Data-driven methods for structural condition assessment have been extensively investigated using deep learning (DL). However, studies on quasi-static response data-based structural health diagnoses are relatively insufficient. The difficulty is that quasi-static response data contain coupled effects of structural parameters and external loads. Considering that the correlation between quasi-static responses subjected to identical external loads is only a function of structural parameters and independent from the external loads, the correlation can therefore be employed as an indicator of the structural condition. This study proposes a condition assessment approach for cable-stayed bridges based on correlation modeling between the deflection of girders and tension in cables. The correlation is modeled by an unsupervised DL network comprising two variational autoencoders (AE) and two generative adversarial networks (GANs). The input and output are marginal probability density functions (PDFs). The DL network is trained as the reconstruction and translation processes to model the intra-class and inter-class correlations. Assumptions of shared latent space and cycle consistency are taken to ensure mutual modeling capacity. The Wasserstein distance between the predicted and ground-truth PDFs of tension in cables is used as an indicator of the structural condition. Using probabilistic correlation of quasi-static responses only requires the PDF of external loads to be identical and does not need the external loads to be precisely identical at any moment, thus relieving time-synchronization restrictions for different sensors. The results show that the predicted PDFs agree well with the ground-truth values under normal conditions. Furthermore, the Wasserstein distance is sensitive to damage and shows noticeable variations when the damage of the stay cable occurs.

中文翻译：

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基于准静态响应的类内和类间概率相关性的桥梁状态评估无监督深度学习方法

用于结构条件评估的数据驱动方法已使用深度学习 (DL) 进行了广泛研究。然而，基于准静态响应数据的结构健康诊断研究相对不足。困难在于准静态响应数据包含结构参数和外部载荷的耦合效应。考虑到相同外荷载作用下的准静态响应之间的相关性只是结构参数的函数，与外荷载无关，因此可以将相关性用作结构条件的指标。本研究提出了一种基于大梁挠度与索拉力之间相关性建模的斜拉桥状态评估方法。相关性由无监督 DL 网络建模，该网络包括两个变分自动编码器 (AE) 和两个生成对抗网络 (GAN)。输入和输出是边际概率密度函数 (PDF)。DL 网络被训练为重建和翻译过程，以对类内和类间相关性进行建模。采用共享潜在空间和循环一致性的假设来确保相互建模能力。索中张力的预测和真实 PDF 之间的 Wasserstein 距离用作结构状况的指标。使用准静态响应的概率相关性只需要外部载荷的PDF相同，并且不需要外部载荷在任何时刻都完全相同，从而缓解不同传感器的时间同步限制。结果表明，预测的 PDF 与正常条件下的真实值非常吻合。此外，Wasserstein 距离对损坏很敏感，并且在斜拉索发生损坏时显示出明显的变化。