INFLUENCE OF MEASUREMENT ERRORS ON STRUCTURAL DAMAGE IDENTIFICATION USING ARTIFICIAL NEURAL NETWORKS

被引：0
|
作者：
王柏生
倪一清
高赞明
机构：
来源：
Journal of Zhejiang University-Science A(Applied Physics & Engineering) | 2000年 / 03期
关键词：
structural damage identification; artificial neural network; measurement error;
D O I：
暂无
中图分类号：
TU312.3TP183 [];
学科分类号：
081405 ;
摘要：
The effect of measurement errors on structural damage identification using artificial neural networks (ANN) was investigated in this study. By using back propagation (BP) networks with proper input vectors, numerical simulation tests for damage detection on a six storey frame were conducted with measurement errors in deterministic as well as probabilistic senses. The identifiability using ANN for damage location and extent was studied for the cases of measurement errors with different degrees. The results showed that there exists a critical level of measurement error beyond which the probability of correct identification is sharply decreased. The identifiability using the neural networks in the presence of modeling and measurement errors is finally verified using experimental data on a two storey steel frame.
引用
收藏
页码：54 / 62
页数：9
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