Damage Identification Method for Irregular Continuous Box Girder Bridge Based on RBF Neural Network Optimized by PSO

被引：2
|
作者：
Liu, Hanbing [1 ]
Sun, Pingyi [1 ]
Tan, Guojin [1 ]
Wang, Yanhua [1 ]
机构：
[1] Jilin Univ, Coll Transportat, Changchun 130022, Peoples R China
来源：
MECHANICAL ENGINEERING, MATERIALS SCIENCE AND CIVIL ENGINEERING II | 2014年 / 470卷
关键词：
irregular continuous box girder bridge; damage identification; PSO; RSF neural network; strain mode;
D O I：
10.4028/www.scientific.net/AMM.470.1034
中图分类号：
TU [建筑科学];
学科分类号：
0813 ;
摘要：
A damage identification method suitable for the characteristics of the irregular continuous box girder bridge is presented. The damage index structured by the ratio of mode shape and relative change in strain mode is represented as input data to identify the structure damage by means of PSO-RBF neural network. Finally, the numerical simulation calculation for the damage identification of an irregular continuous box girder bridge with the length of 4x30 meters verifies the feasibility and validity of the proposed method.
引用
收藏
页码：1034 / 1038
页数：5
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