Main Article Content


 In this study, parametric identification of structural properties such as stiffness and damping is carried out using acceleration responses in the time domain. The process consists of minimizing the difference between the experimentally measured and theoretically predicted acceleration responses. The unknown parameters of certain numerical models, viz., a ten degree of freedom lumped mass system, a nine member truss and a non-uniform simply supported beam are thus identified. Evolutionary and behaviorally inspired optimization algorithms are used for minimization operations. The performance of their hybrid combinations is also investigated. Genetic Algorithm (GA) is a well known evolutionary algorithm used in system identification. Recently Particle Swarm Optimization (PSO), a behaviorally inspired algorithm, has emerged as a strong contender to GA in speed and accuracy. The discrete Ant Colony Optimization (ACO) method is yet another behaviorally inspired method studied here. The performance (speed and accuracy) of each algorithm alone and in their hybrid combinations such as GA with PSO, ACO with PSO and ACO with GA are extensively investigated using the numerical examples with effects of noise added for realism. The GA+PSO hybrid algorithm was found to give the best performance in speed and accuracy compared to all others. The next best in performance was pure PSO followed by pure GA. ACO performed poorly in all the cases.



Inverse problem System identification Genetic algorithm Ant colony optimization Particle swarm optimization

Article Details

How to Cite
Sandesh, S., Kumar Sahu, A., & Shankar, K. (2009). Structural System Identification in the Time Domain using Evolutionary and Behaviorally Inspired Algorithms and their Hybrids. The Journal of Engineering Research [TJER], 6(2), 64–77.


