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An artificial neural network for the fatigue study of bonded FRP-wood interfaces

机译：人工神经网络用于FRP-木材粘结界面疲劳研究

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摘要

The objective of this study is to explore the development of an artificial neural network (ANN) method for the analysis of load ratio effects on fatigue of interfaces for phenolic fiber reinforced polymer (FRP) composite bonded to red maple wood. Experiments were performed with a contoured double cantilever beam (CDCB) specimen under load control, and the crack propagation rate was obtained by the compliance method. Using linear elastic fracture mechanics, the influence of load ratio on fatigue crack growth rate was studied; leading to a modified Paris Law equation based on strain energy release rate range, ΔG, and mean value of strain energy release rate, G_(mean). By constructing suitable network architectures, an ANN can be defined and trained using existing experimental data sets, to provide in turn output fatigue data sets for new input parameters. The crack growth rate as predicted by the ANN approach is compared with the experimental output and theoretical prediction from a modified Paris Law equation. It is shown that the proposed neural network model is able to predict valuable fatigue responses, such as crack growth rate, that would facilitate the development of design guidelines for hybrid material bonded interfaces.
机译：本研究的目的是探索一种人工神经网络（ANN）方法的发展，该方法用于分析负载比对粘结到红枫木的酚醛纤维增强聚合物（FRP）复合材料的界面疲劳的影响。在载荷控制下，采用轮廓双悬臂梁（CDCB）进行了实验，并通过柔度法获得了裂纹扩展速率。利用线性弹性断裂力学，研究了载荷比对疲劳裂纹扩展速率的影响。根据应变能释放率范围ΔG和应变能释放率平均值G_（mean）得出修正的Paris Law方程。通过构建合适的网络架构，可以使用现有的实验数据集定义和训练ANN，从而依次为新的输入参数提供输出疲劳数据集。将ANN方法预测的裂纹扩展速率与实验输出和修改后的Paris Law方程的理论预测进行比较。结果表明，所提出的神经网络模型能够预测有价值的疲劳响应，例如裂纹扩展速率，这将有助于制定混合材料粘结界面的设计准则。

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