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Catastrophic failure in brittle materials is often due to the rapid growthand coalescence of cracks aided by high internal stresses. Hence, accurateprediction of maximum internal stress is critical to predicting time to failureand improving the fracture resistance and reliability of materials. Existinghigh-fidelity methods, such as the Finite-Discrete Element Model (FDEM), arelimited by their high computational cost. Therefore, to reduce computationalcost while preserving accuracy, a novel deep learning model, "StressNet," isproposed to predict the entire sequence of maximum internal stress based onfracture propagation and the initial stress data. More specifically, theTemporal Independent Convolutional Neural Network (TI-CNN) is designed tocapture the spatial features of fractures like fracture path and spall regions,and the Bidirectional Long Short-term Memory (Bi-LSTM) Network is adapted tocapture the temporal features. By fusing these features, the evolution in timeof the maximum internal stress can be accurately predicted. Moreover, anadaptive loss function is designed by dynamically integrating the Mean SquaredError (MSE) and the Mean Absolute Percentage Error (MAPE), to reflect thefluctuations in maximum internal stress. After training, the proposed model isable to compute accurate multi-step predictions of maximum internal stress inapproximately 20 seconds, as compared to the FDEM run time of 4 hours, with anaverage MAPE of 2% relative to test data.

StressNet: Deep Learning to Predict Stress With Fracture Propagation in Brittle Materials