Smart Structures and Systems

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Target-free vision-based approach for vibration measurement and damage identification of truss bridges

  • Dong Tan (Centre for Infrastructural Monitoring and Protection, School of Civil and Mechanical Engineering, Curtin University) ;
  • Zhenghao Ding (Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University) ;
  • Jun Li (Centre for Infrastructural Monitoring and Protection, School of Civil and Mechanical Engineering, Curtin University) ;
  • Hong Hao (Centre for Infrastructural Monitoring and Protection, School of Civil and Mechanical Engineering, Curtin University)
  • Received : 2022.08.26
  • Accepted : 2023.02.02
  • Published : 2023.04.25
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Abstract

This paper presents a vibration displacement measurement and damage identification method for a space truss structure from its vibration videos. Features from Accelerated Segment Test (FAST) algorithm is combined with adaptive threshold strategy to detect the feature points of high quality within the Region of Interest (ROI), around each node of the truss structure. Then these points are tracked by Kanade-Lucas-Tomasi (KLT) algorithm along the video frame sequences to obtain the vibration displacement time histories. For some cases with the image plane not parallel to the truss structural plane, the scale factors cannot be applied directly. Therefore, these videos are processed with homography transformation. After scale factor adaptation, tracking results are expressed in physical units and compared with ground truth data. The main operational frequencies and the corresponding mode shapes are identified by using Subspace Stochastic Identification (SSI) from the obtained vibration displacement responses and compared with ground truth data. Structural damages are quantified by elemental stiffness reductions. A Bayesian inference-based objective function is constructed based on natural frequencies to identify the damage by model updating. The Success-History based Adaptive Differential Evolution with Linear Population Size Reduction (L-SHADE) is applied to minimise the objective function by tuning the damage parameter of each element. The locations and severities of damage in each case are then identified. The accuracy and effectiveness are verified by comparison of the identified results with the ground truth data.

Keywords

Acknowledgement

The first author disclosed receipt of the financial support by China Scholarship Council Grant No.201806380151. The corresponding author acknowledges the support from Australian Research Council Discovery project DP210103631, "AI Assisted Probabilistic Structural Health Monitoring with Uncertain Data".

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