title: Structural damage identification based on rough sets and artificial neural network.
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.
authors.affiliation: Harbin Institute of Technology, Shenzhen Graduate School, Shenzhen 518055, China ; Key Laboratory of C&PC Structures, Southeast University, Nanjing 211189, China.
authors.forename: Chengyin
authors.initials: C
authors.lastname: Liu
authors.affiliation: State Key Laboratory of Robotics and System, Harbin Institute of Technology, Dazhi Street, Nangang District, Harbin 150001, China.
authors.forename: Xiang
authors.initials: X
authors.lastname: Wu
authors.affiliation: Harbin Institute of Technology, Shenzhen Graduate School, Shenzhen 518055, China.
authors.forename: Ning
authors.initials: N
authors.lastname: Wu
authors.affiliation: Harbin Institute of Technology, Shenzhen Graduate School, Shenzhen 518055, China.
authors.forename: Chunyu
authors.initials: C
authors.lastname: Liu
mesh_terms: D061725:Accelerometry / Q000295:instrumentation / Q000379:methods; D000465:Algorithms; D006245:Hardness Tests / Q000379:methods; D016571:Neural Networks, Computer; D013232:Steel / Q000592:standards*
chemical_list: D013232:Steel