StressNet：深度学习预测脆性材料中断裂扩展的应力
StressNet: Deep Learning to Predict Stress With Fracture Propagation in Brittle Materials
摘要：
脆性材料的灾难性破坏通常是由于高内应力辅助下裂纹的快速增长和合并。因此，准确预测材料的最大内应力对于预测材料的失效时间、提高材料的抗断裂能力和可靠性至关重要。现有的高保真方法，如离散元模型（FDEM），是有限的，其计算成本高。因此，为了在保持准确性的同时降低计算成本，提出了一种新的深度学习模型“StressNet”，以基于裂缝扩展和初始应力数据来预测最大内部应力的整个序列。更具体地说，时间无关卷积神经网络（TI-CNN）被设计为捕获裂缝的空间特征，如裂缝路径和剥落区域，双向长短期记忆（Bi-LSTM）网络被适配为捕获时间特征。通过融合这些特征，可以准确地预测最大内应力随时间的演化。此外，通过对均方误差（MSE）和平均绝对百分比误差（MAPE）的动态积分，设计了自适应损失函数，以反映最大内应力的波动。经过训练后，所提出的模型能够在大约20秒内计算出最大内应力的准确多步预测，而FDEM的运行时间为4小时，相对于测试数据，平均MAPE为2%。
英文摘要：
Catastrophic failure in brittle materials is often due to the rapid growth and coalescence of cracks aided by high internal stresses. Hence, accurate prediction of maximum internal stress is critical to predicting time to failure and improving the fracture resistance and reliability of materials. Existing high-fidelity methods, such as the Finite-Discrete Element Model (FDEM), are limited by their high computational cost. Therefore, to reduce computational cost while preserving accuracy, a novel deep learning model, "StressNet," is proposed to predict the entire sequence of maximum internal stress based on fracture propagation and the initial stress data. More specifically, the Temporal Independent Convolutional Neural Network (TI-CNN) is designed to capture the spatial features of fractures like fracture path and spall regions, and the Bidirectional Long Short-term Memory (Bi-LSTM) Network is adapted to capture the temporal features. By fusing these features, the evolution in time of the maximum internal stress can be accurately predicted. Moreover, an adaptive loss function is designed by dynamically integrating the Mean Squared Error (MSE) and the Mean Absolute Percentage Error (MAPE), to reflect the fluctuations in maximum internal stress. After training, the proposed model is able to compute accurate multi-step predictions of maximum internal stress in approximately 20 seconds, as compared to the FDEM run time of 4 hours, with an average MAPE of 2% relative to test data.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
