  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.
