Abstract This paper investigates the applicability of modelling stiffness degradation in fiber reinforced polymer (FRP) composites with a state-of-the-art artificial neural network (ANN) architecture. β-variational autoencoder (β-VAE) is first applied to extract disentangled latent features to represent the underlying driving mechanism. A neural ordinary differential equation (neural ODE) is then adopted to learn the dynamics of the latent features, which enables a continuous prediction of the stiffness over the cycle-domain. The ANN model is trained and validated before compared to both conventional mechanical and phenomenological models, where the ANN-based model shows comparable performance. In addition, a latent S-N curve is proposed based on latent variable analysis, which shows better correlations to the experimental data over the traditional S-N curve. Overall, with recent rapid developments in ANN architectures and algorithms, the ANN model is found to be a very promising tool for solving fatigue-related engineering problems for FRP structures when properly used.

中文翻译：

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神经网络在循环荷载作用下复合材料层合板刚度退化模型中的应用

摘要 本文研究了使用最先进的人工神经网络 (ANN) 架构对纤维增强聚合物 (FRP) 复合材料中的刚度退化建模的适用性。β-变分自编码器（β-VAE）首先用于提取解开的潜在特征以表示潜在的驱动机制。然后采用神经常微分方程（神经 ODE）来学习潜在特征的动力学，从而能够连续预测循环域上的刚度。ANN 模型在与传统的机械和现象学模型相比之前经过训练和验证，其中基于 ANN 的模型显示出可比的性能。此外，基于潜在变量分析提出了潜在SN曲线，与传统 SN 曲线相比，它显示出与实验数据更好的相关性。总体而言，随着最近 ANN 架构和算法的快速发展，发现 ANN 模型在正确使用时是解决 FRP 结构疲劳相关工程问题的非常有前途的工具。