This paper presents an innovative methodology that seamlessly integrates the peridynamic method with advanced deep learning techniques, specifically utilizing the Gated Recurrent Unit (GRU) neural network. This integration results in the development of a highly accurate and efficient model for predicting fatigue cracking and life. This model can effectively forecast the fatigue crack patterns and fatigue life, effectively addressing the limitations of existing data-driven models, which often struggle with accurately predicting fatigue crack growth. One of the key advancements of this study is the significant enhancement in numerical efficiency, reducing the computational cost to mere hundreds of seconds, a substantial improvement over traditional peridynamic simulations. The study begins by establishing a peridynamic fatigue damage model, which is used to generate a comprehensive dataset of mechanical behavior under fatigue loading. A strategy is developed to convert the mechanical data into a suitable format for deep learning, which enables the creation of well-structured training and testing datasets. The Peridynamic-Gated Recurrent Unit (PD-GRU) data-driven model is then proposed, demonstrating exceptional numerical performance and operational efficiency. Through a series of rigorous numerical analyses, the PD-GRU model's capabilities are validated, highlighting its potential as an innovative perspective and groundbreaking tool in the fatigue analysis of materials and structures.

中文翻译：

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一种新的疲劳开裂和寿命预测框架：近场动力学方法与深度神经网络的完美结合


本文提出了一种创新方法，该方法将近场动力学方法与先进的深度学习技术无缝集成，特别是利用门控循环单元 （GRU） 神经网络。这种集成导致了一个高度准确和高效的模型，用于预测疲劳开裂和寿命。该模型可以有效地预测疲劳裂纹模式和疲劳寿命，有效解决了现有数据驱动模型的局限性，这些模型通常难以准确预测疲劳裂纹扩展。这项研究的主要进步之一是数值效率的显著提高，将计算成本降低到仅数百秒，与传统的近场动力学模拟相比有了重大改进。该研究首先建立了一个近动力疲劳损伤模型，该模型用于生成疲劳载荷下机械行为的综合数据集。开发了一种策略，将机械数据转换为适合深度学习的格式，从而能够创建结构良好的训练和测试数据集。然后提出了近动力门控循环单元 （PD-GRU） 数据驱动模型，展示了卓越的数值性能和操作效率。通过一系列严格的数值分析，PD-GRU 模型的功能得到了验证，突出了其作为材料和结构疲劳分析的创新视角和开创性工具的潜力。