摘要
提出了一种基于BP神经网络的结构破损诊断方法，该方法以结构残余力向量作为破损诊断的网络输入。对网络训练样本采用广义空间格点法进行了变换，从而较好地解决了由于系统响应样本在数据空间分布不均对网络收敛速度及网络诊断精度的影响问题。应用实例表明，本文方法能准确诊断结构破损位置与严重程度，是一种有效的结构破损诊断方法。
A method for damage detection in structures using backpropagation neural networks is proposed in this paper. The dynamic residual vector of a structure is taken as inputs of the neural network for parameter identification. The Generalized-Spaced-Lattice(GSL) transformation is used to transform original input and/or output data points of all training pattern onto uniformly spaced lattice points over a multi-dimensional space. Thus, the neural network can learn the training patterns efficiently as well as accurately. Two examples are given to show the effectiveness of the proposed method.
出处
《工程力学》
EI
CSCD
北大核心
2001年第1期56-61,共6页
Engineering Mechanics
