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Biologically inspired soft computing methods in structural mechanics and engineering

  • Ghaboussi, Jamshid (Department of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign)
  • Published : 2001.05.25

Abstract

Modem soft computing methods, such as neural networks, evolutionary models and fuzzy logic, are mainly inspired by the problem solving strategies the biological systems use in nature. As such, the soft computing methods are fundamentally different from the conventional engineering problem solving methods, which are based on mathematics. In the author's opinion, these fundamental differences are the key to the full understanding of the soft computing methods and in the realization of their full potential in engineering applications. The main theme of this paper is to discuss the fundamental differences between the soft computing methods and the mathematically based conventional methods in engineering problems, and to explore the potential of soft computing methods in new ways of formulating and solving the otherwise intractable engineering problems. Inverse problems are identified as a class of particularly difficult engineering problems, and the special capabilities of the soft computing methods in inverse problems are discussed. Soft computing methods are especially suited for engineering design, which can be considered as a special class of inverse problems. Several examples from the research work of the author and his co-workers are presented and discussed to illustrate the main points raised in this paper.

Keywords

References

  1. Chang, F-K., and Lessard, L. (1991), "Damage tolerance of laminated composites containing an open hole and subjected to compressive loadings. Part I: Analysis", J. Composite Mat., 25, 2-43. https://doi.org/10.1177/002199839102500101
  2. Chou, J.H., and Ghaboussi, J. (1998), "Studies in bridge damage detection using genetic algorithm", Proc., 6th East Asia-Pacific Conf. Struct. Eng. & Constr., Taiwan.
  3. Chou, J.H., Ghaboussi, J., and Clark, R. (1998), "Application of neural networks to the inspection of railroad rail", Proc., 25th Ann. Conf. Review Progr. Quantit. NDE, Snowbird, Utah.
  4. Ghaboussi, J., Garrett, J.H., and Wu, X. (1991), "Knowledge-based modeling of material behavior with neural networks", J. Eng. Mech., ASCE, 117(1), 132-153. https://doi.org/10.1061/(ASCE)0733-9399(1991)117:1(132)
  5. Ghaboussi, J., Banan, M.R., and Florom, R.L. (1994), "Application of neural networks in acoustic wayside fault detection in railway engineering", Proc., W. Cong. Railway Research, Paris, France.
  6. Ghaboussi, J., Zhang, M., Wu, X., and Pecknold, D.A. (1997), "Nested adaptive neural networks: A new architecture", Proc., Int, Conf. on Artificial NN Eng., St. Louis, Mo.
  7. Ghaboussi, J., and Sidarta, D.E. (1998), "New nested adaptive neural networks (NANN) for constitutive modeling", Int. J. Comput. and Geotech., 22(1), 29-52. https://doi.org/10.1016/S0266-352X(97)00034-7
  8. Ghaboussi, J., and Lin, C.-C.J. (1998), "A new method of generating earthquake accelerograms using neural networks", Int. J. Earthquake Eng. & Struct. Dyn., 27, 377-396. https://doi.org/10.1002/(SICI)1096-9845(199804)27:4<377::AID-EQE735>3.0.CO;2-2
  9. Ghaboussi, J., Pecknold, D.A., and Zhang, M. (1998), "Autoprogressive training of neural networks in complex systems", Proc., Int, Conf. on Artificial NN Eng., St. Louis, Mo.
  10. Ghaboussi, J., Pecknold, D.A., Zhang, M., and HajAli, R. (1998). "Autoprogressive training of neural network constitutive models", Int. J. Numer. Meth. Eng., 42, 105-126. https://doi.org/10.1002/(SICI)1097-0207(19980515)42:1<105::AID-NME356>3.0.CO;2-V
  11. Ghaboussi, J., and Wu, X. (1998), "Soft computing with neural networks for engineering applications: fundamental issues and adaptive approaches", Struct. Eng. and Mech., An Int. J., 6(8), 955 -969. https://doi.org/10.12989/sem.1998.6.8.955
  12. Hertz, J., Krogh, A., and Palmer, R.G. (1991), Introduction to the Theory of Neural Computations, Addison-Wesley, Redwood City, California.
  13. Holland, J.H. (1975), Adaptation in Natural and Artificial Systems, The University of Michigan Press, Ann Arbor, Michigan.
  14. Kohonen, T. (1990), "The self organizing map", Proc. of the IEEE, 78(9), 1464-1480. https://doi.org/10.1109/5.58325
  15. Lin, C.-C.J., and Ghaboussi, J. (2000), "Generating multiple spectrum compatible accelerograms using stochastic neural networks", Int. J. Earthquake Eng. & Struct., to appear.
  16. Raich, A.M. (1998), "An evolutionary based methodology for representing and evolving structural design solutions", Ph.D thesis, Dept. of Civil and Environmental Engineering, Univ. of Illinois at Urbana-Champaign, Urbana, Illinois.
  17. Raich, A.M., and Ghaboussi, J. (1997), "Implicit representation in genetic algorithm using redundancy", Int. J. Evolutionary Computing, 5(3).
  18. Raich, A.M., and Ghaboussi, J. (1999), "Evolving structural design solutions using an implicit redundant genetic algorithm", Proc., Genetic and Evol. Comp. Conf., GECCO-99, Orlando, Florida.
  19. Ritter, H., Martinetz, T., and Schulten, K. (1992), Neural Computation and Self-Organizing Maps, Addison-Wesley Publishing Company, Reading, Massachusetts.
  20. Shrestha, S.M., and Ghaboussi, J. (1998), "Evolution of optimal structural shapes using genetic algorithm", J. Struct. Eng., ASCE, 124(8), 1331 - 1338. https://doi.org/10.1061/(ASCE)0733-9445(1998)124:11(1331)
  21. Sidarta, D.E., and Ghaboussi, J. (1998), "Constitutive modeling of geomaterials from non-uniform material tests", Int. J. Comput. and Geotech., 22(1), 53-71. https://doi.org/10.1016/S0266-352X(97)00035-9

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