A novel damage detection model, which utilizes the spatiotemporal characteristics of the acceleration data, is proposed to assess the structural integrity of railway bridges. For this, the measured acceleration data are decomposed into several intrinsic mode functions (IMFs) using the sparse random mode decomposition model. The generated IMFs are subsequently integrated into the enhanced time series conditional generative adversarial network model to identify possible damage in bridges across various frequency bands. The influence of environmental and operational variables (EOVs), particularly temperature fluctuations, was also investigated. The proposed model was verified using both numerical and experimental data from a plate girder bridge. Further validation was conducted using the Z24 bridge dataset, and damage cases under the influence of EOVs were successfully predicted. Throughout the validation process, various anomaly metrics were introduced to establish a threshold value, and a covariance‐based time domain metric was proven to be the most effective in our cases.

中文翻译：

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使用时频分解和条件生成模型的铁路桥梁损伤检测


提出了一种新的损伤检测模型，该模型利用加速度数据的时空特性来评估铁路桥梁的结构完整性。为此，使用稀疏随机模态分解模型将测得的加速度数据分解为几个本征模态函数 （IMF）。生成的 IMF 随后被集成到增强的时间序列条件生成对抗网络模型中，以识别跨不同频段的桥梁可能造成的损坏。还研究了环境和操作变量 （EOV） 的影响，尤其是温度波动。使用板梁桥的数值和实验数据验证了所提出的模型。使用 Z24 桥梁数据集进行了进一步验证，并成功预测了受 EOV 影响的损伤情况。在整个验证过程中，引入了各种异常指标来建立阈值，而基于协方差的时域指标被证明在我们的案例中是最有效的。