Prediction of Progressive Frost Damage Development of Concrete Using Machine-Learning Algorithms

Abstract

Understanding the deterioration mechanism of concrete structures in cold climates that are susceptible to frost damage from repeated freezing and thawing cycles is imperative for ensuring their durability and serviceability. This study analyzed the impact of freeze–thaw (FT) exposure on concrete structural behavior by developing three machine-learning approaches—artificial neural networks (ANN), random forests (RF), and support vector machines (SVM)—to quantify the progressive loss in compressive strength after repeated FT cycles. The results demonstrate that all of the proposed models can predict the deteriorated compressive strength of concrete and align closely with the experimental results. The ANN model demonstrated the highest prediction accuracy with an R2 of 0.924, exhibiting a higher prediction accuracy than RF and SVM models. Sensitivity analysis using Shapley additive explanations (SHAP) revealed that concrete with an initially high strength, along with a lower water–cement ratio and air entrainment, exhibited the least reduction in compressive strength after freezing–thawing cycles, underlining the positive impact of these factors on the FT durability of concrete. The proposed modeling approach accurately predicts the residual compressive strength after FT exposure, enabling the selection of optimal concrete materials and structural designs for cold climates.

Keywords

• freeze–thaw
• machine learning
• artificial neural network
• durability