使用功率谱密度矩累积函数(MCF-PSD)和深度学习对桥梁跨度进行结构健康监测

IF 0.7 Q4 CONSTRUCTION & BUILDING TECHNOLOGY
Thanh Q. Nguyen, Hoang B. Nguyen
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引用次数: 7

摘要

本文提出了一种新的评定缺陷桥梁受力性能的参数。它是基于功率谱形状变化的功率谱密度矩累积函数(MCF-PSD),通过深度学习模型通过谱矩值的累积函数进行训练。这一新参数可以评估刚度随时间的衰减,从而有助于预测桥梁跨度的工作性。它不仅可以识别桥梁跨度中的危险位置,还可以识别同一桥梁的不同跨度中的风险位置,这证明了它对结构随时间变化的敏感性。本研究表明,与以往的结构质量评估研究相比,使用累积函数算法训练MCF-PSD取得了突出的效果。因此,它满足了评估结构损伤程度的标准,也促进了缺陷诊断和预测的新发展。本研究的结论表明,该函数的变化是评估同一跨度或同一桥梁不同跨度测量位置之间差异以及同一跨度不同位置行为的基础。因此,MCF-PSD在评估结构刚度衰减时比其他参数更敏感。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Structural health monitoring of bridge spans using Moment Cumulative Functions of Power Spectral Density (MCF-PSD) and deep learning
This article proposes a new parameter in evaluating mechanical behaviors of defected bridge spans. It is Moment Cumulative Function of Power Spectral Density (MCF-PSD) based on changes in shape of power spectrum and trained via cumulative function of spectral moment value by deep learning model. This new parameter allows evaluating stiffness attenuation along time, thereby helps to forecast the workability of bridge span. It can identify risky positions in not only a bridge span but also various spans of the same bridge, which proves its sensitivity to the structure’s behavior change over time. This study reveals that training MCF-PSD using cumulative function algorithm has gained outstanding results in comparison with previous studies in structural quality assessment. Therefore, it fulfills criteria of evaluating the damage level in a structure and also fosters new development of defect diagnosis and forecast. Conclusions from this study show that the change of this function is the basis to evaluate difference among measurement positions in the same span or among different spans of the same bridge and behaviors at different positions in the same span. Therefore, MCF-PSD is more sensitive than other parameters in evaluating the structure’s stiffness attenuation.
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来源期刊
Bridge Structures
Bridge Structures CONSTRUCTION & BUILDING TECHNOLOGY-
CiteScore
1.10
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