An efficient stochastic dynamic analysis of soil media using radial basis function artificial neural network

Abstract

Since a lot of engineering problems are along with uncertain parameters, stochastic methods are of great importance for incorporating random nature of a system property or random nature of a system input. In this study, the stochastic dynamic analysis of soil mass is performed by finite element method in the frequency domain. Two methods are used for stochastic analysis of soil media which are spectral decomposition and Monte Carlo methods. Shear modulus of soil is considered as a random field and the seismic excitation is also imposed as a random process. In this research, artificial neural network is proposed and added to Monte Carlo method for sake of reducing computational effort of the random analysis. Then, the effects of the proposed artificial neural network are illustrated on decreasing computational time of Monte Carlo simulations in comparison with standard Monte Carlo and spectral decomposition methods. Numerical verifications are provided to indicate capabilities, accuracy and efficiency of the proposed strategy compared to the other techniques.

Keywords

stochastic analysis / random seismic excitation / finite element method / artificial neural network / frequency domain analysis / Monte Carlo simulation

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Abbreviations

ANN Artificial neural network

KL Karhunen–Loève

MC Monte Carlo

PC Polynomial chaos

RBF Radial basis function

SFEM Stochastic finite element method

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2017 Higher Education Press and Springer-Verlag Berlin Heidelberg