Prediction of alkali-silica reaction expansion of concrete using explainable machine learning methods

Traditionally, ASR expansion is determined using experimental and finite element method (FEM) based numerical modelling. However, these methods are time-consuming and computationally costly, which makes ASR prediction challenging. Machine learning (ML) techniques can serve as effective alternatives for the early detection of expansion in concrete structures. In this study, we developed four different machine learning models – extreme gradient boosting (XGBoost), random forest (RF), support vector regression (SVR), and k-nearest neighbor (KNN) to predict the ASR expansion in concrete using a comprehensive dataset with 1896 data points. Each model was evaluated based on the model performance and XGBoost shows the most effective model for predicting the ASR expansion with R2 of 0.99 for training and R2 of 0.98 for testing. After identifying the best-performing model, Shapley Additive Explanations (SHAP) were employed to interpret its predictions. This approach provides insights into the model’s decision-making process, clarifying the complex nature of machine learning algorithms. Using Shapley Additive Explanations (SHAP), time and the average size of reactive aggregate were identified as critical parameters influencing ASR expansion. This research holds significant value for the construction industry, as accurately predicting ASR expansion can lead to optimized material usage and improved structural performance.