基于神经网络模型的桥梁结构损伤识别

给出一种神经网络方法在钢桥结构损伤检测中的应用。着重讨论了网络设计和学习算
法问题。网络结构模拟桁架桥，训练样本从多个损伤区域产生。仿真表明，本算法只需少量的结构参数就可得到较满意的辨识结果，且适用于现场测量数据不精确或不能完全已知的情况，具有较好的工程实用性。
关键词人工神经网络损伤识别 桁架桥
Abstract The paper presents an application of ANN on the damage detection of steel bridge
structures. The issues relating to the design of network and learning algorithm are addressed and network architectures have been developed with reference to trussed bridge structures. The training patterns are generated for multiple damaged zones in a structure. The results of simulation show that the algorithm is suitable for structural identification of bridges where the measured data are expected to be imprecise and often incomplete. The engineering importance of the method is demonstrated from the fact that measured input at only a few locations in the structure is needed in the identification process using the ANN.
Key words ANN; Damage detection; Bridge truss structure