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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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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Institution:	Universiti Malaysia Perlis
Language:	English

id	my.unimap-20236
record_format	dspace
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

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

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