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Damage Identification Using Experimental Modal Analysis and Adaptive Neuro-Fuzzy Interface System (ANFIS)

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Topics in Modal Analysis I, Volume 5

Abstract

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 (R2). The ANFIS model could predict the severity of damage with MSE of 0.0049 and correlation coefficient (R2) 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.

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References

  1. Mehrjoo M, Khaji N, Moharrami H, Bahreininejad A (2008) Damage detection of truss bridge joints using artificial neural networks. J Expert Syst Appl 35(3):1122–1131

    Article  Google Scholar 

  2. Park JH, Kim JT, Hong DS, Ho DD, Yi JH (2009) Sequential damage detection approaches for beams using time-modal features and artificial neural networks. J Sound Vib 323:451–474

    Article  Google Scholar 

  3. Suh MW, Shim MB, Kim MY (2000) Crack identification using hybrid neuro-genetic technique. J Sound Vib 234(4):617–635

    Article  Google Scholar 

  4. Rosales MB, Filipich CP, Buezas FS (2009) Crack detection in beam-like structures. J Eng Struct 31:2257–2264

    Article  Google Scholar 

  5. Ramadas C, Balasubramaniam K, Joshi M, Krishnamurthy CV (2008) Detection of transverse cracks in a composite beam using combined features of lamb wave and vibration techniques in ANN environment. Int J Smart Sensing Intell Syst 1(10):970–984

    Google Scholar 

  6. Lam F, Ng CT (2008) The selection of pattern features for structural damage detection using an extended Bayesian ANN algorithm. J Eng Struct 30:2762–2770

    Article  Google Scholar 

  7. Gonzalez MP, Zapico JL (2008) Seismic damage identification in buildings using neural networks and modal data. J Comput Struct 86(3):416–426

    Article  Google Scholar 

  8. Chandrashekhar M, Ganguli R (2011) Structural damage detection using modal curvature and fuzzy logic. Struct Heal Monit 10:115–129

    Article  Google Scholar 

  9. Jang JSR (1993) ANFIS: adaptive network-based fuzzy inference systems. IEEE Trans Syst Man Cyber 23(3):665–685

    Article  Google Scholar 

  10. Salajegheh E, Salajegheh J, Seyedpoor SM, Khatibinia M (2009) Optimal design of geometrically nonlinear space trusses using an adaptive neuro-fuzzy inference system. Sci Iran Trans Civ Eng 16:403–414

    Google Scholar 

  11. Fonseca ET, Vellasco PCG, Vellasco MMBR, Andrade SAL (2008) A neuro-fuzzy evaluation of steel beams patch load behavior. Adv Eng Softw 39:558–572

    Article  Google Scholar 

  12. Samandar A (2011) A model of adaptive neural-based fuzzy inference system (ANFIS) for prediction of friction coefficient in open channel flow. Sci Res Essays 6(5):1020–1027

    Google Scholar 

  13. El-Shafie A, Jaafer O, Seyed A (2011) Adaptive neuro-fuzzy inference system based model for rainfall forecasting in Klang river, Malaysia. Int J Phys Sci 6(12):2875–2888

    Google Scholar 

  14. Karaagac B, Inal M, Deniz V (2011) Predicting optimum cure time of rubber compounds by means of ANFIS. Mater Des xxx:xxx–xxx

    Google Scholar 

  15. Jalalifar H, Mojedifar S, Sahebi AA, Nezamabadipour H (2011) Application of the adaptive neuro-fuzzy inference system for prediction of a rock engineering classification system. Comput Geotech 38:783–790

    Article  Google Scholar 

  16. Wang YM, Elhag TMS (2008) An adaptive neuro-fuzzy inference system for bridge risk assessment. Expert Syst Appl 34(4):3099–3106

    Article  Google Scholar 

  17. Jang JSR (1992) Self-learning fuzzy controllers based on temporal backpropagation. IEEE Trans Neural Networks 3(5):714–723

    Article  Google Scholar 

  18. Jang JSR (1997) Neuro-fuzzy and soft computing. Prentice-Hall, New Jersey

    Google Scholar 

  19. Takagi T, Sugeno M (1985) Fuzzy identification of systems and its applications to modeling and control. IEEE Trans Syst Man Cyber 15:116–132

    Article  MATH  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Civil Engineering, University of Malaya, Kuala Lumpur, Malaysia

    Seyed Jamalaldin S. Hakim & H. Abdul Razak

Authors
  1. Seyed Jamalaldin S. Hakim
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  2. H. Abdul Razak
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Corresponding author

Correspondence to Seyed Jamalaldin S. Hakim .

Editor information

Editors and Affiliations

  1. University of Cincinnati, Cincinnati, 45221, Ohio, USA

    R. Allemang

  2. Michigan Technological University, Houghton, 49931-1295, USA

    J. De Clerck

  3. University Massachusetts Lowell, Lowell, 01854, USA

    C. Niezrecki

  4. Michigan Technological University, Houghton, 49931-1295, USA

    J.R. Blough

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© 2012 The Society for Experimental Mechanics, Inc. 2012

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Hakim, S.J.S., Razak, H.A. (2012). Damage Identification Using Experimental Modal Analysis and Adaptive Neuro-Fuzzy Interface System (ANFIS). In: Allemang, R., De Clerck, J., Niezrecki, C., Blough, J. (eds) Topics in Modal Analysis I, Volume 5. Conference Proceedings of the Society for Experimental Mechanics Series. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-2425-3_37

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  • DOI: https://doi.org/10.1007/978-1-4614-2425-3_37

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  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-1-4614-2424-6

  • Online ISBN: 978-1-4614-2425-3

  • eBook Packages: EngineeringEngineering (R0)

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