  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.
