Auto-Regressive U-Net for Full-Field Prediction of Shrinkage-Induced Damage in Concrete
This work addresses the problem of computationally intensive damage prediction in concrete for materials science and engineering, offering an incremental improvement through a deep learning approach.
The paper tackles predicting time-dependent full-field damage in concrete by introducing an auto-regressive U-Net model to forecast damage evolution and a CNN to estimate mechanical properties, demonstrating high computational efficiency and robust performance on synthesized datasets.
This paper introduces a deep learning approach for predicting time-dependent full-field damage in concrete. The study uses an auto-regressive U-Net model to predict the evolution of the scalar damage field in a unit cell given microstructural geometry and evolution of an imposed shrinkage profile. By sequentially using the predicted damage output as input for subsequent predictions, the model facilitates the continuous assessment of damage progression. Complementarily, a convolutional neural network (CNN) utilises the damage estimations to forecast key mechanical properties, including observed shrinkage and residual stiffness. The proposed dual-network architecture demonstrates high computational efficiency and robust predictive performance on the synthesised datasets. The approach reduces the computational load traditionally associated with full-field damage evaluations and is used to gain insights into the relationship between aggregate properties, such as shape, size, and distribution, and the effective shrinkage and reduction in stiffness. Ultimately, this can help to optimize concrete mix designs, leading to improved durability and reduced internal damage.