Abstract
This paper aims to further enhance the accuracy and efficiency of large bridge structural health monitoring (SHM) through noncontact remote sensing (NRS). For these purposes, the authors put forward an intelligent NRS method that collects the holographic geometric deformation of the test bridge, using the static image sequences. Specifically, a uniaxial automatic cruise acquisition device was designed to collect the dynamic and static images on bridge facade under different damage conditions. Considering the strong spatiotemporal correlations of the sequence data, the relationships between the time history images in six fixed fields of view were identified through deep learning under spatiotemporal sequences. On this basis, the behavioral features of the bridge structure were obtained under vehicle load. Finally, the global holographic deformation of the test bridge and the envelope spectrum of the global holographic deformation were derived from the deformation data. The research results show that the output data of our NRS method were basically consistent with the finite‐element prediction (maximum error: 11.11%) and dial gauge measurement (maximum error: 12.12%); the NRS method is highly sensitive to the actual deformation of the bridge structure under different damage conditions and can capture the deformation in a continuous and accurate manner. Compared with the limited number of measuring points, holographic deformation data also shows higher sensitivity in damage identification.
Highlights
With the elapse of time, it is inevitable for a bridge to face structural degradation under the long-term effects of natural factors
Considering the advantages of deep learning in feature extraction over traditional machine learning, this paper obtains the structural deformations of the bridge and the behavioral features of the bridge structure in the same field of view and different time sequences through the deep learning of the grayscales and contours in the images collected by the intelligent noncontact remote sensing (NRS) system
The geometric deformation of a reduced-scale model for a 24 m span self-anchored suspension bridge under multiple damage conditions was captured with the NRS method, and the global behavioral features of the test bridge were identified using the 3D convolutional neural network (CNN) algorithm. e results were compared with finite-element prediction and dial gauge measurement. e main conclusions are as follows: (1) A fixed point uniaxial automatic cruise acquisition device was designed to collect the dynamic and static images on bridge facade under different damage conditions. en, the spatiotemporal sequences of static images were processed by Matlab edge function, Canny edge detector, and 3D CNN
Summary
With the elapse of time, it is inevitable for a bridge to face structural degradation under the long-term effects of natural factors (e.g., climate and environment). E main functions are to monitor the state and behavior of the bridge structure, while tracking and recording the environmental conditions On the upside, these systems have high local accuracy, run on an intelligent system, and support long-term continuous observation. Considering the defect of the contact sensors in traditional SHM systems (i.e., the observed data are discrete due to the limited number of sensors), the noncontact remote sensing (NRS) was adopted to fully capture the exact global damage and local damage of the structure and meet the engineering requirement on measurement accuracy and low cost
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