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In this work, we propose to leverage the advantages of both the ArtificialNeural Network (ANN) based Second Order Reliability Method (SORM) andImportance sampling to yield an Adaptive Importance Sampling based ANN, withspecific application towards failure probability and sensitivity estimates ofVariable Stiffness Composite Laminate (VSCL) plates, in the presence ofmultiple independent geometric and material uncertainties. The performancefunction for the case studies is defined based on the fundamental frequency ofthe VSCL plate. The accuracy in both the reliability estimates and sensitivitystudies using the proposed method were found to be in close agreement with thatobtained using the ANN based brute-force MCS method, with a significantcomputational savings of 95\%. Moreover, the importance of taking into accountthe randomness in ply thickness for failure probability estimates is alsohighlighted quantitatively under the sensitivity studies section.

composite structures

Adaptive Importance Sampling based Neural Network framework for  Reliability and Sensitivity Prediction for Variable Stiffness Composite  Laminates with hybrid uncertainties