首页> 外文期刊>Advances in Acoustics and Vibration >A Crack Identification Method for Bridge Type Structures under Vehicular Load Using Wavelet Transform and Particle Swarm Optimization
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A Crack Identification Method for Bridge Type Structures under Vehicular Load Using Wavelet Transform and Particle Swarm Optimization

机译:基于小波变换和粒子群优化的车辆荷载作用下桥梁结构裂缝识别方法。

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摘要

In this work a crack identification method is proposed for bridge type structures carrying moving vehicle. The bridge is modeled as an Euler-Bernoulli beam, and open cracks exist on several points of the beam. Half-car model is adopted for the vehicle. Coupled equations of the beam-vehicle system are solved using Newmark-Beta method, and the dynamic responses of the beam are obtained. Using these and the reference displacements, an objective function is derived. Crack locations and depths are determined by solving the optimization problem. To this end, a robust evolutionary algorithm, that is, the particle swarm optimization (PSO), is employed. To enhance the performance of the method, the measured displacements are denoised using multiresolution property of the discrete wavelet transform (DWT). It is observed that by the proposed method it is possible to determine small cracks with depth ratio 0.1 in spite of 5% noise interference.
机译:在这项工作中,提出了一种用于移动车辆的桥梁型结构的裂缝识别方法。桥梁被建模为Euler-Bernoulli梁,并且在梁的几个点上都存在开裂。车辆采用半车模型。利用Newmark-Beta方法求解了梁-车辆系统的耦合方程,得到了梁的动力响应。使用这些和参考位移,得出目标函数。裂纹的位置和深度是通过解决优化问题来确定的。为此,采用了鲁棒的进化算法,即粒子群优化(PSO)。为了增强该方法的性能,使用离散小波变换(DWT)的多分辨率属性对测量的位移进行了消噪。可以看出,尽管噪声干扰为5%,但通过所提出的方法仍可以确定深度比为0.1的小裂纹。

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