Abstract: Damage and cracks may occur with dam during long operation period or when earthquake happens. It is difficult to diagnose internal damage by conventional methods, which would leave security hazards. A two-step damage detection method for large-scale hydraulic structures was introduced to solve this problem. Taking non-overflow section of Wudu reservoir as an example, combined with vibration parameter identification technique, research on damage identification of gravity dam based on RBF neural networks is carried out. Firstly, this paper verifies the validity of this method by theoretical research and numerical simulation. Then, the result of damage identification is checked by comparing with the dynamic characteristics obtained from the shaking table model test. The results indicate that the method is feasible to identify the position of damage and predict the extent of damage of gravity dam, but further study is still needed to solve practical problems.

Key words: neural networks, gravity dam, damage identification, model test

摘要： 大坝在长期使用过程中或遭遇地震时可能出现损伤、产生裂缝，使用常规的方法诊断大坝内部裂缝损伤十分困难。为克服这一困难，提出了适用于大型水利工程结构损伤识别的两步诊断方法。以武都水库非溢流坝段为例，基于振动参数识别技术，利用径向基函数神经网络对重力坝损伤识别展开研究，先从理论研讨和数值模拟验证该方法的有效性，再结合振动台模型试验中所得的结构动力特性进行检验，对比所得的损伤识别效果。结果表明：该方法对重力坝进行损伤位置识别、损伤程度预测是可行的，有待于在实际工程应用中进行检验。