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Damage identification using experimental modal analysis and adaptive neuro-fuzzy interface system (ANFIS)

The adaptive neuro-fuzzy inference system (ANFIS) is a process for mapping from a given input to a single output using the fuzzy logic and neuro-adaptive learning algorithms. Using a given input-output data set, ANFIS constructs a Fuzzy Inference System (FIS) whose fuzzy membership function paramete...

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Main Authors: Hakim, S.J.S., Razak, H.A.
Format: Conference or Workshop Item
Language:English
Published: 2012
Subjects:
TA Engineering (General). Civil engineering (General)
Online Access:http://eprints.um.edu.my/9055/1/Damage_identification_using_experimental_modal_analysis_and_adaptive_neuro-fuzzy_interface_system_%28ANFIS%29.pdf
http://eprints.um.edu.my/9055/
http://www.scopus.com/inward/record.url?eid=2-s2.0-84864025634&partnerID=40&md5=87a2e4ed41f974cf8f15fc55a9e79f0a http://link.springer.com/chapter/10.1007/978-1-4614-2425-3₃₇
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spelling my.um.eprints.90552014-01-27T01:04:56Z http://eprints.um.edu.my/9055/ Damage identification using experimental modal analysis and adaptive neuro-fuzzy interface system (ANFIS) Hakim, S.J.S. Razak, H.A. TA Engineering (General). Civil engineering (General) The adaptive neuro-fuzzy inference system (ANFIS) is a process for mapping from a given input to a single output using the fuzzy logic and neuro-adaptive learning algorithms. Using a given input-output data set, ANFIS constructs a Fuzzy Inference System (FIS) whose fuzzy membership function parameters are adjusted using combination of back propagation algorithm with a least square type of method. The feasibility of ANFIS as strong tool for predicting the severity of damage in a model steel girder bridge is examined in this research. Reduction in the structural stiffness produces changes in the dynamics properties, such as the natural frequencies and mode shapes. In this study, natural frequencies of a structure are applied as effective input parameters to train the ANFIS and the required data are obtained from experimental modal analysis. The performance of ANFIS model was assessed using Mean Square Error (MSE) and coefficient of determination (R 2). The ANFIS model could predict the severity of damage with MSE of 0.0049 and correlation coefficient (R 2) of 0.9976 for traing data sets. The results show the ability of an adaptive neuro-fuzzy inference system to predict the damage severity of the structure with high accuracy. © The Society for Experimental Mechanics, Inc. 2012. 2012 Conference or Workshop Item PeerReviewed application/pdf en http://eprints.um.edu.my/9055/1/Damage_identification_using_experimental_modal_analysis_and_adaptive_neuro-fuzzy_interface_system_%28ANFIS%29.pdf Hakim, S.J.S. and Razak, H.A. (2012) Damage identification using experimental modal analysis and adaptive neuro-fuzzy interface system (ANFIS). In: 30th IMAC, A Conference on Structural Dynamics, 2012, 2012, Jacksonville, FL. http://www.scopus.com/inward/record.url?eid=2-s2.0-84864025634&partnerID=40&md5=87a2e4ed41f974cf8f15fc55a9e79f0a http://link.springer.com/chapter/10.1007/978-1-4614-2425-3₃₇
institution Universiti Malaya
building UM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaya
content_source UM Research Repository
url_provider http://eprints.um.edu.my/
language English
topic TA Engineering (General). Civil engineering (General)
spellingShingle TA Engineering (General). Civil engineering (General)
Hakim, S.J.S.
Razak, H.A.
Damage identification using experimental modal analysis and adaptive neuro-fuzzy interface system (ANFIS)
description The adaptive neuro-fuzzy inference system (ANFIS) is a process for mapping from a given input to a single output using the fuzzy logic and neuro-adaptive learning algorithms. Using a given input-output data set, ANFIS constructs a Fuzzy Inference System (FIS) whose fuzzy membership function parameters are adjusted using combination of back propagation algorithm with a least square type of method. The feasibility of ANFIS as strong tool for predicting the severity of damage in a model steel girder bridge is examined in this research. Reduction in the structural stiffness produces changes in the dynamics properties, such as the natural frequencies and mode shapes. In this study, natural frequencies of a structure are applied as effective input parameters to train the ANFIS and the required data are obtained from experimental modal analysis. The performance of ANFIS model was assessed using Mean Square Error (MSE) and coefficient of determination (R 2). The ANFIS model could predict the severity of damage with MSE of 0.0049 and correlation coefficient (R 2) of 0.9976 for traing data sets. The results show the ability of an adaptive neuro-fuzzy inference system to predict the damage severity of the structure with high accuracy. © The Society for Experimental Mechanics, Inc. 2012.
format Conference or Workshop Item
author Hakim, S.J.S.
Razak, H.A.
author_facet Hakim, S.J.S.
Razak, H.A.
author_sort Hakim, S.J.S.
title Damage identification using experimental modal analysis and adaptive neuro-fuzzy interface system (ANFIS)
title_short Damage identification using experimental modal analysis and adaptive neuro-fuzzy interface system (ANFIS)
title_full Damage identification using experimental modal analysis and adaptive neuro-fuzzy interface system (ANFIS)
title_fullStr Damage identification using experimental modal analysis and adaptive neuro-fuzzy interface system (ANFIS)
title_full_unstemmed Damage identification using experimental modal analysis and adaptive neuro-fuzzy interface system (ANFIS)
title_sort damage identification using experimental modal analysis and adaptive neuro-fuzzy interface system (anfis)
publishDate 2012
url http://eprints.um.edu.my/9055/1/Damage_identification_using_experimental_modal_analysis_and_adaptive_neuro-fuzzy_interface_system_%28ANFIS%29.pdf
http://eprints.um.edu.my/9055/
http://www.scopus.com/inward/record.url?eid=2-s2.0-84864025634&partnerID=40&md5=87a2e4ed41f974cf8f15fc55a9e79f0a http://link.springer.com/chapter/10.1007/978-1-4614-2425-3₃₇
_version_ 1643688457120251904
score 13.211869

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