Early-age stress (EAS) is an important index for evaluating the early-age cracking risk of concrete. This paper encompasses a thermo-chemo-mechanical (TCM) model and active ensemble learning (AEL) for predicting the EAS evolution. The TCM model provides the data for the AEL model. First, based on Fourier's law, Arrhenius’ equation, and rate-type creep law, a TCM model is built to simulate the heat transfer, cement hydration, and viscoelasticity, which together determine the EAS evolution. Then, a material model composed of an eXtreme Gradient Boosting model and adjusted Model Code 2010 is built to allow for parametric study and database construction. Finally, an AEL framework is built, which incorporates principal component analysis (PCA), Gaussian process, and light gradient boosting machine (LGBM). This study resulted in the following findings: (1) The dimensionality of the 672-by-1 EAS vector can be effectively reduced by PCA, and the first principal component (PC) is a global index representing the magnitude of the EAS; (2) the mechanical field of the TCM model is validated by testing data. Correlation analysis on the first PC quantifies the influence of various input parameters of the TCM model, which is in accordance with common understandings of the EAS evolution process. (3) The AEL and one-shot ensemble learning (OSEL) both achieve high prediction performance in the testing set, whose R² reaches 0.961 and 0.948, respectively. Thanks to the uncertainty-based query procedure, comparing with OSEL, AEL shows advantages in prediction performance over the whole training history. (4) AEL can significantly reduce the number of samples required for training, which can be a major improvement in efficiency considering the computational cost of the TCM model.

中文翻译：

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通过热化学力学模型和主动集成学习预测约束混凝土的早期应力演化

早期应力（EAS）是评价混凝土早期开裂风险的重要指标。本文包含用于预测 EAS 演化的热化学机械 (TCM) 模型和主动集成学习 (AEL)。TCM 模型为 AEL 模型提供数据。首先，基于傅立叶定律、阿累尼乌斯方程和速率型蠕变定律，建立了一个TCM模型来模拟传热、水泥水化和粘弹性，它们共同决定了EAS的演化。然后，构建由 eXtreme Gradient Boosting 模型和调整后的 Model Code 2010 组成的材料模型，以进行参数研究和数据库构建。最后，构建了一个AEL框架，该框架结合了主成分分析（PCA）、高斯过程和光梯度提升机（LGBM）。本研究得出以下发现：（1）PCA可以有效降低672×1 EAS向量的维数，第一主成分（PC）是代表EAS大小的全局指标；(2)通过测试数据验证TCM模型的力学场。首台PC相关性分析量化了TCM模型各种输入参数的影响，符合EAS演化过程的共识。(3) AEL 和 one-shot ensemble learning (OSEL) 在测试集中都取得了很高的预测性能，其 (2)通过测试数据验证TCM模型的力学场。首台PC相关性分析量化了TCM模型各种输入参数的影响，符合EAS演化过程的共识。(3) AEL 和 one-shot ensemble learning (OSEL) 在测试集中都取得了很高的预测性能，其 (2)通过测试数据验证TCM模型的力学场。首台PC相关性分析量化了TCM模型各种输入参数的影响，符合EAS演化过程的共识。(3) AEL 和 one-shot ensemble learning (OSEL) 在测试集中都取得了很高的预测性能，其R ²分别达到 0.961 和 0.948。由于基于不确定性的查询过程，与 OSEL 相比，AEL 在整个训练历史中的预测性能上显示出优势。(4) AEL 可以显着减少训练所需的样本数量，考虑到 TCM 模型的计算成本，这可以大大提高效率。