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Data-driven method of damage detection using sparse sensors installation by SEREPa

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Abstract

This paper presents a model-based method of damage detection and severity identification in structural elements. The model-based method is performed with a finite element model. One of the important challenges in damage identification problems is lack of measured degrees of freedom and limitations of installed sensors on the structures. The new approach of this study is the use of expanded mode shapes data to train artificial neural network (ANN). In this study, the measured mode shapes are expanded by SEREPa. SEREPa expansion is developed based on the System-Equivalent Reduction and Expansion Process (SEREP), which is a non-smooth method and protects the measured data. ANN was then trained through the expanded data as inputs, location and severity of damage as outputs. The algorithm used to train ANN is scaled conjugate gradient. The advantage of this algorithm is that less data storage space is used and lower computation costs are needed. To show SEREPa’s efficiency in estimating unmeasured mode shapes, an experimental example containing a truss tower was presented. Two numerical examples including a plane truss and a space truss were presented to illustrate efficiency of damage detection method. Finally, the proposed method was verified by an experimental example. Damage prediction results for both numerical and experimental examples indicated an acceptable accuracy of the proposed method.

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References

  1. Farshadi M, Esfandiari A, Vahedi M (2017) Structural model updating using incomplete transfer function and modal data. Struct Control Health Monit 24(7):1–13. https://doi.org/10.1002/stc.1932

    Article  Google Scholar 

  2. Kaveh A, Dadras A (2018) Structural damage identification using an enhanced thermal exchange optimization algorithm. Eng Optim 50(3):430–451. https://doi.org/10.1080/0305215X.2017.1318872

    Article  Google Scholar 

  3. Wei Z, Liu J, Lu Z (2018) Structural damage detection using improved particle swarm optimization. Inverse Probl Sci Eng 26(6):792–810. https://doi.org/10.1080/17415977.2017.1347168

    Article  MathSciNet  MATH  Google Scholar 

  4. Zare Hosseinzadeh A, Ghodrati Amiri G, Seyed Razzaghi SA (2017) Model-based identification of damage from sparse sensor measurements using Neumann series expansion. Inverse Probl Sci Eng 25(2):239–259. https://doi.org/10.1080/17415977.2016.1160393

    Article  MathSciNet  MATH  Google Scholar 

  5. Doebling SW, Farrar CR, Prime MB (1998) A summary review of vibration-based damage identification methods. Los Alamos National Laboratory, Massachusetts, USA, Rep. LA-UR-98-0375

  6. Hakim SJS, Razak HA (2014) Modal parameters based structural damage detection using artificial neural networks–a review. Smart Struct Syst 14(2):159–189

    Article  Google Scholar 

  7. Hosseini M, Khoshnoudian F, Esfandiari A (2017) Improved data expansion method used in damage detection method. J Civ Struct Health Monit 7(1):15–27. https://doi.org/10.1007/s13349-016-0205-4

    Article  Google Scholar 

  8. Miguel LF, de Menezes RR, Miguel LFF (2006) Mode shape expansion from data-based system identification procedures. Mecanica Computacional 25:1593–1602

    Google Scholar 

  9. Ghannadi P, Kourehli SS (2018) Investigation of the accuracy of different finite element model reduction techniques. Struct Monit Maint 5(3):417–428. https://doi.org/10.12989/smm.2018.5.3.417

    Article  Google Scholar 

  10. Kourehli SS (2015) Damage assessment in structures using incomplete modal data and artificial neural network. Int J Struct Stab Dyn. https://doi.org/10.1142/S0219455414500874

    Article  Google Scholar 

  11. Dinh-Cong D, Vo-Duy T, Nguyen-Thoi T (2018) Damage assessment in truss structures with limited sensors using a two-stage method and model reduction. Appl Soft Comput 66:264–277. https://doi.org/10.1016/j.asoc.2018.02.028

    Article  Google Scholar 

  12. Marwala T (2010) Finite element model updating using computational intelligence techniques: applications to structural dynamics. Springer, London. https://doi.org/10.1007/978-1-84996-323-7

    Book  MATH  Google Scholar 

  13. Liu F, Li H (2012) Rapid direct-mode shape expansion for offshore jacket structures using a hybrid vector. Ocean Eng 51:119–128. https://doi.org/10.1016/j.oceaneng.2012.05.015

    Article  Google Scholar 

  14. Gordan M, Razak HA, Ismail Z, Ghaedi K (2017) Recent developments in damage identification of structures using data mining. Latin Am J Solids Struct 14(13):2373–2401. https://doi.org/10.1590/1679-78254378

    Article  Google Scholar 

  15. Hakim SJS, Razak HA (2013) Structural damage detection of steel bridge girder using artificial neural networks and finite element models. Steel Compos Struct 14(4):367–377

    Article  Google Scholar 

  16. Nazari F, Baghalian S (2011) A new method for damage detection in symmetric beams using artificial neural network and finite element method. Int J Eng Appl Sci 3(2):30–36

    Google Scholar 

  17. Weinstein JC, Sanayei M, Brenner BR (2018) Bridge damage identification using artificial neural networks. J Bridge Eng. https://doi.org/10.1061/(ASCE)BE.1943-5592.0001302

    Article  Google Scholar 

  18. Goh LD, Bakhary N, Rahman AA, Ahmad BH (2016) Application of neural network for prediction of unmeasured mode shape in damage detection. Adv Struct Eng 16(1):99–113. https://doi.org/10.1260/1369-4332.16.1.99

    Article  Google Scholar 

  19. Kourehli SS (2018) Prediction of unmeasured mode shapes and structural damage detection using least squares support vector machine. Struct Monit Maint 5(3):379–390. https://doi.org/10.12989/smm.2018.5.3.379

    Article  Google Scholar 

  20. Au FTK, Cheng YS, Tham LG, Bai ZZ (2003) Structural damage detection based on a micro-genetic algorithm using incomplete and noisy modal test data. J Sound Vib 259(5):1081–1094. https://doi.org/10.1006/jsvi.2002.5116

    Article  Google Scholar 

  21. Ghiasi R, Ghasemi MR, Noori M (2018) Comparative studies of metamodeling and AI-based techniques in damage detection of structures. Adv Eng Softw 125:101–112. https://doi.org/10.1016/j.advengsoft.2018.02.006

    Article  Google Scholar 

  22. Zhang Z, Sun C, Bridgelall R, Sun M (2018) Road profile reconstruction using connected vehicle responses and wavelet analysis. J Terrramech 80:21–30. https://doi.org/10.1016/j.jterra.2018.10.004

    Article  Google Scholar 

  23. Zhang Z, Sun C, Bridgelall R, Sun M (2018) Application of a machine learning method to evaluate road roughness from connected vehicles. J Transp Eng Part B Pavements. https://doi.org/10.1061/JPEODX.0000074

    Article  Google Scholar 

  24. O’Callahan J and Li P (1994) A non smoothing SEREP process for modal expansion. In: Proceedings of the 12th international modal analysis

  25. Kaveh A, Zolghadr A (2015) An improved CSS for damage detection of truss structures using changes in natural frequencies and mode shapes. Adv Eng Softw 80:93–100. https://doi.org/10.1016/j.advengsoft.2014.09.010

    Article  Google Scholar 

  26. Qu ZQ (2013) Model order reduction techniques with applications in finite element analysis. Springer, Berlin. https://doi.org/10.1007/978-1-4471-3827-3

    Book  Google Scholar 

  27. Weber B, Paultre P (2009) Damage identification in a truss tower by regularized model updating. J Struct Eng 136(3):307–316. https://doi.org/10.1061/(ASCE)ST.1943-541X.0000105

    Article  Google Scholar 

  28. 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. https://doi.org/10.4028/www.scientific.net/AMR.163-167.2756

    Article  Google Scholar 

  29. Møller MF (1993) A scaled conjugate gradient algorithm for fast supervised learning. Neural Netw 6(4):525–533. https://doi.org/10.1016/S0893-6080(05)80056-5

    Article  Google Scholar 

  30. Kaveh A, Bakhshpoori T, Hamze-Ziabari SM (2018) GMDH-based prediction of shear strength of FRP-RC beams with and without stirrups. Comput Concr 22(02):197–207. https://doi.org/10.12989/cac.2018.22.2.197

    Article  Google Scholar 

  31. Beale MH, Hagan MT, Demuth HB (2012) Neural network toolbox™ user’s guide. Citeseer, Boston

    Google Scholar 

  32. MATLAB (2018). https://www.mathworks.com/help/matlab/

  33. Kim MJ, Eun HC (2017) Identification of damage-expected members of truss structures using frequency response function. Adv Mech Eng 9(1):1–10. https://doi.org/10.1177/1687814016687911

    Article  MathSciNet  Google Scholar 

  34. Majumdar A, Maiti DK, Maity D (2012) Damage assessment of truss structures from changes in natural frequencies using ant colony optimization. Appl Math Comput 218(19):9759–9772. https://doi.org/10.1016/j.amc.2012.03.031

    Article  MATH  Google Scholar 

  35. Los Alamos National Laboratory Engineering Institute (2018) SHM Data Sets and Software. http://www.lanl.gov/projects/national-security-education-center/engineering/software/shm-data-sets-and-software.php

  36. Ha NV, Golinval JC (2010) Damage localization in linear-form structures based on sensitivity investigation for principal component analysis. J Sound Vib 329(21):4550–4566. https://doi.org/10.1016/j.jsv.2010.04.032

    Article  Google Scholar 

  37. Hemez FM, Doebling SW (1999) Test-analysis correlation and finite element model updating for nonlinear transient dynamics. Los Alamos National Laboratory, Massachusetts, USA, Rep. LA-UR-98-4573. https://www.osti.gov/biblio/759471

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Authors and Affiliations

  1. Department of Civil Engineering, Ahar Branch, Islamic Azad University, Ahar, Iran

    Parsa Ghannadi & Seyed Sina Kourehli

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  1. Parsa Ghannadi
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  2. Seyed Sina Kourehli
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Correspondence to Seyed Sina Kourehli.

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Ghannadi, P., Kourehli, S.S. Data-driven method of damage detection using sparse sensors installation by SEREPa. J Civil Struct Health Monit 9, 459–475 (2019). https://doi.org/10.1007/s13349-019-00345-8

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  • DOI: https://doi.org/10.1007/s13349-019-00345-8

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