Damage detection based on improved particle swarm optimization using vibration data

被引：165

作者：

Kang, Fei ^{[1
]}

Li, Jun-jie ^{[1
]}

Xu, Qing ^{[1
]}

机构：

[1] Dalian Univ Technol, Fac Infrastruct Engn, Dalian 116024, Peoples R China

来源：

APPLIED SOFT COMPUTING | 2012年 / 12卷 / 08期

基金：

中国国家自然科学基金;

关键词：

Damage identification; Particle swarm optimization; Artificial immune system; Modal parameter; Swarm intelligence; GENETIC ALGORITHM; IDENTIFICATION;

D O I：

10.1016/j.asoc.2012.03.050

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

An immunity enhanced particle swarm optimization (IEPSO) algorithm, which combines particle swarm optimization (PSO) with the artificial immune system, is proposed for damage detection of structures. Some immune mechanisms, selection, receptor editing and vaccination are introduced into the basic PSO to improve its performance. The objective function for damage detection is based on vibration data, such as natural frequencies and mode shapes. The feasibility and efficiency of IEPSO are compared with the basic PSO, a differential evolution algorithm and a real-coded genetic algorithm on two examples. Results show that the proposed strategy is efficient on determining the sites and the extents of structure damages. (C) 2012 Elsevier B. V. All rights reserved.

引用

页码：2329 / 2335

页数：7

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