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...

Descripción completa

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
_version_ 1782323278176583680
author Liu, Chengyin
Wu, Xiang
Wu, Ning
Liu, Chunyu
author_facet Liu, Chengyin
Wu, Xiang
Wu, Ning
Liu, Chunyu
author_sort Liu, Chengyin
collection PubMed
description 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.
format Online
Article
Text
id pubmed-4074987
institution National Center for Biotechnology Information
language English
publishDate 2014
publisher Hindawi Publishing Corporation
record_format MEDLINE/PubMed
spelling pubmed-40749872014-07-10 Structural Damage Identification Based on Rough Sets and Artificial Neural Network Liu, Chengyin Wu, Xiang Wu, Ning Liu, Chunyu ScientificWorldJournal Research Article 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. Hindawi Publishing Corporation 2014 2014-06-11 /pmc/articles/PMC4074987/ /pubmed/25013847 http://dx.doi.org/10.1155/2014/193284 Text en Copyright © 2014 Chengyin Liu et al. https://creativecommons.org/licenses/by/3.0/ 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.
spellingShingle Research Article
Liu, Chengyin
Wu, Xiang
Wu, Ning
Liu, Chunyu
Structural Damage Identification Based on Rough Sets and Artificial Neural Network
title Structural Damage Identification Based on Rough Sets and Artificial Neural Network
title_full Structural Damage Identification Based on Rough Sets and Artificial Neural Network
title_fullStr Structural Damage Identification Based on Rough Sets and Artificial Neural Network
title_full_unstemmed Structural Damage Identification Based on Rough Sets and Artificial Neural Network
title_short Structural Damage Identification Based on Rough Sets and Artificial Neural Network
title_sort structural damage identification based on rough sets and artificial neural network
topic Research Article
url 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
work_keys_str_mv AT liuchengyin structuraldamageidentificationbasedonroughsetsandartificialneuralnetwork
AT wuxiang structuraldamageidentificationbasedonroughsetsandartificialneuralnetwork
AT wuning structuraldamageidentificationbasedonroughsetsandartificialneuralnetwork
AT liuchunyu structuraldamageidentificationbasedonroughsetsandartificialneuralnetwork

Ejemplares similares