Advertisement

Damage Detection of Truss Employing Swarm-Based Optimization Techniques: A Comparison

  • Swarup K. BarmanEmail author
  • Dipak K. Maiti
  • Damodar Maity
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 949)

Abstract

Swarm-based optimization techniques are very popular and well known in the field of damage detection of structures. Present paper evaluates the performance of three different variants of particle swarm optimization (PSO) and continuous ant colony optimization (ACOr) to detect damages in plane and space truss structure based on frequency and mode shapes-based objective function. The algorithms considered for the comparison are: unified particle swarm optimization (UPSO), ageing leader challenger particle swarm optimization (ALC-PSO), enhanced PSO with intelligent particle number (IPN-PSO) and continuous ant colony optimization (ACOr). A 25 member plane truss and a 25 member space truss are considered for the comparison among the algorithms. The numerical study reveals the superiority of UPSO over other algorithms in terms of minimum computational effort and success rate.

Keywords

PSO ALC-PSO UPSO ACOr Frequency Modeshapes 

Notes

Acknowledgements

This research work is financially supported by ISRO (Indian Space Research Organisation) IIT Kharagpur cell. The authors are grateful to ISRO cell for their financial support to carry out the research work at Department of Aerospace Engineering, IIT, Kharagpur.

References

  1. 1.
    Adams, R.D., Cawley, P., Pie, C.J., Stone, B.J.: A vibration technique for non-destructively assessing the integrity of structures. J. Mech. Eng. Sci. 20, 93–100 (1978).  https://doi.org/10.1243/JMES_JOUR_1978_020_016_02CrossRefGoogle Scholar
  2. 2.
    Cawley, P., Adams, R.D.: A vibration technique for non-destructive testing of fibre composite structures. J. Compos. Mater. 13, 161–175 (1979).  https://doi.org/10.1177/002199837901300207CrossRefGoogle Scholar
  3. 3.
    Messina, A., Williams, E.J., Contursi, T.: Structural damage detection by a sensitivity and statistical-based method. J. Sound Vib. 216, 791–808 (1998).  https://doi.org/10.1006/jsvi.1998.1728CrossRefGoogle Scholar
  4. 4.
    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).  https://doi.org/10.1016/j.amc.2012.03.031CrossRefzbMATHGoogle Scholar
  5. 5.
    Mohan, S.C., Maiti, D.K., Maity, D.: Structural damage assessment using FRF employing particle swarm optimization. Appl. Math. Comput. 219, 10387–10400 (2013).  https://doi.org/10.1016/j.amc.2013.04.016MathSciNetCrossRefzbMATHGoogle Scholar
  6. 6.
    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).  https://doi.org/10.1016/j.advengsoft.2014.09.010CrossRefGoogle Scholar
  7. 7.
    Kang, F., Li, J., Xu, Q.: Damage detection based on improved particle swarm optimization using vibration data. Appl. Soft Comput. 12, 2329–2335 (2012).  https://doi.org/10.1016/j.asoc.2012.03.050CrossRefGoogle Scholar
  8. 8.
    Nanda, B., Maity, D., Maiti, D.K.: Modal parameter based inverse approach for structural joint damage assessment using unified particle swarm optimization. Appl. Math. Comput. 242, 407–422 (2014).  https://doi.org/10.1016/j.amc.2014.05.115MathSciNetCrossRefzbMATHGoogle Scholar
  9. 9.
    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).  https://doi.org/10.1016/j.swevo.2015.10.010CrossRefGoogle Scholar
  10. 10.
