Skip to main content

Adaptive Sine Cosine Algorithm Integrated with Differential Evolution for Structural Damage Detection

  • Conference paper
  • First Online:
Computational Science and Its Applications – ICCSA 2017 (ICCSA 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10404))

Included in the following conference series:

Abstract

A sine cosine algorithm is one promising meta-heuristic recently proposed. In this work, the algorithm is extended to be self-adaptive and its main reproduction operators are integrated with the mutation operator of differential evolution. The new algorithm is called adaptive sine cosine algorithm integrated with differential evolution (ASCA-DE) and used to tackle the test problems for structural damage detection. The results reveal that the new algorithm outperforms a number of established meta-heuristics.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Sinou, J.J.: A review of damage detection and health monitoring of mechanical systems from changes in the measurement of linear and non-linear vibrations. In: Sapri, R.C. (ed.) Mechanical Vibrations: Measurement, Effects and Control, pp. 643–702. Nova Science Publishers, Inc., Hauppauge (2009)

    Google Scholar 

  2. Chen, H., Shi, X., He, Q., Mao, J.H., Liu, Y., Kang, H., Shen, J.: A multiresolution investigation on fatigue damage of aluminum alloys at micrometer level, Int. J. Damage. Mech. 26 (2017). doi:10.1177/1056789517693411

  3. Shen, J., Mao, J., Boileau, J., Chow, C.L.: Material damage estimated via linking micro/macroscale defects to macroscopic mechanical properties. Int. J. Damage Mech 23, 537–566 (2014)

    Article  Google Scholar 

  4. Wang, X., Hu, N., Fukunaga, H., Yao, Z.: Structural damage identification using static test data and changes in frequencies. Eng. Struct. 23, 610–621 (2001). doi:10.1016/S0141-0296(00)00086-9

    Article  Google Scholar 

  5. Gerist, S., Maheri, M.R.: Multi-stage approach for structural damage detection problem using basis pursuit and particle swarm optimization. J. Sound Vib. 384, 210–226 (2016). doi:10.1016/j.jsv.2016.08.024

    Article  Google Scholar 

  6. Koh, B.H., Dyke, S.J.: Structural health monitoring for flexible bridge structures using correlation and sensitivity of modal data. Comput. Struct. 85, 117–130 (2007). doi:10.1016/j.compstruc.2006.09.005

    Article  Google Scholar 

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

    Article  Google Scholar 

  8. Laier, J.E., Villalba, J.D.: Ensuring reliable damage detection based on the computation of the optimal quantity of required modal data. Comput. Struct. 147, 117–125 (2015). doi:10.1016/j.compstruc.2014.09.020

    Article  Google Scholar 

  9. Chou, J.H., Ghaboussi, J.: Genetic algorithm in structural damage detection. Comput. Struct. 79, 1335–1353 (2001). doi:10.1016/S0045-7949(01)00027-X

    Article  Google Scholar 

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

    MATH  Google Scholar 

  11. Tabrizian, Z., Amiri G.G., Beigy, M.H.A.: Charged system search algorithm utilized for structural damage detection. Shock Vib. 2014, Article ID 194753, 13 p. (2014). doi:10.1155/2014/194753

  12. Xu, H., Ding, Z., Lu, Z., Liu, J.: Structural damage detection based on Chaotic Artificial Bee Colony algorithm. Struct. Eng. Mech. 55(6), 1223–1239 (2015). doi:10.12989/sem.2015.55.6.1223

    Article  Google Scholar 

  13. Ding, Z.H., Huang, M., Lu, Z.R.: Structural damage detection using artificial bee colony algorithm with hybrid search strategy. Swarm Evol. Comput. 28, 1–13 (2016). doi:10.1016/j.swevo.2015.10.010

    Article  Google Scholar 

  14. Pholdee, N., Bureerat, S.: Structural health monitoring through meta-heuristics – comparative performance study. Adv. Comput. Des. 1, 315–327 (2016). doi:10.12989/acd.2016.1.4.315

    Google Scholar 

  15. Pal, J., Banerjee, S.: A combined modal strain energy and particle swarm optimization for health monitoring of structures. J. Civil Struct. Health Monit. 5, 353–363 (2015). doi:10.1007/s13349-015-0106-y

    Article  Google Scholar 

  16. Casciati, S.: Stiffness identification and damage localization via differential evolution algorithms. Struct. Control Health Monit. 15, 436–449 (2008). doi:10.1002/stc.236

    Article  Google Scholar 

  17. Agarwalla, D.K., Dash, A.K., Bhuyan, S.K., Nayak, P.S.K.: Damage detection of fixed-fixed beam: a fuzzy neuro hybrid system based approach. In: Panigrahi, B.K., Suganthan, P.N., Das, S. (eds.) SEMCCO 2014. LNCS, vol. 8947, pp. 363–372. Springer, Cham (2015). doi:10.1007/978-3-319-20294-5_32

    Chapter  Google Scholar 

  18. Jiao, Y.B., Liu, H.B., Cheng, Y.C., Gong, Y.F.: Damage identification of bridge based on chebyshev polynomial fitting and fuzzy logic without considering baseline model parameters. Shock Vib. 2015, Article ID 187956, 10 p. (2015). doi:10.1155/2015/187956

