Hakim, S.J.S. and Razak, H.A. (2012) Damage identification using experimental modal analysis and adaptive neurofuzzy interface system (ANFIS). In: 30th IMAC, A Conference on Structural Dynamics, 2012, 2012, Jacksonville, FL.

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Abstract
The adaptive neurofuzzy inference system (ANFIS) is a process for mapping from a given input to a single output using the fuzzy logic and neuroadaptive learning algorithms. Using a given inputoutput 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 neurofuzzy inference system to predict the damage severity of the structure with high accuracy. Â© The Society for Experimental Mechanics, Inc. 2012.
Item Type:  Conference or Workshop Item (Paper) 

Additional Information:  Conference code: 89958 Export Date: 16 December 2013 Source: Scopus doi: 10.1007/9781461424253₃₇ Language of Original Document: English Correspondence Address: Hakim, S.J.S.; Department of Civil Engineering, University of Malaya, Kuala Lumpur, Malaysia; email: jamalhakim@siswa.um.edu.my References: Mehrjoo, M., Khaji, N., Moharrami, H., Bahreininejad, A., Damage detection of truss bridge joints using artificial neural networks (2008) J Expert Syst Appl, 35 (3), pp. 11221131; Park, J.H., Kim, J.T., Hong, D.S., Ho, D.D., Yi, J.H., Sequential damage detection approaches for beams using timemodal features and artificial neural networks (2009) J Sound Vib, 323, pp. 451474; Suh, M.W., Shim, M.B., Kim, M.Y., Crack identification using hybrid neurogenetic technique (2000) J Sound Vib, 234 (4), pp. 617635; Rosales, M.B., Filipich, C.P., Buezas, F.S., Crack detection in beamlike structures (2009) J Eng Struct, 31, pp. 22572264; Ramadas, C., Balasubramaniam, K., Joshi, M., Krishnamurthy, C.V., Detection of transverse cracks in a composite beam using combined features of lamb wave and vibration techniques in ANN environment (2008) Int J Smart Sensing Intell Syst, 1 (10), pp. 970984; Lam, F., Ng, C.T., The selection of pattern features for structural damage detection using an extended Bayesian ANN algorithm (2008) J Eng Struct, 30, pp. 27622770; Gonzalez, M.P., Zapico, J.L., Seismic damage identification in buildings using neural networks and modal data (2008) J Comput Struct, 86 (3), pp. 416426; Chandrashekhar, M., Ganguli, R., Structural damage detection using modal curvature and fuzzy logic (2011) Struct Heal Monit, 10, pp. 115129; Jang, J.S.R., ANFIS: Adaptive networkbased fuzzy inference systems (1993) IEEE Trans Syst Man Cyber, 23 (3), pp. 665685; Salajegheh, E., Salajegheh, J., Seyedpoor, S.M., Khatibinia, M., Optimal design of geometrically nonlinear space trusses using an adaptive neurofuzzy inference system (2009) Sci Iran Trans Civ Eng, 16, pp. 403414; Fonseca, E.T., Vellasco, P.C.G., Mmbr, V., Andrade, S.A.L., A neurofuzzy evaluation of steel beams patch load behavior (2008) Adv Eng Softw, 39, pp. 558572; Samandar, A., A model of adaptive neuralbased fuzzy inference system (ANFIS) for prediction of friction coefficient in open channel flow (2011) Sci Res Essays, 6 (5), pp. 10201027; ElShafie, A., Jaafer, O., Seyed, A., Adaptive neurofuzzy inference system based model for rainfall forecasting in Klang river, Malaysia (2011) Int J Phys Sci, 6 (12), pp. 28752888; Karaagac, B., Inal, M., Deniz, V., Predicting optimum cure time of rubber compounds by means of ANFIS (2011) Mater des, 30, pp. xxxxxx; Jalalifar, H., Mojedifar, S., Sahebi, A.A., Nezamabadipour, H., Application of the adaptive neurofuzzy inference system for prediction of a rock engineering classification system (2011) Comput Geotech, 38, pp. 783790; Wang, Y.M., Elhag, T.M.S., An adaptive neurofuzzy inference system for bridge risk assessment (2008) Expert Syst Appl, 34 (4), pp. 30993106; Jang, J.S.R., Selflearning fuzzy controllers based on temporal backpropagation (1992) IEEE Trans Neural Networks, 3 (5), pp. 714723; Jang, J.S.R., (1997) Neurofuzzy and Soft Computing, , PrenticeHall, New Jersey; Takagi, T., Sugeno, M., Fuzzy identification of systems and its applications to modeling and control (1985) IEEE Trans Syst Man Cyber, 15, pp. 116132 
Uncontrolled Keywords:  Adaptive neurofuzzy inference system (ANFIS), Damage detection, Mean square error (MSE), Modal analysis, Adaptive neurofuzzy inference system, ANFIS model, Coefficient of determination, Correlation coefficient, Damage Identification, Data sets, Experimental modal analysis, Fuzzy inference systems, Fuzzy membership function, Input parameter, Inputoutput data, Interface system, Least Square, Mode shapes, NeuroFuzzy, Single output, Steel girder bridge, Structural stiffness, Forecasting, Fuzzy logic, Fuzzy systems, Learning algorithms, Least squares approximations, Natural frequencies, Soil structure interactions, Structural dynamics, Mean square error 
Subjects:  T Technology > TA Engineering (General). Civil engineering (General) 
Divisions:  Faculty of Engineering 
Depositing User:  Mr Jenal S 
Date Deposited:  27 Jan 2014 01:04 
Last Modified:  27 Jan 2014 01:04 
URI:  http://eprints.um.edu.my/id/eprint/9055 
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