基于BP神经网络的网格结构损伤识别与试验研究

针对网格结构中杆件数量众多，但节点数总是远远小于杆件数的特点，损伤识别中采用了基于BP神经网络技术和面向节点的损伤初步定位方法的网格结构损伤识别的三步法。对双层柱面网壳结构模型在不同杆件去掉时的四种损伤情况下的振动特性进行了实测，并以实测低阶模态的频率变化率和少数测点的振型分量作为神经网络输入参数，对模型的各种损伤情况进行了识别。结果表明，所用的方法可以精简神经网络的结构，并提高其模式识别的能力。该方法可用于对大型复杂结构的损伤识别。

Damage identification of grid structures based on BP neural networks and experimental study

Based on the fact that the number of elements is much larger than that of the nodes in a grid structure,preliminary localization method of the nodes oriented structural damage and a three-step damage identification strategy for large grid structures are used in the damage identification process based on BP networks. The vibration parameters of a double-layer cylindrical latticed shell model are measured under four different damage cases. The variation rate of the measured lower frequencies and components of the lower mode shapes moasured from a few nodes of the lattice shell model are used as the input parameters of the BP neural network, the identification of different damage cases are carried out. The results indicate that the method used in this paper can greatly reduce the architecture of the BP networks, and thus the pattern recognition capability of the BP networks can be remarkably improved. These methods can be used for the ciency. damage identification of large complex structures with significant efficiency.