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The Performance of a Modified Harmony Search Algorithm in the Structural Identification and Damage Detection of a Scaled Offshore Wind Turbine Laboratory Model

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EngOpt 2018 Proceedings of the 6th International Conference on Engineering Optimization (EngOpt 2018)

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Abstract

Offshore wind turbines are subjected to harsh environmental and operational conditions that affect their dynamic properties and cause damage. Visual checks are, for economic reasons, kept as low as possible, thus making the ability to detect damage via transmitted measurements a vital issue.

Identifying a structure is considered in essence an inverse problem which can be solved using model-updating techniques, which treat the identification problem as an optimization problem that are well-solved using meta-heuristic optimization schemes.

The objective of this study is to investigate the performance of the harmony search algorithm, both basic and modified, in identifying a scaled laboratory model of an offshore wind turbine supporting structure and detect the effects of damage and marine growth. The laboratory model is tested in a wave basin and is subjected to a variety of damage and marine growth levels.

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References

  1. International wind energy development: offshore report 2010. Technical report, BTM Consult ApS, Ringkøbing, Denmark (2010)

    Google Scholar 

  2. Bani-Hani, K., Ghaboussi, J., Schneider, S.P.: Experimental study of identification and control of structures using neural network. Part 1: identification. Earthq. Eng. Struct. Dyn. 28(9), 995–1018 (1999)

    Article  Google Scholar 

  3. Bayissa, W.L., Haritos, N.: Structural damage identification using a global optimization technique. Int. J. Struct. Stab. Dyn. 9(4), 745–763 (2009)

    Article  Google Scholar 

  4. Charalampakis, A.E., Dimou, C.K.: Identification of Bouc-Wen hysteretic systems using particle swarm optimization. Comput. Struct. 88(21–22), 1197–1205 (2010)

    Article  Google Scholar 

  5. Chou, J., Ghaboussi, J.: Genetic algorithm in structural damage detection. Comput. Struct. 79(14), 1335–1353 (2001)

    Article  Google Scholar 

  6. Cunha, J., Cogan, S., Berthod, C.: Application of genetic algorithms for the identification of elastic constants of composite materials from dynamic tests. Int. J. Numer. Methods Eng. 45(7), 891–900 (1999)

    Article  Google Scholar 

  7. Degertekin, S.O.: Harmony search algorithm for optimum design of steel frame structures: a comparative study with other optimization methods. Struct. Eng. Mech. 29, 391–410 (2008)

    Article  Google Scholar 

  8. Degertekin, S.O., Hayalioglu, M.S., Gorgun, H.: Optimum design of geometrically non-linear steel frames with semi-rigid connections using a harmony search algorithm. Steel Compos. Struct. 9, 535–555 (2009)

    Article  Google Scholar 

  9. Franco, G., Betti, R., Lu, H.: Identification of structural systems using an evolutionary strategy. J. Eng. Mech. 130(10), 1125–1139 (2004)

    Article  Google Scholar 

  10. Geem, Z.W., Kim, J.H., Loganathan, G.V.: A new heuristic optimization algorithm: harmony search. Simulation 76, 60–68 (2001)

    Article  Google Scholar 

  11. Hao, H., Xia, Y.: Vibration-based damage detection of structures by genetic algorithm. J. Comput. Civ. Eng. 16(3), 222–229 (2002)

    Article  MathSciNet  Google Scholar 

  12. Hasancebi, O., Erdal, F., Saka, M.: An adaptive harmony search method for structural optimization. J. Struct. Eng. 136, 419–431 (2010)

    Article  Google Scholar 

  13. Jahjouh, M.M.: Modified Adaptive Harmony Search Algorithm Approach on Structural Identification and Damage Detection. Ph.D. thesis, Leibniz Universität Hannover (2016)

    Google Scholar 

  14. Jahjouh, M.M., Nackenhorst,U.: Structural identification of two dimensional shear buildings using a modified adaptive harmony search algorithm. In: Engineering Optimization 2014, pp. 193–198. CRC Press, London (2014)

    Google Scholar 

  15. Jahjouh, M.M., Nackenhorst, U.: Damage detection of wind turbine supporting structures using an improved harmony search algorithm. Vibroeng. PROCEDIA 6, 87–92 (2015)

    Google Scholar 

  16. Jahjouh, M.M., Nackenhorst, U.: A modified harmony search approach on structural identification and damage detection of wind turbine supporting structures. J. Vibroeng. 17(1) (2016, in press)

