用自回归U-Net预测混凝土收缩损伤演化，提升计算效率。
Auto-Regressive U-Net for Full-Field Prediction of Shrinkage-Induced Damage in Concrete
- 采用自回归U-Net逐时预测损伤场演化
- 模型在合成数据上实现高精度损伤与力学性能预测
- 适合混凝土材料设计优化与耐久性评估
本文提出一种深度学习方法，用于预测混凝土的时变全场损伤。研究使用自回归U-Net模型，基于微结构几何和施加的收缩分布，预测单元体中标量损伤场的演化。通过将前一时刻的损伤预测结果作为下一时刻输入，模型可实现损伤演化的连续评估。同时，一个卷积神经网络（CNN）利用损伤估计值预测关键力学性能，包括实测收缩率和残余刚度。所提出的双网络架构在合成数据集上表现出高计算效率与强鲁棒性。该方法显著降低传统全场损伤评估的计算负荷，并用于揭示骨料形状、尺寸及分布对有效收缩和刚度退化的影响关系。最终有助于优化混凝土配合比设计，提升耐久性并减少内部损伤。
原文摘要 · Abstract (English)
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.
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