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Structural health monitoring of bridge spans using Moment Cumulative Functions of Power Spectral Density (MCF-PSD) and deep learning

机译：使用电位谱密度（MCF-PSD）和深度学习时刻累积函数的桥梁跨度的结构健康监测

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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.
机译：本文提出了评估缺陷桥跨度的机械行为的新参数。基于功率谱形状的变化，是基于功率谱的变化，通过深度学习模型通过谱力矩值的累积函数训练的力量谱密度（MCF-PSD）的力矩累积函数。该新参数允许评估刚度衰减的时间，从而有助于预测桥跨的可加工性。它可以识别危险的位置，不仅是桥跨度，而且可以识别同一桥的各种跨度，这证明了其对结构的行为随时间的变化的敏感性。本研究表明，使用累积函数算法的训练MCF-​​PSD与以前的结构质量评估中的研究相比，使用占效果的结果。因此，它符合评估结构中损伤水平的标准，并促进了缺陷诊断和预测的新发展。本研究的结论表明，该功能的变化是评估同一跨度的测量位置的差异或在同一桥和行为的不同位置处的不同位置的不同位置的差异。因此，MCF-PSD比评估结构刚度衰减的其他参数更敏感。

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