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Data-driven algorithm for real-time fatigue life prediction of structures with stochastic parameters
Computer Methods in Applied Mechanics and Engineering ( IF 7.2 ) Pub Date : 2020-12-01 , DOI: 10.1016/j.cma.2020.113373
S.Z. Feng , X. Han , Z.J. Ma , Grzegorz Królczyk , Z.X. Li

Abstract Fatigue crack growth analysis using extended finite element method (XFEM) is an efficient way to predict the residual life of structures; however, when the structure parameters vary stochastically, it will be very hard to make accurate predictions. To bridge this research gap, this work proposed a data-driven learning algorithm to improve the prediction capacity of fatigue life by considering stochastic parameters of structures. In this new algorithm, the XFEM was firstly employed to generate a large amount of datasets that pair the structural responses with remaining fatigue life. Then, the back propagation neural network (BPNN) was employed to construct a fatigue life prediction model based on the XFEM datasets. Real-time prediction for the structural fatigue life was achieved using the constructed BPNN model without knowing the exact distribution functions of stochastic parameters. Several numerical examples were performed to evaluate the performance of the proposed algorithm. The analysis results demonstrate that the proposed data-driven algorithm can accurately predict the fatigue life of the structures with stochastic parameters.

中文翻译:

随机参数结构实时疲劳寿命预测的数据驱动算法

摘要 采用扩展有限元法(XFEM)进行疲劳裂纹扩展分析是预测结构剩余寿命的有效方法;然而,当结构参数随机变化时,很难做出准确的预测。为了弥补这一研究空白,这项工作提出了一种数据驱动的学习算法,通过考虑结构的随机参数来提高疲劳寿命的预测能力。在这种新算法中,XFEM 首先用于生成大量数据集,这些数据集将结构响应与剩余疲劳寿命配对。然后,采用反向传播神经网络(BPNN)构建基于 XFEM 数据集的疲劳寿命预测模型。在不知道随机参数的准确分布函数的情况下,使用构建的 BPNN 模型实现了结构疲劳寿命的实时预测。进行了几个数值例子来评估所提出算法的性能。分析结果表明,所提出的数据驱动算法可以准确地预测具有随机参数的结构的疲劳寿命。
更新日期:2020-12-01
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