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Application of a mode shape derivative-based damage index in artificial neural network for structural damage identification in shear frame building

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Abstract

Artificial Neural Networks (ANN) have been proven applicable for updating finite-element (FE) baseline model and structural damage assessment. Most ANN-based damage identification methods use natural frequencies and mode shapes as input layer, limiting their application to quantifying single symmetrical damage in small structures. However, getting higher modal information of a structure is a crucial challenge in practice. As of late, researchers began utilizing mode shape derivatives as input layer in ANN to defeat the challenges for damage assessment in real-life structures. This study, therefore, proposes an ANN-based damage assessment method that employs the change in the first mode shape slope (CFMSS) damage index (DI) as input layer in ANN. For single-damage scenarios, the CFMSS-based DI has been able to detect, locate, and quantify the damage. For multiple-damage scenarios, the DI and corresponding stiffness reduction (SR) are fit as input and output layers, respectively, in ANN to measure the damage severity. Structural damage intensity is indicated as rate of decrease in story stiffness compared to baseline model. The efficiency of the proposed damage identification method is demonstrated through a nine-story numerical shear frame model and an experimental test on a three-story steel shear frame model.

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References

  1. Rytter A (1993) Vibration based inspection of civil engineering structures. Ph.D. thesis, Aaalborg University, Denmark

  2. Lee YS, Chung MJ (2001) A study on crack detection using eigen frequency test data. Comput Struct 77(3):327–342

    Article  Google Scholar 

  3. Su WC, Huang CS, Hung SL, Chen LJ, Lin WJ (2012) Locating damaged storys in a shear building based on its sub-structural natural frequencies. Eng Struct 39:126–138

    Article  Google Scholar 

  4. Khiem NT, Toan LK (2014) A novel method for crack detection in beam like structures by measurements of natural frequencies. J Sound Vib 333(18):4084–4103

    Article  Google Scholar 

  5. Pandey A, Biswas M, Samman M (1991) Damage detection in curvature mode shapes. J Sound Vib 145(2):321–332

    Article  Google Scholar 

  6. Zhu H, Li L, He XQ (2011) Damage detection method for shear buildings using the change in first mode shape slopes. Comput Struct 89(9–10):733–743

    Article  Google Scholar 

  7. Nguyen KV (2014) Mode shapes analysis of a cracked beam and its application for crack detection. J Sound Vib 333(3):848–872

    Article  Google Scholar 

  8. Shi ZY, Law SS, Zhang LM (2002) Improved damage quantification from elemental modal strain energy change. J Eng Mech 128(5):521. https://doi.org/10.1061/(asce)0733-9399(2002)128:5(521)

    Article  Google Scholar 

  9. Koo KY, Sung SH, Jung HJ (2011) Damage quantification of shear buildings using deflections obtained by modal flexibility. Smart Mater Struct 20(4):045010

    Article  Google Scholar 

  10. Yan YJ, Cheng L, Wu ZY, Yam LH (2007) Development of vibration-based structural damage detection technique. Mech Syst Signal Process 21(5):2198–2211

    Article  Google Scholar 

  11. Gonzalez I, Karoumi R (2015) BWIM aided damage detection in bridge using machine learning. J Civ Struct Health Monit 5(5):715–725

    Article  Google Scholar 

  12. Neves AC, González I, Leander J, Karoumi R (2017) Structural health monitoring of bridges: a model-free ANN-based approach to damage detection. J Civ Struct Health Monit 7(5):689–702

    Article  Google Scholar 

  13. Nazarian E, Taylor T, Weifeng T, Ansari F (2018) Machine-learning based approach for post event assessment of damage in a turn-of-the-century building structure. J Civ Struct Health Monit 8(2):237–251

    Article  Google Scholar 

  14. Ni PH, Law SS (2016) Hybrid computational strategy for structural damage detection with short-term monitoring data. Mech Syst Signal Process 70–71:650–663

    Article  Google Scholar 

  15. Dackermann U, Li J, Samali B (2010) Dynamic-based damage identification using neural network ensembles and damage index method. Adv Struct Eng 13(6):1001–1016

    Article  Google Scholar 

  16. Hakim SJS, Abdul Razak H, Ravanfar SA (2015) Fault diagnosis on beam-like structures from modal parameters using artificial neural networks. Measurements 76:45–61

    Google Scholar 

  17. Liu H, Song G, Jiao Y, Zhang P, Wang X (2014) Damage identification of bridge on modal flexibility and neural network improved by particle swarm optimization. Math Probl Eng 2014:640925. https://doi.org/10.1155/2014/640925

    Google Scholar 

  18. Ghadimi S, Kourehli SS (2016) Multiple crack detection in Euler beams using extreme learning machine. KSCE J Civ Eng 21(1):389–396

    Article  Google Scholar 

  19. Tan ZX, Thambiratnam DP, Chan THT, Abdul Razak H (2017) Detecting damage in steel beams using modal strain energy based damage index and Artificial Neural Network. Eng Fail Anal 79:253–262

    Article  Google Scholar 

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

    Article  Google Scholar 

  21. Pathirage CSN, Li J, Li L, Hoa H, Liu W, Ni P (2018) Structural damage identification based on autoencoder neural networks and deep learning. Eng Struct 172(1):13–28

    Article  Google Scholar 

  22. Chang CM, Lin TK, Chang CW (2018) Applications of neural network models for structural health monitoring based on derived modal properties. Measurement 129:457–470

    Article  Google Scholar 

  23. Bakhary N, Hao H, Deeks AJ (2010) Substructuring technique for damage detection using statistical multi-stage artificial neural network. Adv Struct Eng 13(4):619–639

    Article  Google Scholar 

  24. Ding S, Li H, Su C, Yu J, Jin F (2013) Evolutionary artificial neural networks: a review. Artif Intell Revis 39(3):251–260

    Article  Google Scholar 

  25. Ticknor Jonat HL (2013) A Bayesian regularized artificial neural network for stock market forecasting. Expert Syst Appl 40(14):5501–5506

    Article  Google Scholar 

  26. Burdern F, Winkler D (2008) Bayesian regularization of neural networks. Method Mol Biol 458:25–44. https://doi.org/10.1007/978-1-60327-101-1_3

    Google Scholar 

  27. Inman DJ (2008) Engineering vibration. Prentice Hall, Upper Saddle River

    Google Scholar 

  28. Friswell M, Mottershead JE (1995) Finite element model updating structural dynamics. Springer, Netherlands

    Book  MATH  Google Scholar 

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Acknowledgements

The authors would like to thank the Department of Civil Engineering NIT Durgapur, India for providing necessary support and cooperation for this Investigation. Support from R & D Scheme of Technical Education Quality Improvement Programme (TEQIP) of Government of India is gratefully acknowledged.

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Authors and Affiliations

  1. Department of Civil Engineering, National Institute of Technology Durgapur, A-Zone, M.G. Avenue, Durgapur, West Bengal, 713209, India

    Animesh Paral, Dilip Kr. Singha Roy & Amiya K. Samanta

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  1. Animesh Paral
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  2. Dilip Kr. Singha Roy
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  3. Amiya K. Samanta
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Paral, A., Singha Roy, D.K. & Samanta, A.K. Application of a mode shape derivative-based damage index in artificial neural network for structural damage identification in shear frame building. J Civil Struct Health Monit 9, 411–423 (2019). https://doi.org/10.1007/s13349-019-00342-x

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