" @default.
• W4387167989 title "UAV Image-Based Defect Detection for Ancient Bridge Maintenance" @default.
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• W4387167989 doi "https://doi.org/10.1007/978-3-031-35399-4_1" @default.
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