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The Scientific World Journal
Volume 2014 (2014), Article ID 193284, 9 pages
http://dx.doi.org/10.1155/2014/193284
Research Article

Structural Damage Identification Based on Rough Sets and Artificial Neural Network

1Harbin Institute of Technology, Shenzhen Graduate School, Shenzhen 518055, China
2Key Laboratory of C&PC Structures, Southeast University, Nanjing 211189, China
3State Key Laboratory of Robotics and System, Harbin Institute of Technology, Dazhi Street, Nangang District, Harbin 150001, China

Received 27 February 2014; Accepted 22 April 2014; Published 11 June 2014

Academic Editor: Hua-Peng Chen

Copyright © 2014 Chengyin Liu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

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.