Influence of measurement errors on structural damage identification using artificial neural networks

被引：2

作者：

Wang Bai-sheng

Ni Yi-qing

Ko Jan-ming

机构：

[1] Zhejiang University,Dept. of Civil Engineering

[2] Hong Kong Polytechnic University,Dept. of Civil and Structural Engineering

来源：

Journal of Zhejiang University-SCIENCE A | 2000年 / 1卷 / 3期

关键词：

structural damage identification; artificial neural network; measurement error; A; TU312.3;

D O I：

10.1631/BF02910639

中图分类号：

学科分类号：

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

页码：291 / 299

页数：8

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