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Comparison of radial basis function and back-error propagation neural networks for crack detection in variable cross-section beams

International Conference on Applications and Design in Mechanical Engineering 2012 (ICADME 2012) organized by School of Mechatronic Engineering, Universiti Malaysia Perlis (UniMAP), 27th - 28th Februari 2012 at Bayview Beach Resort, Penang, Malaysia.

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Bibliographic Details
Main Authors: Neda Nasiri, Shahab Ilbeigi, Foad Nazari, Behzad Asmar, Mahdi Karimi, Sara Baghalian
Other Authors: nedanasiri@rocketmail.com
Format: Working Paper
Language:English
Published: Universiti Malaysia Perlis (UniMAP) 2012
Subjects:
Crack detection
Finite element method
Artificial neural network
Radial basis function
Non-uniform beam
Online Access:http://dspace.unimap.edu.my/xmlui/handle/123456789/20236
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spelling my.unimap-202362012-07-10T05:04:11Z Comparison of radial basis function and back-error propagation neural networks for crack detection in variable cross-section beams Neda Nasiri Shahab Ilbeigi Foad Nazari Behzad Asmar Mahdi Karimi Sara Baghalian nedanasiri@rocketmail.com shahab_ilbeigi@yahoo.com foadnazari@gmail.com asmar.behzad@yahoo.com karimi_mh@yahoo.com sara.baghalian@gmail.com Crack detection Finite element method Artificial neural network Radial basis function Non-uniform beam International Conference on Applications and Design in Mechanical Engineering 2012 (ICADME 2012) organized by School of Mechatronic Engineering, Universiti Malaysia Perlis (UniMAP), 27th - 28th Februari 2012 at Bayview Beach Resort, Penang, Malaysia. In this study a method for identification of crack in variable cross-section beam is presented. The process of crack identification is consists of three steps. In first step, three natural frequencies of a variable cross-section beam for different locations and depths of cracks are obtained using Finite Element Method (FEM). In second step, two Back-Error Propagation neural networks (BEP) and two Radial Basis Function neural networks (RBF) are created and trained. The inputs of neural networks are first three natural frequencies and the outputs of first and second BEP and also RBF are corresponding locations and depth of cracks, respectively. In third step, some of natural frequencies of variable cross-section beam with distinct crack conditions are applied as inputs to trained neural networks. Finally obtained results of two types of neural networks are compared with each other. Computed results illustrate that computed cracks characteristics are in good agreements with actual data. 2012-07-10T05:04:11Z 2012-07-10T05:04:11Z 2012-02-27 Working Paper http://hdl.handle.net/123456789/20236 en Proceedings of the International Conference on Applications and Design in Mechanical Engineering 2012 (ICADME 2012) Universiti Malaysia Perlis (UniMAP) Pusat Pengajian Kejuruteraan Mekatronik
institution Universiti Malaysia Perlis
building UniMAP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Perlis
content_source UniMAP Library Digital Repository
url_provider http://dspace.unimap.edu.my/
language English
topic Crack detection
Finite element method
Artificial neural network
Radial basis function
Non-uniform beam
spellingShingle Crack detection
Finite element method
Artificial neural network
Radial basis function
Non-uniform beam
Neda Nasiri
Shahab Ilbeigi
Foad Nazari
Behzad Asmar
Mahdi Karimi
Sara Baghalian
Comparison of radial basis function and back-error propagation neural networks for crack detection in variable cross-section beams
description International Conference on Applications and Design in Mechanical Engineering 2012 (ICADME 2012) organized by School of Mechatronic Engineering, Universiti Malaysia Perlis (UniMAP), 27th - 28th Februari 2012 at Bayview Beach Resort, Penang, Malaysia.
author2 nedanasiri@rocketmail.com
author_facet nedanasiri@rocketmail.com
Neda Nasiri
Shahab Ilbeigi
Foad Nazari
Behzad Asmar
Mahdi Karimi
Sara Baghalian
format Working Paper
author Neda Nasiri
Shahab Ilbeigi
Foad Nazari
Behzad Asmar
Mahdi Karimi
Sara Baghalian
author_sort Neda Nasiri
title Comparison of radial basis function and back-error propagation neural networks for crack detection in variable cross-section beams
title_short Comparison of radial basis function and back-error propagation neural networks for crack detection in variable cross-section beams
title_full Comparison of radial basis function and back-error propagation neural networks for crack detection in variable cross-section beams
title_fullStr Comparison of radial basis function and back-error propagation neural networks for crack detection in variable cross-section beams
title_full_unstemmed Comparison of radial basis function and back-error propagation neural networks for crack detection in variable cross-section beams
title_sort comparison of radial basis function and back-error propagation neural networks for crack detection in variable cross-section beams
publisher Universiti Malaysia Perlis (UniMAP)
publishDate 2012
url http://dspace.unimap.edu.my/xmlui/handle/123456789/20236
_version_ 1643793017426935808
score 13.160551

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