  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.
