关键词:
Strain measurement
摘要:
Evaluating the mechanical performance and operational conditions of bridge structures relies on accurately measuringement of vibration displacement. Such measurement can provide essential parameters, such as mode and frequency, to assess the operational status and condition of damaged bridge structures. However, traditional displacement monitoring techniques have high cost, low accuracyare expensive, have low accuracy, and offer and limited measurement positions. This paper proposes a A low-cost, non-contact, and multi-point measurement method was proposed in this paper,based on the Kernelized correlation filters (KCF) algorithm to measure the vibration displacement of bridges. The proposed method used shaking Shaking table tests on a dual-column pier model were conducted,with energy dissipation links under different white noise sweeps. The recorded vibration displacement from a laser displacement sensor (LDS) was used as a reference for comparison. The natural frequencies and mode shapes of the dual-column pier and bridges were identified using the covariance-driven stochastic subspace identification method. The study verified the The reliability, feasibility, and accuracy of machine vision technology in identifying the natural frequencies and mode shapes of bridges were verified. The results showed that the small amplitude vibration displacement of the dual-column pier identified by the KCF algorithm is almost consistent with the waveforms, change trends, and peak values recorded by LDS, with a maximum peak error of 4%. The error between the natural frequencies identified by the KCF algorithm and the LDS results was within 2.5%. The confidence level of the mode shapes identified between both approaches was above 0.90. © 2025 Chinese Vibration Engineering Society. All rights reserved.