Advertisement

Soft-computing-based parameter identification as the basis for prognoses of the structural behaviour of tunnels

  • Bernhard Pichler
  • Roman Lackner
  • Herbert A. Mang
Chapter

Abstract

A parameter identification (PI) method for determination of unknown model parameters in tunnelling is presented. The PI method is based on measurement data provided by the construction site. Model parameters for finite element (FE) analyses are identified such that the results of these calculations meet the available measurements as well as possible. For the determination of the unknown parameter set, use of an artificial neural network (ANN) is proposed. The network is trained to approximate results of already performed FE simulations. A genetic algorithm (GA) uses the trained ANN to provide a prognosis for an optimal parameter set which, finally, must be assessed by an additional FE analysis. In contrast to other gradient-free methods requiring a large number of FE simulations, the proposed PI method renders back analysis of model parameters feasible even for large-scale models. Finally, the performance of this PI method as the basis for prognoses of the structural behaviour of a tunnel is demonstrated.

Keywords

Artificial Neural Network Finite Element Analysis Hide Layer Structural Behaviour Network Weight 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [1]
    Darwin, C. (1995) Origin of species. Modern Library, Random House.Google Scholar
  2. [2]
    Duvaut, G., Lions, J.L. (1971) Les inequations en mechanique et en physique [The inequations in mechanics and physics]. Dunod, Paris. In French.Google Scholar
  3. [3]
    Feng, X.-T., Zhang, Z., and Sheng, Q. (2000) Estimating mechanical rock mass parameters relating to the Three Gorges Project permanent shiplock using an intelligent displacement back analysis method. International Journal of Rock Mechanic and Mining Sciences, 37: 1039–1054.CrossRefGoogle Scholar
  4. [4]
    Gioda, G., Sakurai, S. (1987) Back analysis procedure for the interpretation of field measurements in geomechanics. International Journal for Numerical and Analytical Methods in Geomechanics, 11: 555–583.MATHCrossRefGoogle Scholar
  5. [5]
    Goldberg, D. E. (1989) Genetic algorithms in search, optimisation, and machine learning. Addison Wesley, Reading, Massachusetts.Google Scholar
  6. [6]
    Gupta, J. N. D., Sexton, R. S. (1999) Comparing backpropagation with a genetic algorithm for neural network training. Omega, The International Journal of Management Science, 27: 679–684.Google Scholar
  7. [7]
    Huber, N. (2000) Anwendung Neuronaler Netze bei nichtlinearen Problemen der Mechanik [Application of neural networks to nonlinear problems of mechanics]. Habilitation-thesis, Forschungszentrum Karlsruhe GmbH, Karlsruhe, Germany. In German.Google Scholar
  8. [8]
    Lackner, R., Hellmich, Ch., and Mang, H.A. (2002) Constitutive modelling of cementitious materials in the framework of chemoplasticity. International Journal for Numerical Methods in Engineering, 53: 2357–2388.MATHCrossRefGoogle Scholar
  9. [9]
    Mahnken, R., Stein, E. (1995) Parameter identification for viscoplastic models based on analytical derivatives of a least-squares functional and stability investigations. International Journal of Plasticity, 12: 451–479.CrossRefGoogle Scholar
  10. [10]
    Ohkami, T., Ichikawa, Y. (1997) A parameter identification procedure for viscoelastic materials. Computers and Geotechnics, 21: 255–275.CrossRefGoogle Scholar
  11. [11]
    Ohkami, T., Swoboda, G. (1999) Parameter identification of viscoelastic materials. Computers and Geotechnics, 24: 279–295.CrossRefGoogle Scholar
  12. [12]
    Pichler, B., Lackner, R., and Mang, H.A. (2001) Soft-computing based structural back analysis of material model parameters for soil in the context of tunnelling according to the New Austrian Tunnelling Method. In Valliappan, S. (editor) Proceedings of the 1st Asian Pacific Congress on Computational Mechanics, 2: 1101–1108.Google Scholar
  13. [13]
    Pichler, B., Lackner, R., and Mang, H.A. (2002) Back analysis of model parameters in geotechnical engineering by means of soft computing. International Journal for Numerical Methods in Engineering, Accepted for publication.Google Scholar
  14. [14]
    Pichler, B., Mang, H.A. (2001) Parameter identification for sophisticated material models by means of iterative back analyses based on soft computing. In Waszczyszyn, Z., et al. (editors) CD-ROM Proceedings of the 2nd European Conference on Computational MechanicsGoogle Scholar
  15. [15]
    Rojas, R. (1996) Theorie der neuronalen Netze: eine systematische Einführung [Theory of neural networks: A systematic introduction]. Springer, Berlin. In GermanGoogle Scholar
  16. [16]
    Sakurai, S., and Takeuchi, K. (1983) Back analysis of measured displacements of tunnels. Rock Mechanics and Rock Engineering, 16: 173–180.CrossRefGoogle Scholar
  17. [17]
    Swoboda, G., Ichikawa, Y., Dong, Q., and Zaki, M. (1999) Back analysis of large geotechnical models. International Journal for Numerical and Analytical Methods in Geomechanics, 23: 1455–1472.MATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Wien 2003

Authors and Affiliations

  • Bernhard Pichler
    • 1
  • Roman Lackner
    • 1
  • Herbert A. Mang
    • 1
  1. 1.Institute for Strength of MaterialsVienna University of TechnologyAustria

Personalised recommendations