During flood events, the dynamic interaction between flowing water and bridges generates random loadings that force bridges to vibrate in all six degrees of freedom. It is difficult for a structural damage detection method to select a degree of freedom, or damage feature, to accurately describe and predict damage. The methodology presented here identifies damage-sensitive features and uses them to monitor bridge health. A small-scale physical model of a multi-span highway bridge was constructed to satisfy geometrical, Cauchy, and Froude similarities, and six-dimensional hydrodynamic forces induced by simulated flood events were investigated as an input excitation in a tilting flume. It was determined that pitch, roll, and surge motions can be used as damage features during the inundated stage, while pitch, roll, surge, and heave can be used before the inundated stage. In addition, angular velocity signals exhibited more consistent damage indices than acceleration. Using the damage features, the proposed algorithm could successfully detect damage and damage severity during simulated flood stages. Identifying damage features can reduce the size of the collected data and inform emergency responders’ decisions. This case study can be used to test methods at full scale on similar structures to develop automated health-monitoring systems.

在洪水事件期间，流水与桥梁之间的动态相互作用会产生随机载荷，从而迫使桥梁在所有六个自由度上振动。对于结构损伤检测方法，很难选择自由度或损伤特征来准确描述和预测损伤。此处介绍的方法可识别对损伤敏感的功能，并使用它们来监控桥梁的运行状况。为满足几何，柯西和弗洛德的相似性，构建了一个多跨公路桥梁的小规模物理模型，并研究了由模拟洪水事件引起的六维水动力作为倾斜槽中的输入激励。已确定俯仰，横摇和喘振运动可以用作淹没阶段的损坏特征，而俯仰，横摇，喘振，并可以在淹没阶段之前使用升沉。另外，角速度信号显示出比加速度更一致的损伤指数。利用损伤特征，所提算法可以在模拟洪水阶段成功检测出损伤和损伤严重性。识别损坏特征可以减少所收集数据的大小并通知应急人员的决定。该案例研究可用于在类似结构上全面测试方法，以开发自动化的健康监控系统。识别损坏特征可以减少所收集数据的大小并通知应急人员的决定。该案例研究可用于在类似结构上全面测试方法，以开发自动化的健康监控系统。识别损坏特征可以减少所收集数据的大小并通知应急人员的决定。该案例研究可用于在类似结构上全面测试方法，以开发自动化的健康监控系统。