A spatiotemporal deep learning framework is proposed that is capable of 2D full-field prediction of fracture in concrete mesostructures. This framework not only predicts fractures but also captures the entire history of the fracture process, from the crack initiation in the interfacial transition zone to the subsequent propagation of the cracks in the mortar matrix. In addition, a convolutional neural network is developed which can predict the averaged stress-strain curve of the mesostructures. The UNet modeling framework, which comprises an encoder-decoder section with skip connections, is used as the deep learning surrogate model. Training and test data are generated from high-fidelity fracture simulations of randomly generated concrete mesostructures. These mesostructures include geometric variabilities such as different aggregate particle geometrical features, spatial distribution, and the total volume fraction of aggregates. The fracture simulations are carried out in Abaqus, utilizing the cohesive phase-field fracture modeling technique as the fracture modeling approach. In this work, to reduce the number of training datasets, the spatial distribution of three sets of material properties for three-phase concrete mesostructures, along with the spatial phase-field damage index, are fed to the UNet to predict the corresponding stress and spatial damage index at the subsequent step. It is shown that after the training process using this methodology, the UNet model is capable of accurately predicting damage on the unseen test dataset by using 470 datasets. Moreover, another novel aspect of this work is the conversion of irregular finite element data into regular grids using a developed pipeline. This approach allows for the implementation of less complex UNet architecture and facilitates the integration of phase-field fracture equations into surrogate models for future developments.

翻译：本文提出了一种时空深度学习框架，能够对混凝土细观结构中的断裂行为进行二维全场预测。该框架不仅能预测断裂现象，还能捕捉断裂过程的完整历史，包括界面过渡区的裂纹萌生以及随后在砂浆基体中的裂纹扩展。此外，本研究还开发了一种卷积神经网络，能够预测细观结构的平均应力-应变曲线。采用包含跳跃连接的编码器-解码器结构的UNet建模框架作为深度学习代理模型。训练和测试数据通过随机生成的混凝土细观结构的高保真断裂模拟产生。这些细观结构包含几何变异性，如不同的骨料颗粒几何特征、空间分布以及骨料总体积分数。断裂模拟在Abaqus中完成，采用内聚力相场断裂建模技术作为断裂建模方法。为减少训练数据集数量，本研究将三相混凝土细观结构的三组材料属性空间分布与空间相场损伤指数共同输入UNet，以预测后续步骤的对应应力及空间损伤指数。结果表明，采用该方法训练后，UNet模型仅需470个数据集即可准确预测未见测试数据集上的损伤。本研究的另一创新点在于通过开发的流程将不规则有限元数据转换为规则网格。该方法允许采用复杂度较低的UNet架构，并为未来开发中将相场断裂方程集成至代理模型提供了便利。