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Neural network aided stochastic computations and earthquake engineering

George Stefanou , Manolis Papadrakakis , Michalis Fragiadakis , Nikos Lagaros , Yiannis Tsompanakis

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Περίληψη

This article presents recent developments in the field of stochastic finite element analysis of structures and earthquake engineering aided by neural computations. The incorporation of neural networks (NN) in this type of problems is crucial since it leads to substantial reduction of the excessive computational cost. In particular, a hybrid method is presented for the simulation of homogeneous non-Gaussian stochastic fields with prescribed target marginal distribution and spectral density function. The presented method constitutes an efficient blending of the Deodatis-Micaletti method with a NN based function approximation. Earthquake-resistant design of structures using probabilistic safety analysis is an emerging field in structural engineering. We investigate the efficiency of soft computing methods when incorporated into the solution of computationally intensive earthquake engineering problems.

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