基于自适应重要抽样神经网络的变刚度复合材料层合板可靠性和灵敏度预测
Adaptive Importance Sampling based Neural Network framework for Reliability and Sensitivity Prediction for Variable Stiffness Composite Laminates with hybrid uncertainties
摘要：
在这项工作中，我们建议利用人工神经网络（ANN）为基础的二阶可靠性方法（SORM）和重要性抽样的优势，产生一个自适应重要性抽样为基础的人工神经网络，具体应用对失效概率和灵敏度估计的变刚度复合材料层压板（VSCL）板，在存在多个独立的几何和材料的不确定性。基于VSCL板的基频定义了案例研究的性能函数。使用所提出的方法的可靠性估计和灵敏度研究的准确性被认为是在使用基于人工神经网络的蛮力MCS方法获得的接近一致，与95%的显着的计算节省。此外，在敏感性研究部分，定量地强调了考虑层厚度随机性对失效概率估计的重要性。
英文摘要：
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.
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