    Google Scholar 

  17. Jahjouh, M.M., Nackenhorst, U.: A modified metaheuristic optimization approach on the structural identification and damage detection of an experimentally tested wind turbine supporting structure. In: Proceedings in Applied Mathematics and Mechanics PAMM (2016). Wiley-VCH, Braunschweig

    Google Scholar 

  18. Karimi, I., Khaji, N., Ahmadi, M.T., Mirzayee, M.: System identification of concrete gravity dams using artificial neural networks based on a hybrid finite element-boundary element approach. Eng. Struct. 32(11), 3583–3591 (2010)

    Article  Google Scholar 

  19. Koh, C., Perry, M.: Structural Identification and Damage Detection using Genetic Algorithms. Taylor and Francis Group, London (2010)

    Google Scholar 

  20. Koh, C.G., Chen, Y.F., Liaw, C.Y.: A hybrid computational strategy for identification of structural parameters. Comput. Struct. 81(2), 107–117 (2003)

    Article  Google Scholar 

  21. Koh, C.G., Hong, B., Liaw, C.Y.: Parameter identification of large structural systems in time domain. J. Struct. Eng. 126(8), 957–963 (2000)

    Article  Google Scholar 

  22. Koh, C.G., Trinh, T.N.: 19 An Evolutionary Divide-and- Conquer Strategy for Structural Identification, 1st edn. Elsevier Inc. (2013)

    Google Scholar 

  23. Lefik, M., Schrefler, B.A.: Artificial neural network for parameter identifications for an elasto-plastic model of superconducting cable under cyclic loading. Comput. Struct. 80(22), 1699–1713 (2002)

    Article  Google Scholar 

  24. Li, J., Liao, B., Huang, M.: Structural damage identification via modal data based on genetic algorithm. In: International Conference on Computational Intelligence and Software Engineering, pp. 1–4. IEEE, December 2010

    Google Scholar 

  25. Marano, G.C., Quaranta, G., Monti, G.: Modified genetic algorithm for the dynamic identification of structural systems using incomplete measurements. Comput. Aided Civ. Infrastruct. Eng. 26(2), 92–110 (2011)

    Article  Google Scholar 

  26. Masri, S.F., Nakamura, M., Chassiakos, A.G., Caughey, T.K.: Neural network approach to detection of changes in structural parameters. J. Eng. Mech. 122(4), 350–360 (1996)

    Article  Google Scholar 

  27. Masri, S.F., Smyth, A.W., Chassiakos, A.G., Caughey, T.K., Hunter, N.F.: Application of neural networks for detection of changes in nonlinear systems. J. Eng. Mech. 126(7), 666–676 (2000)

    Article  Google Scholar 

  28. Miguel, L.F.F., Miguel, L.F.F., Kaminski Jr., J., Riera, J.D.: Damage detection under ambient vibration by harmony search algorithm. Expert. Syst. Appl. 39(10), 9704–9714 (2012)

    Article  Google Scholar 

  29. Na, C., Kim, S., Kwak, H.: Structural damage evaluation using genetic algorithm. J. Sound Vib. 330(12), 2772–2783 (2011)

    Article  Google Scholar 

  30. Perry, M.J., Koh, C.G.: Output-only structural identification in time domain: numerical and experimental studies. Earthq. Eng. Struct. Dyn. 37(4), 517–533 (2008)

    Article  Google Scholar 

  31. Perry, M.J., Koh, C.G., Choo, Y.S.: Modified genetic algorithm strategy for structural identification. Comput. Struct. 84(8–9), 529–540 (2006)

    Article  Google Scholar 

  32. Raich, A.M., Liszkai, T.R.: Improving the performance of structural damage detection methods using advanced genetic algorithms. J. Struct. Eng. 133(3), 449–461 (2007)

    Article  Google Scholar 

  33. Rao, A.R.M., Lakshmi, K., Ganesan, K.: Structural system identification using quantum behaved particle swarm optimisation algorithm. Struct. Durab. Health Monit. 9(2), 99–128 (2013)

    Google Scholar 

  34. Sohn, H., Farrar, C.R., Hemez, F.M., Shunk, D.D., Stinemates, D.W., Nadler, B.R., Czarnecki, J.J.: A Review of Structural Health Monitoring Literature: 1996–2001. Technical report, Los Alamos National Laboratory, Los Alamos, USA (2004)

    Google Scholar 

  35. Spraul, C., Pham, H.-D., Arnal, V., Reynaud, M.: Effect of marine growth on floating wind turbines mooring lines responses. In: 23ème Congrès Français de Mécanique, pp. 1–17 (2017)

    Google Scholar 

  36. Sun, H., Betti, R.: Simultaneous identification of structural parameters and dynamic input with incomplete output-only measurements. Struct. Control. Health Monit. 21(6), 868–889 (2014)

