Advanced stochastic finite element simulations and reliability analysis
Abstract
This thesis presents a series of methodologies that have been implemented in the framework of SFEM and reliability analysis in order to reduce the computational effort involved .The first methodology is a neural network-based subset simulation in which neural networks are trained and then used as robust meta-models in order to increase the efficiency of subset simulation with a minimum additional computational effort. In the second methodology neural networks are used in the framework of MCS for computing the reliability of stochastic structural systems, fields, by providing robust neural network estimates of the structural response. The third methodology consists of constructing an adaptive sparse polynomial chaos (PC) expansion of the response of stochastic systems in the framework of spectral stochastic finite element method (SSFEM). The proposed methodology utilizes the concept of variability response function (VRF) in order to compute an a priori low cost estimation of the spatial ...
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