Structural Damage Identification Based on Rough Sets and Artificial Neural Network

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...

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Detalles Bibliográficos
Autores principales: Liu, Chengyin, Wu, Xiang, Wu, Ning, Liu, Chunyu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2014
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4074987/
https://www.ncbi.nlm.nih.gov/pubmed/25013847
http://dx.doi.org/10.1155/2014/193284
Descripción
Sumario: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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