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 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 h, with an average MAPE of 2% relative to test data.

脆性材料的灾难性破坏通常是由于高内应力导致的裂纹的快速增长和聚结。因此，最大内应力的准确预测对于预测失效时间并提高材料的抗断裂性和可靠性至关重要。现有的高保真度方法（例如有限离散元素模型（FDEM））受其高计算量的限制。因此，为了在保持精度的同时降低计算成本，提出了一种深度学习模型StressNet，该模型基于裂缝的传播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，时间独立卷积神经网络（TI-CNN）旨在捕获诸如裂缝路径和剥落区域等裂缝的空间特征，双向长短期记忆（Bi-LSTM）网络适用于捕获时间特征。通过融合这些特征，可以准确地预测最大内应力随时间的变化。此外，通过动态地集成均方误差（MSE）和平均绝对百分比误差（MAPE）来设计自适应损失函数，以反映最大内应力的波动。训练后，与FDEM运行4小时相比，所提出的模型能够在大约20秒内计算出最大内部应力的准确的多步预测，相对于测试数据，平均MAPE为2％。通过动态平均均方误差（MSE）和平均绝对百分比误差（MAPE）来设计自适应损失函数，以反映最大内应力的波动。训练后，与FDEM运行4小时相比，所提出的模型能够在大约20秒内计算出最大内部应力的准确的多步预测，相对于测试数据，平均MAPE为2％。通过动态平均均方误差（MSE）和平均绝对百分比误差（MAPE）来设计自适应损失函数，以反映最大内应力的波动。训练后，与FDEM运行4小时相比，所提出的模型能够在大约20秒内计算出最大内部应力的准确的多步预测，相对于测试数据，平均MAPE为2％。