结构损伤识别的耦合神经网络方法

A Coupled Neural Networks Method for Structural Damage Identification

目的基于误差传播算法的BPNN和基于自适应共振理论的ART神经网络,提出一种耦合神经网络的三级识别模型,以实现对结构损伤的自主识别.方法采用分步识别的思想,利用ART神经网络首先识别出有损伤的层,然后用遗传算法搜索最佳的BP神经网络结构来分别识别结构损伤的具体位置和损伤程度.结果通过对结构健康监测基准问题的计算表明,提出的耦合神经网络的识别模型能够自主识别结构损伤的发生,正确识别结构损伤的具体位置和损伤程度.结论基于误差传播算法的BPNN和基于自适应共振理论的ART神经网络组成的耦合神经网络识别模型具有自主识别结构损伤发生的能力,且识别速度快,能够正确识别结构损伤发生的具体位置和损伤程度,适宜于在线监测.

A three-level identification model is proposed in the damage identification of structures. The BPNN and ART neural networks, which are based on error propagation algorithm and self-adaptive resonance theory respectively, are coupled in this model to improve its performance. The damage of structures could be identified step by step. First ART neural networks are employed to identify the occurrence of the damage, and then the location and grade of the damage is confirmed by using the genetic algorithm to search for the optimal BP neural networks. It can be concluded from the numerical analyses of the benchmark structures that satisfactory results could be obtained by using this coupled neural networks method. The coupled method based on BPNN and ART could identify the occurrence, location and grade of the damage and it can efficiently monitor the status of on-line structures.