Alkali-Silica-Reaction (ASR) is one of the most deteriorating phenomena in concrete structures. This study uses a machine learning approach (i.e. Artificial Neural Network) to provide further insight into ASR. The approach combines chemo-mechanical and kinetics-based approaches to develop a time- and temperature-dependent model of ASR, which is eventually used in generating user-friendly charts to conveniently assess existing concrete structures. To reach a higher degree of confidence in the precision of the model, an experimental dataset was developed from the laboratory and was combined with a dataset from the literature. A comparison between the developed model and a chemo-mechanical one (Gao's model) showed higher accuracy for the developed model. This higher accuracy was more obvious regarding the specimen with fine single-size aggregate grading. This study also reveals a varying thickness of connected porosity $\left(t_c\right)$ for fine single-size aggregate. Based on the results, aggregate size and $t_c$ have a coupled effect on the ASR-induced expansion.

碱-二氧化硅反应（ASR）是混凝土结构中最恶化的现象之一。这项研究使用机器学习方法（即人工神经网络）来提供对ASR的进一步了解。该方法结合了基于化学-机械和动力学的方法，以开发时间和温度相关的ASR模型，该模型最终用于生成用户友好的图表，以方便地评估现有的混凝土结构。为了提高对模型精度的信心，从实验室开发了一个实验数据集，并将其与文献中的数据集进行了组合。所开发的模型与化学机械模型（高氏模型）之间的比较表明，所开发的模型具有更高的准确性。对于具有精细单一尺寸骨料分级的样品，这种更高的精度更为明显。这项研究还揭示了连通孔隙厚度的变化$（{Ť}_C）$适用于精细的单一尺寸骨料。根据结果​​，合计规模和${Ť}_C$ 对ASR引起的膨胀具有耦合作用。