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Adequacy of first mode shape differences for damage identification of cantilever structures using neural networks

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Abstract

Damage identification of structures has attracted attention of researchers due to sudden collapse of in-service structures. Modal parameters and their derivatives have been widely employed in the proposed damage identification techniques. However, mode shape differences have been shown to be an ideal damage indicator when used as the input vector of neural networks. Since measurement of higher-order mode shapes is very difficult to be acquired reliably, this study investigated the adequacy of using only the first mode shape differences for damage identification using artificial neural networks. Results of numerical and experimental studies on a cantilever beam indicated that the first mode shape differences alone can accurately localize imposed damages. Damage intensity at the lower levels of cantilever beam was predicted with less than 15% error; however, prediction of damage intensity at the free end of the beam encountered large discrepancies. It was also found that damage localization was successful even when the first mode shape differences were measured at few points along the beam.

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References

  1. Vafaei M, Adnan AB, Rahman AB Abd (2014) A neuro-wavelet technique for seismic damage identification of cantilever structures. Struct Infrastruct Eng 10(12):1666–1684

    Article  Google Scholar 

  2. Fan W, Qiao P (2011) Vibration-based damage identification methods: a review and comparative study. Struct Health Monit 10(1):83–111

    Article  Google Scholar 

  3. Vafaei M, Adnan AB (2014) Seismic damage detection of tall airport traffic control towers using wavelet analysis. Struct Infrastruct Eng 10(1):106–127

    Article  Google Scholar 

  4. Zhan JW, Xia H, Chen SY, De Roeck G (2011) Structural damage identification for railway bridges based on train-induced bridge responses and sensitivity analysis. J Sound Vib 330(4):757–770

    Article  Google Scholar 

  5. Arangio S, Bontempi F (2015) Structural health monitoring of a cable-stayed bridge with Bayesian neural networks. Struct Infrastruct Eng 11(4):575–587

    Article  Google Scholar 

  6. Vafaei M, Adnan AB, Rahman AB Abd (2013) Real-time seismic damage detection of concrete shear walls using artificial neural networks. J Earthq Eng 17(1):137–154

    Article  Google Scholar 

  7. Vafaei M, Alih SC, Rahman ABA, Adnan AB (2015) A wavelet-based technique for damage quantification via mode shape decomposition. Struct Infrastruct Eng 11(7):869–883

    Article  Google Scholar 

  8. Bandara RP, Chan TH, Thambiratnam DP (2014) Frequency response function based damage identification using principal component analysis and pattern recognition technique. Eng Struct 66:116–128

    Article  Google Scholar 

  9. Wu X, Ghaboussi J, Garrett JH (1992) Use of neural networks in detection of structural damage. Comput Struct 42(4):649–659

    Article  Google Scholar 

  10. Sahin M, Shenoi RA (2003) Quantification and localization of damage in beam-like structures by using artificial neural networks with experimental validation. Eng Struct 25(14):1785–1802

    Article  Google Scholar 

  11. Li J, Dackermann U, Xu Y, Samali B (2011) Damage identification in civil engineering structures utilizing PCA-compressed residual frequency response functions and neural network ensembles. Struct Control Health Monit 18(2):207–226

    Article  Google Scholar 

  12. Yam LH, Yan YJ, Jiang JS (2003) Vibration-based damage detection for composite structures using wavelet transform and neural network identification. Compos Struct 60(4):403–412

    Article  Google Scholar 

  13. Lee JJ, Lee JW, Yi JH, Yun CB, Jung YJ (2005) Neural network based damage detection for bridges considering errors in baseline finite elements models. J Sound Vib 280(3):555–578

    Article  Google Scholar 

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

    Article  Google Scholar 

  15. Zang C, Imregun M (2001) Structural damage detection using artificial neural networks and measured FRF data reduced via principal component projection. J Sound Vib 242(5):813–827

    Article  Google Scholar 

  16. Qi GZ, Xun G, Xiaozhai Q, Dong W, Chang P (2005) Local measurement for structural health monitoring. Earthq Eng Eng Vib 4(1):165–172

    Article  Google Scholar 

  17. Wilson EL, Habibullah A (2003) Structural Analysis Program Sap 2000. Berkeley, Calif. Computer and Structures Inc, Users Manual

    Google Scholar 

  18. Dackermann U (2010) Vibration-based damage identification methods for civil engineering structures using artificial neural networks. Ph.D. Thesis, University of Technology Sydney. Sydney, Australia

  19. Lyn Dee G, Bakhary N, Abdul Rahman A, Hisham Ahmad B (2011) A comparison of artificial neural network learning algorithms for vibration-based damage detection. Adv Mater Res 163:2756–2760

    Google Scholar 

  20. https://en.wikipedia.org/wiki/Westerholt_Power_Station

  21. http://airwaysnews.com/html/airplanes-and-airports/tampa-st-petersburg-international-airport-terminal-photos-planespotting-and-history-tampa-fl/air-traffic-control-tower/9388

  22. https://en.wikipedia.org/wiki/Transmission_tower

  23. http://news.nationalpost.com/news/canada/a-115-metre-high-wind-turbine-on-the-bruce-peninsula-begins-listing-facility-owners-tether-it

Download references

Acknowledgement

Authors are thankful to the comments from anonymous reviewers. In addition, the financial support from Malaysian Ministry of Higher Education under the Grant No. R.J130000.7822.4F760 is greatly acknowledged.

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Authors and Affiliations

  1. Center for Forensic Engineering, Faculty of Civil Engineering, Universiti Teknologi Malaysia, 81310, Skudai, Johor, Malaysia

    Mohammadreza Vafaei

  2. Institute of Noise and Vibration, Faculty of Civil Engineering, Universiti Teknologi Malaysia, 81310, Skudai, Johor, Malaysia

    Sophia C. Alih

Authors
  1. Mohammadreza Vafaei
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  2. Sophia C. Alih
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Correspondence to Mohammadreza Vafaei.

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Vafaei, M., Alih, S.C. Adequacy of first mode shape differences for damage identification of cantilever structures using neural networks. Neural Comput & Applic 30, 2509–2518 (2018). https://doi.org/10.1007/s00521-017-2846-6

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  • DOI: https://doi.org/10.1007/s00521-017-2846-6

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