This paper aims to further enhance the accuracy and efficiency of large bridge structural health monitoring (SHM) through\nnoncontact remote sensing (NRS). For these purposes, the authors put forward an intelligent NRS method that collects the\nholographic geometric deformation of the test bridge, using the static image sequences. Specifically, a uniaxial automatic cruise\nacquisition device was designed to collect the dynamic and static images on bridge facade under different damage conditions.\nConsidering the strong spatiotemporal correlations of the sequence data, the relationships between the time history images in six\nfixed fields of view were identified through deep learning under spatiotemporal sequences. On this basis, the behavioral features of\nthe bridge structure were obtained under vehicle load. Finally, the global holographic deformation of the test bridge and the\nenvelope spectrum of the global holographic deformation were derived from the deformation data. The research results show that\nthe output data of our NRS method were basically consistent with the finite-element prediction (maximum error: 11.11%) and\ndial gauge measurement (maximum error: 12.12%); the NRS method is highly sensitive to the actual deformation of the\nbridge structure under different damage conditions and can capture the deformation in a continuous and accurate manner.\nCompared with the limited number of measuring points, holographic deformation data also shows higher sensitivity in\ndamage identification.
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