Adaptive Neuro Fuzzy Inference System (ANFIS) and Artificial Neural Networks (ANNs) for structural damage identification

In this paper, adaptive neuro-fuzzy inference system (ANFIS) and artificial neural networks (ANNs) techniques are developed and applied to identify damage in a model steel girder bridge using dynamic parameters. The required data in the form of natural frequencies are obtained from experimental modal analysis. A comparative study is made using the ANNs and ANFIS techniques and results showed that both ANFIS and ANN present good predictions. However the proposed ANFIS architecture using hybrid learning algorithm was found to perform better than the multilayer feedforward ANN which learns using the backpropagation algorithm. This paper also highlights the concept of ANNs and ANFIS followed by the detail presentation of the experimental modal analysis for natural frequencies extraction.

[1]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[2]  Ranjan Ganguli,et al.  Composite material and piezoelectric coefficient uncertainty effects on structural health monitoring using feedback control gains as damage indicators , 2011 .

[3]  B. Karaağaç,et al.  Predicting optimum cure time of rubber compounds by means of ANFIS , 2012 .

[4]  Mun-Bo Shim,et al.  Crack identification using neuro-fuzzy-evolutionary technique , 2002 .

[5]  S. Talukdar,et al.  A comparative study of compressive, flexural, tensile and shear strength of concrete with fibres of different origins , 2007 .

[6]  Ranjan Ganguli,et al.  Structural Damage Detection Using Modal Curvature and Fuzzy Logic , 2009 .

[7]  Chung Bang Yun,et al.  Neural networks-based damage detection for bridges considering errors in baseline finite element models , 2003 .

[8]  Jin H. Huang,et al.  Detection of cracks using neural networks and computational mechanics , 2002 .

[9]  Hossein Nezamabadi-pour,et al.  Application of the ANFIS model in deflection prediction of concrete deep beam , 2013 .

[10]  Marta B. Rosales,et al.  Crack detection in beam-like structures , 2009 .

[11]  Yi-Qing Ni,et al.  Experimental investigation of seismic damage identification using PCA-compressed frequency response functions and neural networks , 2006 .

[12]  Enrico Zio,et al.  A neuro-fuzzy technique for fault diagnosis and its application to rotating machinery , 2009, Reliab. Eng. Syst. Saf..

[13]  Jyh-Shing Roger Jang,et al.  Self-learning fuzzy controllers based on temporal backpropagation , 1992, IEEE Trans. Neural Networks.

[14]  Ri Levin,et al.  DYNAMIC FINITE ELEMENT MODEL UPDATING USING NEURAL NETWORKS , 1998 .

[15]  E. Salajegheh,et al.  Optimal Design of Geometrically Nonlinear Space Trusses Using an Adaptive Neuro-Fuzzy Inference System , 2009 .

[16]  Martin T. Hagan,et al.  Neural network design , 1995 .

[17]  Mohd Saleh Jaafar,et al.  An approach to predict ultimate bearing capacity of surface footings using artificial neural network , 2008 .

[18]  Fi-John Chang,et al.  Adaptive neuro-fuzzy inference system for prediction of water level in reservoir , 2006 .

[19]  Ardeshir Bahreininejad,et al.  Damage detection of truss bridge joints using Artificial Neural Networks , 2008, Expert Syst. Appl..

[20]  H. Joel Trussell,et al.  Fuzzy inference systems implemented on neural architectures for motor fault detection and diagnosis , 1999, IEEE Trans. Ind. Electron..

[21]  H. Abdul Razak,et al.  Damage detection of steel bridge girder using Artificial Neural Networks , 2012 .

[22]  Seung-Chang Lee,et al.  Prediction of concrete strength using artificial neural networks , 2003 .

[23]  Ying-Ming Wang,et al.  An adaptive neuro-fuzzy inference system for bridge risk assessment , 2008, Expert Syst. Appl..

[24]  Peter Avitabile,et al.  Efficient techniques for forced response involving linear modal components interconnected by discrete nonlinear connection elements , 2009 .

[25]  Chih-Chen Chang,et al.  Selection of training samples for model updating using neural networks , 2002 .

[26]  M. M. Reda Taha,et al.  Damage identification for structural health monitoring using fuzzy pattern recognition , 2005 .

[27]  Ranjan Ganguli,et al.  Health monitoring of a helicopter rotor in forward flight using fuzzy logic , 2002 .

[28]  S.J.S. Hakim,et al.  Development and Applications of Artificial Neural Network for Prediction of Ultimate Bearing Capacity of Soil and Compressive Strength of Concrete , 2006 .

[29]  S. Sivanandam,et al.  Introduction to Fuzzy Logic using MATLAB , 2006 .

[30]  Hossein Nezamabadi-pour,et al.  Application of the adaptive neuro-fuzzy inference system for prediction of a rock engineering classification system , 2011 .

[31]  Michio Sugeno,et al.  Fuzzy identification of systems and its applications to modeling and control , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[32]  Abdulkadir Çevik,et al.  Neuro-fuzzy modeling of rotation capacity of wide flange beams , 2011, Expert Syst. Appl..

[33]  Myung-Won Suh,et al.  Crack Identification Using Hybrid Neuro-Genetic Technique , 2000 .

[34]  Dayal R. Parhi,et al.  Smart crack detection of a cracked cantilever beam using fuzzy logic technology with hybrid membership functions , 2011 .

[35]  Jyh-Shing Roger Jang,et al.  ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..

[36]  Qiao Hu,et al.  Fault diagnosis of rotating machinery based on multiple ANFIS combination with GAs , 2007 .

[37]  Ömer Civalek Flexural and axial vibration analysis of beams with different support conditions using artificial neural networks , 2004 .

[38]  Shi-jie Zheng,et al.  A genetic fuzzy radial basis function neural network for structural health monitoring of composite laminated beams , 2011, Expert Syst. Appl..

[39]  Marley M. B. R. Vellasco,et al.  A neuro-fuzzy evaluation of steel beams patch load behaviour , 2008, Adv. Eng. Softw..

[40]  C. S. Cai,et al.  Application of artificial neural networks to the response prediction of geometrically nonlinear truss structures , 2007 .

[41]  Michio Sugeno,et al.  Industrial Applications of Fuzzy Control , 1985 .

[42]  Xiao Zhi Gao,et al.  Soft computing methods in motor fault diagnosis , 2001, Appl. Soft Comput..

[43]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

[44]  A. N. Galybin,et al.  Crack identification in curvilinear beams by using ANN and ANFIS based on natural frequencies and frequency response functions , 2011, Neural Computing and Applications.

[45]  Mohd Saleh Jaafar,et al.  Application of Artificial Neural Networks to Predict Compressive Strength of High Strength Concrete , 2011 .

[46]  Yong Lu,et al.  A two-level neural network approach for dynamic FE model updating including damping , 2004 .

[47]  Ayhan am,et al.  A model of adaptive neural-based fuzzy inference system (ANFIS) for prediction of friction coefficient in open channel flow , 2011 .

[48]  S. Kumar,et al.  Neuro-fuzzy approaches for pipeline condition assessment , 2007 .

[49]  D. Jeng,et al.  Estimation of pile group scour using adaptive neuro-fuzzy approach , 2007 .

[50]  Hosein Naderpour,et al.  Prediction of FRP-confined compressive strength of concrete using artificial neural networks , 2010 .