    Nhamage, I.A., Lopez, R.H., Miguel, L.F.F.: An improved hybrid optimization algorithm for vibration based-damage detection. Adv. Eng. Softw. 93, 47–64 (2016).  https://doi.org/10.1016/j.advengsoft.2015.12.003CrossRefGoogle Scholar
  11. 11.
    Seyedpoor, S.M.: A two stage method for structural damage detection using a modal strain energy based index and particle swarm optimization. Int. J. Non-Linear Mech. 47, 1–8 (2012).  https://doi.org/10.1016/j.ijnonlinmec.2011.07.011CrossRefGoogle Scholar
  12. 12.
    Maity, D., Tripathy, R.R.: Damage assessment of structures from changes in natural frequencies using genetic algorithm. Struct. Eng. Mech. 19, 21–42 (2005).  https://doi.org/10.12989/sem.2005.19.1.021CrossRefGoogle Scholar
  13. 13.
    Zapico, J.L., González, M.P., Friswell, M.I., Taylor, C.A., Crewe, A.J.: Finite element model updating of a small scale bridge. J. Sound Vib. 268, 993–1012 (2003).  https://doi.org/10.1016/S0022-460X(03)00409-7CrossRefGoogle Scholar
  14. 14.
    Beena, P., Ganguli, R.: Structural damage detection using fuzzy cognitive maps and Hebbian learning. Appl. Soft Comput. 11, 1014–1020 (2011).  https://doi.org/10.1016/j.asoc.2010.01.023CrossRefGoogle Scholar
  15. 15.
    Chandrupatla, T.R., Belegundu, A.D.: Introduction to Finite Elements in Engineering. Prentice Hall, Upper Saddle River, New Jersey, USA (2002)zbMATHGoogle Scholar
  16. 16.
    Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks, Piscataway, NJ, pp. 1942–1948 (1995)Google Scholar
  17. 17.
    Parsopoulos, K.E., Vrahatis, M.N.: Unified particle swarm optimization for solving constrained engineering optimization problems. In: Proceedings of the First International Conference on Advances in Natural Computation, vol. Part III, pp. 582–591. Springer, Berlin, Heidelberg (2005)Google Scholar
  18. 18.
    Chen, W.N., Zhang, J., Lin, Y., Chen, N., Zhan, Z.H., Chung, H.S.H., Li, Y., Shi, Y.H.: Particle swarm optimization with an aging leader and challengers. IEEE Trans. Evol. Comput. 17, 241–258 (2013).  https://doi.org/10.1109/TEVC.2011.2173577CrossRefGoogle Scholar
  19. 19.
    Lee, J.H., Song, J.-Y., Kim, D.-W., Kim, J.-W., Kim, Y.-J., Jung, S.-Y.: Particle swarm optimization algorithm with intelligent particle number control for optimal design of electric machines. IEEE Trans. Ind. Electron. 65, 1791–1798 (2017).  https://doi.org/10.1109/TIE.2017.2760838CrossRefGoogle Scholar
  20. 20.
    Socha, K., Dorigo, M.: Ant colony optimization for continuous domains. Eur. J. Oper. Res. 185, 1155–1173 (2008).  https://doi.org/10.1016/j.ejor.2006.06.046MathSciNetCrossRefzbMATHGoogle Scholar
  21. 21.
    Esfandiari, A., Bakhtiari-Nejad, F., Rahai, A.: Theoretical and experimental structural damage diagnosis method using natural frequencies through an improved sensitivity equation. Int. J. Mech. Sci. 70, 79–89 (2013).  https://doi.org/10.1016/j.ijmecsci.2013.02.006CrossRefGoogle Scholar
  22. 22.
    Barman, S.K., Maiti, D.K., Maity, D.: A new hybrid unified particle swarm optimization technique for damage assessment from changes of vibration responses. In: Proceedings of ICTACEM 2017 International Conference on Theoretical, Applied, Computational and Experimental Mechanics, pp 1–12. IIT Kharagpur, Kharagpur, India (2017)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Swarup K. Barman
    • 1
    Email author
  • Dipak K. Maiti
    • 1
  • Damodar Maity
    • 2
  1. 1.Department of Aerospace EngineeringIIT KharagpurKharagpurIndia
  2. 2.Department of Civil EngineeringIIT KharagpurKharagpurIndia

Personalised recommendations