Smart Structures and Systems

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Structural damage identification for elements and connections using an improved genetic algorithm

  • Ramezani, Meysam (International Institute of Earthquake Engineering and Seismology (IIEES)) ;
  • Bahar, Omid (International Institute of Earthquake Engineering and Seismology (IIEES))
  • Received : 2020.04.16
  • Accepted : 2021.08.15
  • Published : 2021.11.25
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Abstract

All structures are exposed to damage during their lifetime. Timely detection of the damages can prevent the reduction of stiffness/resistance of structures. The large number of elements in structures in comparison to the number of measurable data can limit the performance of the closed-form methods. This study presents a new damage detection method determining damage severity and location in the elements and connections via Improved Genetic Algorithm (IGA) based on limited number of mode shapes. This study describes how damage can be accurately identified based on the least number of modes. In this approach, healthy elements are identified by the IGA algorithm and removed from the search space. In this way, the damaged elements are examined more carefully and the severity of the damage is estimated more accurately. In this study, to evaluate the performance of the proposed method, two numerical examples are used. The numerical study includes a 2D truss structure under 4 damage scenarios and a 3D structure with a much larger number of elements under 6 different damage scenarios. Moreover, the performance of this algorithm in presence of noise in modal information is also examined. The results show that the proposed method can accurately detect damage to elements and connections, even in the presence of noise, by using only one mode in the 2D truss and two modes in the 3D structure. In order to evaluate the efficiency of this method in determining the damage of connections, a cantilever beam has been modeled and experimentally tested. The connection stiffness of the beam has been computed using both IGA and load-deformation measurement methods. In the IGA method only the first mode shape of the beam is employed to determine the connection stiffness. To derive the mode shapes in rotational degrees of freedoms which contain valuable information on connection stiffness, a novel, straightforward, and practical approach has been proposed. The results also indicate the high performance of this method to accurately estimate the connection stiffness.

Keywords

Acknowledgement

The present study is a part of the first author's PhD thesis at the International Institute of Earthquake Engineering and Seismology, whose support is gratefully appreciated.

References

  1. Alten, K., Ralbovsky, M., Vorwagner, A., Toplitzer, H. and Wittmann, S. (2017), "Evaluation of different monitoring techniques during damage infliction on structures", Procedia Eng., 199, 1840-1845. https://doi.org/10.1016/j.proeng.2017.09.106
  2. Alvandi, A. and Cremona, C. (2006), "Assessment of vibration-based damage identification techniques", J. Sound Vib., 292(1-2), 179-202. https://doi.org/10.1016/j.jsv.2005.07.036
  3. Daei, M., Sokhangou, F. and Hejazi, M. (2017), "A new intelligent algorithm for damage detection in frames via modal properties", Intell. Build. Int., 9(4), 222-236. https://doi.org/10.1080/17508975.2016.1161584
  4. Datta, D. and Dutta, A. (2020), "Structural health monitoring using improved subspace identification method by including rotational degrees of freedom", In: Advances in Rotor Dynamics, Control, and Structural Health Monitoring, Springer, pp. 215-226. https://doi.org/10.1007/978-981-15-5693-7_16
  5. Ding, Z., Li, J. and Hao, H. (2019), "Structural damage identification using improved Jaya algorithm based on sparse regularization and Bayesian inference", Mech. Syst. Signal Process., 132, 211-231. https://doi.org/10.1016/j.ymssp.2019.06.029
  6. Doebling, S.W., Farrar, C.R. and Prime, M.B. (1998), "A summary review of vibration-based damage identification methods", Shock Vib. Digest, 30(2), 91-105. https://doi.org/10.1177/058310249803000201
  7. Ghiasia, R. and Ghasemi, M.R. (2018), "Optimization-based method for structural damage detection with consideration of uncertainties-a comparative study", Smart Struct. Syst., Int. J., 22(5), 561-574. https://doi.org/10.12989/sss.2018.22.5.561
  8. Gomes, G.F., Da Cunha, S.S. and Ancelotti, A.C. (2019), "A sunflower optimization (SFO) algorithm applied to damage identification on laminated composite plates", Eng. Comput., 35(2), 619-626. https://doi.org/10.1007/s00366-018-0620-8
  9. Gomes, H. and Silva, N. (2008), "Some comparisons for damage detection on structures using genetic algorithms and modal sensitivity method", Appl. Mathe. Modell., 32(11), 2216-2232. https://doi.org/10.1016/j.apm.2007.07.002
  10. Hester, D., Brownjohn, J., Huseynov, F., Obrien, E., Gonzalez, A. and Casero, M. (2020), "Identifying damage in a bridge by analysing rotation response to a moving load", Struct. Infrastruct. Eng., 16(7), 1050-1065. https://doi.org/10.1080/15732479.2019.1680710
  11. Hormozabad, S.J. and Soto, M.G. (2021), "Real-time damage identification of discrete structures via neural networks subjected to dynamic loading", Health Monitoring of Structural and Biological Systems XV, International Society for Optics and Photonics, 115932O. https://doi.org/10.1117/12.2582482
  12. Huseynov, F., Kim, C., Obrien, E., Brownjohn, J., Hester, D. and Chang, K. (2020), "Bridge damage detection using rotation measurements-Experimental validation", Mech. Syst. Signal Process., 135, 106380. https://doi.org/10.1016/j.ymssp.2019.106380
  13. Johnson, E.A., Lam, H.-F., Katafygiotis, L.S. and Beck, J.L. (2004), "Phase I IASC-ASCE structural health monitoring benchmark problem using simulated data", J. Eng. Mech., 130(1), 3-15. https://doi.org/10.1061/(ASCE)0733-9399(2004)130:1(3)
  14. Kaveh, A., Hoseini Vaez, S. and Hosseini, P. (2019), "Enhanced vibrating particles system algorithm for damage identification of truss structures", Scientia Iranica, 26(1), 246-256. https://doi.org/10.24200/SCI.2017.4265
  15. Li, J., Wu, B., Zeng, Q. and Lim, C.W. (2010), "A generalized flexibility matrix based approach for structural damage detection", J. Sound Vib., 329(22), 4583-4587. https://doi.org/10.1016/j.jsv.2010.05.024
  16. Liang, Y., Feng, Q., Li, H. and Jiang, J. (2019), "Damage detection of shear buildings using frequency-change-ratio and model updating algorithm", Smart Struct. Syst., Int. J., 23(2), 107-122. https://doi.org/10.12989/sss.2019.23.2.107
  17. Meng, F., Yu, J., Alaluf, D., Mokrani, B. and Preumont, A. (2019), "Modal flexibility based damage detection for suspension bridge hangers: A numerical and experimental investigation", Smart Struct. Syst., Int. J., 23(1), 15-29. https://doi.org/10.12989/sss.2019.23.1.015
  18. Monforton, G.R. (1962), Matrix analysis of frames with semi-rigid connections.
  19. Moradipour, P., Chan, T.H. and Gallage, C. (2015), "An improved modal strain energy method for structural damage detection, 2D simulation", Struct. Eng. Mech., Int. J., 54(1), 105-119. https://doi.org/10.12989/sem.2015.54.1.105