  19. Pan, D.-G., Lei, S.-S., Wu, S.-C.: Two-stage damage detection method using the artificial neural networks and genetic algorithms. In: Zhu, R., Zhang, Y., Liu, B., Liu, C. (eds.) ICICA 2010. LNCS, vol. 6377, pp. 325–332. Springer, Heidelberg (2010). doi:10.1007/978-3-642-16167-4_42

    Chapter  Google Scholar 

  20. Abdeljaber, O., Avci, O.: Nonparametric structural damage detection algorithm for ambient vibration response: utilizing artificial neural networks and self-organizing maps. J. Archit. Eng. 22, 04016004 (2016). doi:10.1061/(ASCE)AE.1943-5568.0000205

    Article  Google Scholar 

  21. Sidibe, Y., Druaux, F., Lefebvre, D., Maze, G., Léon, F.: Signal processing and Gaussian neural networks for the edge and damage detection in immersed metal plate-like structures. Artif. Intell. Rev. 46, 289–305 (2016). doi:10.1007/s10462-016-9464-z

    Article  Google Scholar 

  22. Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Adv. Eng. Softw. 69, 46–61 (2014). doi:10.1016/j.advengsoft.2013.12.007

    Article  Google Scholar 

  23. Mirjalili, S.: Moth-flame optimization algorithm: a novel nature-inspired heuristic paradigm. Knowl.-Based Syst. 89, 228–249 (2015). doi:10.1016/j.knosys.2015.07.006

    Article  Google Scholar 

  24. Mirjalili, S., Lewis, A.: The whale optimization algorithm. Adv. Eng. Softw. 95, 51–67 (2016). doi:10.1016/j.advengsoft.2016.01.008

    Article  Google Scholar 

  25. Mirjalili, S.: SCA: a sine cosine algorithm for solving optimization problems. Knowl.-Based Syst. 96, 120–133 (2016). doi:10.1016/j.knosys.2015.12.022

    Article  Google Scholar 

  26. Bureerat, S., Pholdee, N.: Optimal truss sizing using an adaptive differential evolution algorithm. J. Comput. Civil Eng. 30, 04015019 (2015). doi:10.1061/(ASCE)CP.1943-5487.0000487

    Article  MATH  Google Scholar 

  27. Zhang, J., Sanderson, A.C.: JADE: adaptive differential evolution with optional external archive. IEEE T. Evolut. Comput. 13, 945–958 (2009). doi:10.1109/TEVC.2009.2014613

    Article  Google Scholar 

  28. Storn, R., Price, K.: Differential evolution – a simple and efficient heuristic for global optimization over continuous spaces. J. Global Optim. 11, 341–359 (1997). doi:10.1023/A:1008202821328

    Article  MathSciNet  MATH  Google Scholar 

  29. Karaboga, D., Basturk, B.: A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J. Global Optim. 39, 459–471 (2007). doi:10.1007/s10898-007-9149-x

    Article  MathSciNet  MATH  Google Scholar 

  30. Socha, K., Dorigo, M.: Ant colony optimization for continuous domains. Eur. J. Oper. Res. 185, 1155–1173 (2008). doi:10.1016/j.ejor.2006.06.046

    Article  MathSciNet  MATH  Google Scholar 

  31. Kaveh, A., Talatahari, S.: A novel heuristic optimization method: charged system search. Acta Mech. 213, 267–289 (2010). doi:10.1007/s00707-009-0270-4

    Article  MATH  Google Scholar 

  32. Husseinzadeh, K.A.: An efficient algorithm for constrained global optimization and application to mechanical engineering design: League championship algorithm (LCA). Comput. Aided Design. 43, 1769–1792 (2011). doi:10.1016/j.cad.2011.07.003

    Article  Google Scholar 

  33. Bureerat, S., Limtragool, J.: Structural topology optimization using simulated annealing with multiresolution design variables. Finite Elem. Anal. Des. 44, 738–747 (2008). doi:10.1016/j.finel.2008.04.002

    Article  Google Scholar 

  34. Venter, G., Sobieszczanski-Sobieski, J.: Particle swarm optimization. AIAA J. 41, 1583–1589 (2003). doi:10.2514/2.2111

    Article  Google Scholar 

  35. Back, T.: Evolutionary Algorithms in Theory and Practice. Oxford University Press, Oxford (1996)

    MATH  Google Scholar 

  36. Rao, R.V., Savsani, V.J., Vakharia, D.P.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Comput. Aided Des. 43, 303–315 (2011). doi:10.1016/j.cad.2010.12.015

    Article  Google Scholar 

  37. Hansen, N., Muller, S.D., Koumoutsakos, P.: Reducing the time complexity of the derandomized evolution strategy with covariance matrix adaptation (CMA-ES). Evol. Comput. 11, 1–18 (2003). doi:10.1162/106365603321828970

    Article  Google Scholar 

Download references

Acknowledgments

The authors are grateful for the support from the Thailand Research Fund (TRF).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Nantiwat Pholdee .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Bureerat, S., Pholdee, N. (2017). Adaptive Sine Cosine Algorithm Integrated with Differential Evolution for Structural Damage Detection. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2017. ICCSA 2017. Lecture Notes in Computer Science(), vol 10404. Springer, Cham. https://doi.org/10.1007/978-3-319-62392-4_6

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-62392-4_6

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-62391-7

  • Online ISBN: 978-3-319-62392-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics