Algorithms for System Identification

  • Todor ZhelyazovEmail author
  • Rajesh Ruphakety
  • Simon Olaffson
Conference paper
Part of the Structural Integrity book series (STIN, volume 8)


Implementations of different algorithms designed for material constant identification are discussed in this contribution. Identification is performed by varying the input variables (i.e., the material constants) and juxtaposing the results obtained by analysis of the model and some benchmark example. In order to reduce the iterations needed to achieve a good agreement with desired results, different numerical strategies can be employed. One of the possibilities is to use a genetic algorithm. The combination of finite element analysis and identification algorithm is a strong tool but it is time consuming and very demanding in computational resources. A surrogate modeling can be employed to reduce computational time. Generally, it consists in replacing the original model with a simplified one. Two approaches are taken into consideration herein: the polynomial chaos expansion and the artificial neural network. The efficiency of the above-mentioned algorithms is to be assessed in terms of computational resource.


FEM Material constants identification Artificial neural network 


  1. 1.
    Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, Reading (1989)zbMATHGoogle Scholar
  2. 2.
    Ghamen, R.G., Spanos, P.D.: Stochastic Finite Elements: Spectral Approach. Springer, Berlin (1991)Google Scholar
  3. 3.
    Hurtado, J.E.: An examination of methods for approximating implicit limit state functions from viewpoint of statistical learning theory. Struct. Saf. 26(3), 271–293 (2004)CrossRefGoogle Scholar
  4. 4.
    Kaymaz, I.: Application of Kriging method to structural reliability problems. Struct. Saf. 27(2), 133–151 (2005)CrossRefGoogle Scholar
  5. 5.
    Echard, B., Gayton, N., Lemaire, M.: AK-MCS: an active learning reliability method combining Kriging and Monte Carlo simulation. Struct. Saf. 33(2), 145–154 (2011)CrossRefGoogle Scholar
  6. 6.
    Lehký, D., Šomodiková, M.: Reliability calculation of time-consuming problems using a small-sample artificial neural network-based response surface method. Neural Comput. Appl. 28, 1249–1263 (2017)CrossRefGoogle Scholar
  7. 7.
    Kriesel, D.: A Brief Introduction to Neural Networks.

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Todor Zhelyazov
    • 1
    Email author
  • Rajesh Ruphakety
    • 2
  • Simon Olaffson
    • 2
  1. 1.Technical University of SofiaSofiaBulgaria
  2. 2.Earthquake Engineering Research Center (EERC), University of IcelandSelfossIceland

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