Reliability Analysis of Post-Tensioned Bridge Using Artificial Neural Network-Based Surrogate Model

  • David LehkýEmail author
  • Martina Šomodíková
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 517)


The reliability analysis of complex structural systems requires utilization of approximation methods for calculation of reliability measures with the view of reduction of computational efforts to an acceptable level. The aim is to replace the original limit state function by an approximation, the so-called response surface, whose function values can be computed more easily. In the paper, an artificial neural network based response surface method in the combination with the small-sample simulation technique is introduced. An artificial neural network is used as a surrogate model for approximation of original limit state function. Efficiency is emphasized by utilization of the stratified simulation for the selection of neural network training set elements. The proposed method is employed for reliability assessment of post-tensioned composite bridge. Response surface obtained is independent of the type of distribution or correlations among the basic variables.


Artificial neural network Latin hypercube sampling Response surface method Reliability Failure probability Load-bearing capacity 


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Brno University of TechnologyBrnoCzech Republic

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