Adaptive importance sampling based neural network framework for reliability and sensitivity prediction for variable stiffness composite laminates with hybrid uncertainties

Abstract

In this work, we propose to leverage the advantages of both the Artificial Neural Network (ANN) based Second Order Reliability Method (SORM) and Importance sampling to yield an Adaptive Importance Sampling based ANN, with specific application towards failure probability and sensitivity estimates of Variable Stiffness Composite Laminate (VSCL) plates, in the presence of multiple independent geometric and material uncertainties. The performance function for the case studies is defined based on the fundamental frequency of the VSCL plate. The accuracy in both the reliability estimates and sensitivity studies using the proposed method were found to be in close agreement with that obtained using the ANN based brute-force MCS method, with a significant computational savings of 95%. Moreover, the importance of taking into account the randomness in ply thickness for failure probability estimates is also highlighted quantitatively under the sensitivity studies section.

Seismic reliability analysis of building structures using subset simulation coupled with deep learning-based surrogate model

“…Xiao, Oladyshkin, and Nowak (2020) investigated an adaptive Kriging approach along with a stratified importance sampling. Mathew, Prajith, Ruiz, Atroshchenko, and Natarajan (2020) developed a framework coupling neural network with adaptive importance sampling, and so forth. Inspired by this research line, this study investigates a highly efficient approach, dubbed SS-DL, that fuses the subset simulation with a DL-based surrogate model for tackling the seismic reliability problem which is still scarce in the literature to the best of the authors’ knowledge.…”