  1. Abbospur, K.C., Schulin, R. and van Ganuethen, M., 2004, "Estimating Unsaturated Hydraulic Parameters using Ant Colony Optimization," Advances in water resources, Vol. 24, pp. 827-841.
  2. Binghui, Yu., Xiaohui Yuan and Jinwen Wang, 2007, "Short-Term Hydro-Thermal Scheduling using Particle Swarm Optimization Method," Energy Conversion and Management, Vol. 48, pp. 1902- 1908.
  3. Bao Zhang, Hong-Fei Teng and Yan-Jun Shi, 2008, "Layout Optimization of Satellite Module using Soft Computing Techniques," Applied Soft Computing, Vol. 8(1), pp. 507-521.
  4. Bullnheimer, B., Hartl, R.F. and Ch. Strauss, 1999, "An Improved Ant System Algorithm for the Vehicle Routing Problem," Annals of Operations Research, Vol. 89, pp. 319-328.
  5. Christian Blum, 2005, "Ant Colony Optimization: Introduction and Recent Trends," Physics of life reviews, Vol. 2, pp. 353-373.
  6. Charles V Camp and Barron Bichon, J., 2004, "Design of Space Trusses Using Ant Colony Optimization," Journal of structural engineering, ASCE, Vol. 130(5), pp. 741-751.
  7. Charles Camp, V., Barron Bichon, J. and Scott Stovall, P., "Design of Steel Frame Using Ant Colony
  8. Optimization," Journal of Structural Engineering, ASCE, Vol.131(3), pp. 369-379.
  9. Chakraborthy. S. and Mukhopadhyay, M., 2002, "Determination of Physical Parameters of Stiffened Plates using Genetic Algorithm," Journal of Computing in Civil Engineering, Vol. 16(3), pp. 206- 221.
  10. Dorigo M., 1992, "Optimization, Learning and Natural Algorithms," Doctoral Dissertation, Politecnico di Milano, Italy.
  11. Dorigo, M.; L. M. Gambardella "Ant Colony System: A cooperative learning approach to the Travelling Salesman Problem", IEEE Transactions on Evolutionary Computation, vol. 1(1), 1997, pp. 53- 66.
  12. Dorigo, M., Maniezzo, V. and Colorni, A., 1994, "Ant System: Optimization by a Colony of Cooperating Agents," IEEE Transactions on Systems, Man and Cybernetics, Vol. 26(1), pp. 29-41.
  13. Eberhant, R.C. and Kennedy, J., 1995, "A New Optimizer using Particle Swarm Theory," Proceedings Of the 6th International Symposium on Micro Machine and Human Science, Nagaya, Japan, pp. 39-43.
  14. Friswell, M.I., Penny, J.E.T. and Garvey, S.D., 1998, "A Combined Genetic and Eigensensitivity Algorithm for the Location of Damage in Structures," Computers and Structures, Vol. 69(5), pp. 547-556.
  15. Haupt, R. L., 1999, "Practical Genetic Algorithms," John Wiley and Sons, New York.
  16. Hitoshi Furuta., Masahiro Yasui. and Saeri Fukuhara., 2005, "Structural Health Monitoring using Evolutionary Computing," 6th World Congress of Structural and Multidisciplinary Optimisation, Brazil.
  17. Hami, Y., Amodeo, L., Yalowi, F. and Chen, H., 2007, "Ant Colony Optimization for Solving Industrial Layout Problem," European Journal of Operation Research (Article in press).
  18. Hanagud, V., Meyyappa, M. and Craig, J.I., 1985, " Method of Multiple Scales and Identification of Nonlinear Structural Dynamic Systems," AIAA journal, Vol. 23(5), pp. 802-807.
  19. Qie, He., Ling Wang, and Bo Liu, 2007, "Parameter Estimation for Chaotic Systems by Particle Swarm Optimization," Chaos, Solitons and Fractals, Vol. 34, pp. 654-661.
  20. Hu, Y. and Zhou Z.Y., 2004, "The Particle Swarm Optimization Algorithm and Its Application in Design Optimisation of a Mini-UAV," Journal of Flight Dynamics, Vol. 22 (2), pp. 61-64.
  21. Jaco, F., Schutte Byung-Il Koh., Jeffrey, A., Reinbolt., Raphael, T., Haftka, Alan, D., George, and Benjamin Fregly, F., 2005, "Evaluation of a Particle Swarm Algorithm For Biomechanical Optimization," Journal of biomedical engineering, Vol. 127, pp. 465-474.
  22. Jesiel, C,. Scott, C. and Christophe, B., 1999, "Application of Genetic Algorithms for the Identification of Elastic Constants of Composite Materials from Dynamic Tests," International journal for numerical methods in engineering, Vol. 45, pp. 891-900.
  23. Gaetan Kerschena, Keith Worden, Alexander Vakakis, F. and Jean-Claude Golinval., 2006, "Past, Present and Future of Nonlinear System Identification in Structural Dynamics," Mechanical Systems and Signal Processing, Vol. 20, pp. 505-592.
  24. Kishore Kumar, R., Sandesh, S. and Shankar, K., 2007, "Parametric Identification of Non-Linear Dynamic Systems using Combined Levenberg-Marquardt and Genetic Algorithm," International Journal of Structural Stability and Dynamics, Vol.7(4).
  25. Koh, C.G., Chen, Y.F. and Liaw, C.Y., 2003, "A Hybrid Computational Strategy for Identification of Structural Parameters," Computers and structures, Vol. 81, pp. 107-117.
  26. Koh, C.G., See, L.M. and Balendra, T., 1991, "Estimation of Structural Parameters in Time Domain: A Substructural Approach," Earthquake Engineering and Structural Dynamics, Vol. 20, pp. 787-801.
  27. Koh, C,G. and Shankar, K., 2003, "Substructural Identification Method without Interface Measurement," Journal of engineering mechanics," Vol. 129(7), pp. 769-776.
  28. Michalwicz, Z., 1994, "Genetic Algorithms + Data structures = Evolutionary Programs" AI Series," Springer Verlag, New York, 1994.
  29. Mouser, C.R. and Dunn, S.A., 2005, "Comparing Genetic Algorithm and Particle Swarm Optimization for Inverse Problems," ANZIAM Journal, Vol. 46(e), pp. C89-C101.
  30. Paul Corry and Erhan Kozan., 2004, "Ant Colony Optimization for Machine Layout Problem," Computational Optimization and Application, Vol. 28, pp. 287-301.
  31. Peng-Yeng Yin., 2006, "Particle Swarm Optimization for Point Pattern Matching," Journal of Visual Communication Image Representation, Vol. 17, pp. 143-162.
  32. Perry, M.J., Koh, C.G. and Choo, Y.S., 2004, "Modified Genetic Algorithm for Structural Identification," Computers and Structures, Vol. 84, pp. 529-540.
  33. Rajamani, D. and Adil, G.K., 1996, "Machine Loading in Flexible Manufacturing Systems Considering Routing Flexibility," Intl J Adv Manuf Technology, Vol. 11(5), pp. 372-380.
  34. Serra, M. and Venini, P., "On Some applications of Ant Colony Optimization Metaheuristic to Plane Truss Optimization," Struct Multidisc Optim , Vol. 32, pp. 499-506.
  35. Shi, Y. and Eberhart, R., 1998, "Parameter Selection in Particle Swarm Optimization," Proceeding of the 7th Annual Conference on Evolutionary Programming, pp. 591-601.