摘要
根据实际动力响应对结构有限元模型进行修正,是实现损伤识别和健康监测的必要前提。针对基于神经网络的模型修正方法的不足,选用均匀设计法构造样本从而有效减少所需样本数量,而且计算效率高。采用遗传算法优化神经网络权值,提高了运算速度。基于上述研究,提出了基于子结构和神经网络的递推模型修正方法。该方法将结构分解成多层次的子结构,选取适当的损伤因素逐步实现逐级的修正。应用该方法对一网壳结构进行了模型修正,修正中首先采用固有频率作为损伤因素,结果表明遗传算法明显地提高了神经网络的计算速度,最后的递推修正效果令人满意;其次提出了采用小波包频带能量作为损伤因素的修正方法,该方法同样准确有效,并且不再依赖传统的模态分析技术,更为实用便捷。
The finite element model need to be updated according to actual dynamic responses in structural damage detection and health monitoring. The model updating method based on artificial neural network （ANN） is improved in this study. Because too much samples are demanded for training ANN, the uniform design method is used to produce samples to reduce the amount of samples in the process of structural damage simulation, thus the computation efficiency is enhanced. The genetic algorithm is used to optimize the initial weight of back propagation network and as a result the operation velocity is enhanced. A stepwise model updating method based on substructures and ANN is presented. A structure is divided into multilayer substructures, and the updating is taken step by step according to appropriate damage factors. A dome structure is selected as an example and two damage factors-the frequencies and the wavelet packet decomposition （WPD） energy spectrums are investigated. The results show that both factors can give accurate results by using this method but the latter is much more practicable.
出处
《工程力学》
EI
CSCD
北大核心
2008年第4期99-105,共7页
Engineering Mechanics
基金
北京市自然科学基金资助重点项目(8041002)
北京市科委奥运专项项目(Z0005174040111)
关键词
模型修正
神经网络
均匀设计法
遗传算法
小波包分解
model updating
artificial neural network
uniform design method
genetic algorithm
waveletpacket decomposition