    Article  Google Scholar 

  37. Sun, H., Lu, H., Betti, R.: Identification of structural models using a modified Artificial Bee Colony algorithm. Comput. Struct. 116, 59–74 (2013)

    Article  Google Scholar 

  38. Tang, H., Xue, S., Fan, C.: Differential evolution strategy for structural system identification. Comput. Struct. 86(21–22), 2004–2012 (2008)

    Article  Google Scholar 

  39. Wang, G.S.: Application of hybrid genetic algorithm to system identification. Struct. Control. Health Monit. 16(2), 125–153 (2009)

    Article  Google Scholar 

  40. Wang, X.M., Koh, C.G., Zhang, J.: Substructural identification of jack-up platform in time and frequency domains. Appl. Ocean. Res. 44, 53–62 (2014)

    Article  Google Scholar 

  41. Xu, B., Wu, Z., Chen, G., Yokoyama, K.: Direct identification of structural parameters from dynamic responses with neural networks. Eng. Appl. Artif. Intell. 17(8), 931–943 (2004)

    Article  Google Scholar 

  42. Yang, X.S.: Nature-Inspired Metaheuristic Algorithms, 1st edn. Luniver Press, UK (2008)

    Google Scholar 

  43. Yun, C., Bahng, E.Y.: Substructural identification using neural networks. Comput. Struct. 77(1), 41–52 (2000)

    Article  Google Scholar 

  44. Zhang, J., Koh, C.G., Trinh, T.N., Wang, X., Zhang, Z.: Identification of jack-up spudcan fixity by an output-only substructural strategy. Mar. Struct. 29(1), 71–88 (2012)

    Article  Google Scholar 

  45. Zhang, Z., Koh, C.G., Duan, W.H.: Uniformly sampled genetic algorithm with gradient search for structural identification - Part II: local search. Comput. Struct. 88(19–20), 1149–1161 (2010)

    Article  Google Scholar 

  46. Zhang, Z., Koh, C.G., Perry, M.J.: Frequency domain substructural identification for arbitrary excitations. Earthq. Eng. Struct. Dyn. 41(4), 605–621 (2012)

    Article  Google Scholar 

  47. Zhao, J., Ivan, J.N., DeWolf, J.T.: Structural damage detection using artificial neural networks. J. Infrastruct. Syst. 4(3), 93–101 (1998)

    Article  Google Scholar 

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Acknowledgments

The support of the research project “Condition evaluation and prediction for offshore wind turbines based on measurement data” (Contract No 0325575C) by the German “Federal Ministry for the Environment, Nature Conversation and Nuclear Safety” is gratefully acknowledged.

Author information

Authors and Affiliations

  1. Institute of Structural Analysis, Leibniz Universität Hannover, Appelstr. 9A, 30167, Hannover, Germany

    Mahmoud Jahjouh & Raimund Rolfes

Authors
  1. Mahmoud Jahjouh
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  2. Raimund Rolfes
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Corresponding author

Correspondence to Mahmoud Jahjouh .

Editor information

Editors and Affiliations

  1. Mechanical Engineering Department, Instituto Superior Técnico, University of Lisbon, Lisbon, Portugal

    H.C. Rodrigues

  2. Federal University of Rio de Janeiro, Rio de Janeiro, Brazil

    J. Herskovits

  3. Instituto Superior Tecnico, University of Lisbon, Lisbon, Portugal

    C.M. Mota Soares

  4. Instituto Superior Tecnico, University of Lisbon, Lisbon, Portugal

    A.L. Araújo

  5. Instituto Superior Tecnico, University of Lisbon, Lisbon, Portugal

    J.M. Guedes

  6. Instituto Superior Tecnico, University of Lisbon, Lisbon, Portugal

    J.O. Folgado

  7. Instituto Superior Tecnico, University of Lisbon, Lisbon, Portugal

    F. Moleiro

  8. IST, IDMEC – Instituto de Engenharia Mecânica, Lisbon, Portugal

    J. F. A. Madeira

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Jahjouh, M., Rolfes, R. (2019). The Performance of a Modified Harmony Search Algorithm in the Structural Identification and Damage Detection of a Scaled Offshore Wind Turbine Laboratory Model. In: Rodrigues, H., et al. EngOpt 2018 Proceedings of the 6th International Conference on Engineering Optimization. EngOpt 2018. Springer, Cham. https://doi.org/10.1007/978-3-319-97773-7_18

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  • DOI: https://doi.org/10.1007/978-3-319-97773-7_18

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-319-97773-7

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