Damage identification for structural health monitoring using fuzzy pattern recognition

被引：88

作者：

Taha, MMR ^{[1
]}

Lucero, J

机构：

[1] Univ New Mexico, Albuquerque, NM 87131 USA

[2] Los Alamos Natl Lab, Los Alamos, NM 87545 USA

来源：

ENGINEERING STRUCTURES | 2005年 / 27卷 / 12期

关键词：

Structural Health Monitoring; artificial neural network; wavelet multi-resolution analysis; damage index; fuzzy set; Bayesian updating;

D O I：

10.1016/j.engstruct.2005.04.018

中图分类号：

TU [建筑科学];

学科分类号：

0813 ;

摘要：

Uncertainty abounds with in situ structural performance assessment and damage detection in Structural Health Monitoring (SHM). Most research in SHM focuses on statistical analysis, data acquisition, feature extraction and data reduction. We introduce a method to improve pattern recognition and damage detection by supplementing Intelligent Structural Health Monitoring (ISHM) with fuzzy sets. Intuitively we know that damage does not occur as a Boolean relation (one of two values, true or false) but progessively. Bayesian updating is used to demarcate levels of damage into fuzzy sets accommodating the uncertainty associated with the ambiguous damage states. The new techniques are examined to provide damage identification using data simulated from finite element analysis of a prestressed concrete bridge without a priori known levels of damage. (c) 2005 Elsevier Ltd. All rights reserved.

引用

页码：1774 / 1783

页数：10

共 24 条

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