  20. Nobahari, M. and Seyedpoor, S. (2011), "Structural damage detection using an efficient correlation-based index and a modified genetic algorithm", Mathe. Comput. Modell., 53(9-10), 1798-1809. https://doi.org/10.1016/j.mcm.2010.12.058
  21. Pal, J., Banerjee, S., Chikermane, S. and Banerji, P. (2017), "Estimation of fixity factors of bolted joints in a steel frame structure using a vibration-based health monitoring technique", Int. J. Steel Struct., 17(2), 593-607. https://doi.org/10.1007/s13296-017-6018-4
  22. Pandey, A. and Biswas, M. (1994), "Damage detection in structures using changes in flexibility", J. Sound Vib., 169(1), 3-17. https://doi.org/10.1006/jsvi.1994.1002
  23. Pedram, M., Esfandiari, A. and Khedmati, M.R. (2018), "Frequency domain damage detection of plate and shell structures by finite element model updating", Inverse Problems Sci. Eng., 26(1), 100-132. https://doi.org/10.1080/17415977.2017.1309398
  24. Quaranta, G., Carboni, B. and Lacarbonara, W. (2016), "Damage detection by modal curvatures: numerical issues", J. Vib. Control, 22(7), 1913-1927. https://doi.org/10.1177/1077546314545528
  25. Ramezani, M. and Bahar, O. (2021), "Indirect structure damage identification with the information of the vertical and rotational mode shapes", Scientia Iranica. https://doi.org/10.24200/SCI.2021.56842.4939
  26. Ramezani, M., Bathaei, A. and Zahrai, S.M. (2017), "Designing fuzzy systems for optimal parameters of TMDs to reduce seismic response of tall buildings", Smart Struct. Syst., Int. J., 20(1), 61-74. https://doi.org/10.12989/sss.2017.19.3.269
  27. Seyedpoor, S.M., Norouzi, E. and Ghasemi, S. (2018), "Structural damage detection using a multi-stage improved differential evolution algorithm (Numerical and experimental)", Smart Struct. Syst., Int. J., 21(2), 235-248. https://doi.org/10.12989/sss.2018.21.2.235
  28. Shahri, A.H. and Ghorbani-Tanha, A. (2017), "Damage detection via closed-form sensitivity matrix of modal kinetic energy change ratio", J. Sound Vib., 401, 268-281. https://doi.org/10.1016/j.jsv.2017.04.039
  29. Shi, Z., Law, S. and Zhang, L. (1998), "Structural damage localization from modal strain energy change", J. Sound Vib., 218(5), 825-844. https://doi.org/10.1006/jsvi.1998.1878
  30. Shi, Z., Law, S. and Zhang, L.M. (2000), "Structural damage detection from modal strain energy change", J. Eng. Mech., 126(12), 1216-1223. https://doi.org/10.1061/(ASCE)0733-9399(2000)126:12(1216)
  31. Silva, M., Santos, A., Figueiredo, E., Santos, R., Sales, C. and Costa, J.C. (2016), "A novel unsupervised approach based on a genetic algorithm for structural damage detection in bridges", Eng. Applicat. Artif. Intell., 52, 168-180. https://doi.org/10.1016/j.engappai.2016.03.002
  32. Srinivas, V., Ramanjaneyulu, K. and Jeyasehar, C.A. (2011), "Multi-stage approach for structural damage identification using modal strain energy and evolutionary optimization techniques", Struct. Health Monitor., 10(2), 219-230. https://doi.org/10.1177/1475921710373291
  33. Tiachacht, S., Bouazzouni, A., Khatir, S., Wahab, M.A., Behtani, A. and Capozucca, R. (2018), "Damage assessment in structures using combination of a modified Cornwell indicator and genetic algorithm", Eng. Struct., 177, 421-430. https://doi.org/10.1016/j.engstruct.2018.09.070
  34. Whalen, T.M. (2008), "The behavior of higher order mode shape derivatives in damaged, beam-like structures", J. Sound Vib., 309(3-5), 426-464. https://doi.org/10.1016/j.jsv.2007.07.054
  35. Yan, W.-J. and Ren, W.-X. (2014), "Closed-form modal flexibility sensitivity and its application to structural damage detection without modal truncation error", J. Vib. Control, 20(12), 1816-1830. https://doi.org/10.1177/1077546313476724
  36. Yan, W.-J., Huang, T.-L. and Ren, W.-X. (2010), "Damage detection method based on element modal strain energy sensitivity", Adv. Struct. Eng., 13(6), 1075-1088. https://doi.org/10.1260/1369-4332.13.6.1075
  37. Yang, Q. and Liu, J. (2009), "Damage identification by the eigenparameter decomposition of structural flexibility change", Int. J. Numer. Methods Eng., 78(4), 444-459. https://doi.org/10.1002/nme.2494
  38. Zheng, T., Luo, W., Hou, R., Lu, Z. and Cui, J. (2020), "A novel experience-based learning algorithm for structural damage identification: simulation and experimental verification", Eng. Optimiz., 52(10), 1658-1681. https://doi.org/10.1080/0305215X.2019.1668935