桥梁的振动位移可反应桥梁的力学性能及运营状态，同时通过振动位移可反演桥梁的动力特性，如模态和频率等参数，从而评估桥梁的运营状态和损伤状况，而传统的位移监测技术成本高和测点有限。本文提出了一种低成本、非接触、多点测的基于核相关滤波器 (kernelized correlation filters，KCF)算法的桥梁小幅振动位移视觉测量方法，开展了不同白噪声扫频下双柱式桥墩模型振动台试验，采用激光位移计(laser displacement sensor ,LDS)作为参考进行比较验证，利用协方差驱动的随机子空间方法识别了桥梁固有频率及模态振型，验证了采用核相关滤波算法在识别双柱式桥墩乃至桥梁小幅振动位移及相应模态频率的可靠性、可行性和准确性。结果表明，基于核相关滤波算法识别的双柱式桥墩小幅振动位移与LDS记录的波形、变化趋势和峰值几乎一致，其峰值误差在4%以内；采用机器视觉识别的振动位移识别的双柱式桥墩固有频率与LDS结果之间的误差在2.5%之内，两者之间识别的模态振型置信水平达0.90以上。
Abstract
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.
关键词
机器视觉 /
改进的KCF算法 /
小幅振动位移 /
动力特性 /
振动台试验
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Key words
Machine vision /
Modified kernelized correlation filters（KCF) algorithm /
Small amplitude vibration displacement /
Dynamic properties /
Shaking table tests
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