利用变异模式分解的桥梁驱动损坏检测方法的介绍和应用

Shahrooz Khalkhali Shandiz, Hamed Khezrzadeh, Saeed Eftekhar Azam
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摘要

在这项研究中,变分模式分解(VMD)方法被用于桥梁的行驶健康监测。首先,提出了半挂牵引车在桥梁上行驶的问题。然后,开发了有限元(FE)代码,并与模态分析结果进行了验证,发现两者完全一致。通过 VMD 对车辆的输出信号进行分解,然后对其进行分析,以识别并精确定位桥梁结构的损坏位置。通过对不同道路等级、损坏严重程度和位置以及噪声的分析,从不同角度考察了该技术的适用范围。结果证明了使用 VMD 进行路过损坏检测的稳健性和可靠性。该方法的结果表明,通过 VMD 方法,即使在路面粗糙的情况下,也能检测到深度为横梁高度 10%至 20%的裂缝。此外,还对 VMD 和著名的经验模式分解 (EMD) 方法的结果进行了比较。比较结果表明,通过使用 VMD,可以确定精确的损坏位置,而 EMD 在本研究考虑的条件下无法检测到任何损坏。研究还调查了噪声和行驶车速的影响,发现使用 VMD 处理输出信号可以可靠地估计出损坏位置。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Introduction and application of a drive-by damage detection methodology for bridges using variational mode decomposition
In this research, the variational mode decomposition (VMD) method is used for the drive-by health monitoring of bridges. Firstly, the problem of a half-trailer tractor moving over a bridge is formulated. Next, a Finite Element (FE) code is developed and verified against modal analysis results where complete agreement is found. The vehicle's output signals are decomposed through VMD and then analyzed to identify and precisely locate damage in the bridge structure. The range of applicability of this technique is examined from different perspectives by including various road classes, damage severity and location, and noise. The results prove the robustness and reliability of using VMD for drive-by damage detection. The method outcomes indicate that through the VMD method, cracks with a depth of 10% to 20% of the beam height can be detected even in the case of a rough road profile. A comparison of the results of the VMD and the well-known empirical mode decomposition (EMD) method has also been conducted. This comparison reveals that by implementing the VMD, precise damage locations can be determined, whereas the EMD fails to detect any damage under the conditions considered in this study. The effects of noise and moving vehicle speed are also investigated in the research, and it is found that processing the output signals using VMD can yield reliable estimates of the damage location(s).
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