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Abstract：
Bridge early warning based on structural health monitoring (SHM) system is of significant importance for ensuring bridge safe operation. The temperature-induced deflection (TID) is a sensitive indicator for performance degradation of continuous rigid frame bridges, but the time-lag effect makes it challenging to predict the TID accurately. A bridge early warning method based on nonlinear modeling for the TID is proposed in this article. Firstly, the SHM data of temperature and deflection of a continuous rigid frame bridge are analyzed to examine the temperature gradient variation patterns. Kernel principal component analysis (KPCA) is used to extract principal temperature components. Then, the TID is extracted through wavelet transform, and a nonlinear modeling method for the TID considering the temperature gradient is proposed using the support vector machine (SVM). Finally, the prediction errors of the KPCA-SVM algorithm are analyzed, and the early warning thresholds are determined based on the statistical patterns of the errors. The results show that the KPCA-SVM algorithm achieves high-precision nonlinear modeling for the TID while significantly reducing the computational load. The prediction results have coefficients of determination above 0.98 and fluctuate within a small range with clear statistical patterns. Setting the early warning thresholds based on the statistical patterns of errors enables dynamic and multi-level warnings for bridge structures.
摘要：
基于结构健康监测（SHM）系统的桥梁预警对于确保桥梁安全运营具有重要意义。温度引起的挠度（TID）是连续刚构桥性能下降的敏感指标，但是时滞效应使得准确预测TID具有挑战性。提出了一种基于非线性建模的桥梁TID预警方法。首先,分析了连续刚构桥的温度和挠度的SHM数据，以检验温度梯度的变化规律。核主成分分析(KPCA)用于提取主温度成分。然后，TID是通过小波变换提取的，利用支持向量机(SVM)提出了一种考虑温度梯度的TID非线性建模方法。最后,分析了KPCA-SVM算法的预测误差,并根据误差的统计模式确定预警阈值。结果表明,KPCA-SVM算法在显著降低计算量的同时,实现了TID的高精度非线性建模。预测结果的决定系数在0.98以上，在小范围内波动，统计规律清晰。根据错误的统计模式设置预警阈值可以实现桥梁结构的动态和多级警告。
