What question did this study set out to answer?

This research aims to develop a machine learning approach to accurately predict the compressive strength of internal-curing concrete using recycled roof-tile waste.

February 8, 2026Open Access

An Interpretable Hybrid Machine Learning Approach for Predicting the Compressive Strength of Internal-Curing Concrete Incorporating Recycled Roof-Tile Waste

Key Points

This research aims to develop a machine learning approach to accurately predict the compressive strength of internal-curing concrete using recycled roof-tile waste.
Developed a hybrid model combining random forest and particle swarm optimization (RF-PSO).
Conducted a comparative analysis of various methodologies before model selection.
Evaluated model performance using statistical metrics including R², RMSE, and MAE.
Performed SHAP analysis to interpret the impact of input variables on compressive strength.
Achieved an R² of 0.961, indicating high prediction accuracy.
Reduced RMSE to 5.361 MPa and MAE to 4.001 MPa, improving statistical error rates by nearly 30% compared to conventional models.
Identified water-to-cement ratio and curing age as primary predictors of compressive strength.

Abstract

The use of recycled materials as internal curing (IC) agents offers substantial benefits to the concrete industry by improving performance and enhancing environmental sustainability. However, the design of IC concrete has grown intricate due to the nonlinear interactions among many input variables. Previous research on IC is mostly experimental, with only a few studies focusing on predicting the compressive strength (CS) of IC concrete. In particular, machine learning has not been applied to quantify the effect of roof-tile waste (RTW) on the CS of IC concrete. This research presents an innovative hybrid model that combines random forest and particle swarm optimization (RF-PSO) to predict the CS of IC concrete using RTW as an IC aggregate. Before model building, a comparative analysis of potential methodologies was conducted, highlighting the key characteristics, benefits, and drawbacks. RF-PSO was then chosen, achieving enhanced accuracy with a coefficient of determination (R2) of 0.961, a root mean square error (RMSE) of 5.361 MPa, and a mean absolute error (MAE) of 4.001 MPa. The RF-PSO model improved prediction accuracy by increasing R2 from 0.906 to 0.961 and reducing statistical errors by nearly 30% compared with conventional machine learning models. A Shapley Additive exPlanations (SHAP) analysis was performed to interpret the model results. The analysis identified the water-to-cement ratio and curing age as the dominant predictors, while IC water contributed a secondary, age-dependent effect. The proposed framework makes contributions: it integrates SHAP-based interpretability into a high-accuracy RF-PSO model and provides a viable tool for reducing empirical trial mixes in sustainable design workflows. Despite the limited dataset, the findings provide a reproducible baseline for future expansion and highlight the potential of combining RTW with IC to improve early and long-term strength.

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