Structural Damage Identification Based on Rough Sets and Artificial Neural Network

被引：1
|
作者：
Liu, Chengyin [1 ,2 ]
Wu, Xiang [3 ]
Wu, Ning [1 ]
Liu, Chunyu [1 ]
机构：
[1] Harbin Inst Technol, Shenzhen Grad Sch, Shenzhen 518055, Peoples R China
[2] Southeast Univ, Key Lab C&PC Struct, Nanjing 211189, Jiangsu, Peoples R China
[3] Harbin Inst Technol, State Key Lab Robot & Syst, Harbin 150001, Peoples R China
来源：
SCIENTIFIC WORLD JOURNAL | 2014年
关键词：
D O I：
10.1155/2014/193284
中图分类号：
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号：
07 ; 0710 ; 09 ;
摘要：
This paper investigates potential applications of the rough sets (RS) theory and artificial neural network (ANN) method on structural damage detection. An information entropy based discretization algorithm in RS is applied for dimension reduction of the original damage database obtained from finite element analysis (FEA). The proposed approach is tested with a 14-bay steel truss model for structural damage detection. The experimental results show that the damage features can be extracted efficiently from the combined utilization of RS and ANN methods even the volume of measurement data is enormous and with uncertainties.
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页数：9
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