Modeling the mechanical properties of lightweight high-strength concrete incorporating supplementary cementitious materials using multi-expression programming and random forest

Lightweight high-strength concrete (LWHSC) is increasingly used as a sustainable material that reduces structural weight while maintaining performance. Promoting sustainability involves using less high-carbon cement and more supplementary cementitious materials (SCMs) like fly ash, silica fume, and slag. However, testing LWHSC’s mechanical behavior is costly and time-consuming, highlighting the need for reliable prediction tools. To address this, this study employs machine learning models multi-expression programming (MEP) and random forest (RF) to forecast the mechanical properties of LWHSC containing SCMs using a large dataset with eight key parameters, including water-to-binder ratio, cement, fly ash, slag, silica fume, aggregate, lightweight aggregate, and basalt fiber. Performance was evaluated with R2, MAE, RMSE, and MSE. Both models captured strength trends, but MEP was more accurate, especially for compressive strength (R2 = 0.98–0.99) versus RF (0.87–0.91), and similarly for tensile and flexural strengths. Errors mostly stayed below 3 MPa for CS, 0.5 MPa for TS, and 2 MPa for FS. Taylor diagrams confirmed MEP predictions closely matched experimental data. Additionally, SHapely Additive ExPlanations (SHAP) investigation demonstrated that the water-to-binder ratio and lightweight aggregate had a favorable impact on the mechanical properties of the LWHSC. The study highlights MEP as a robust, dependable tool for designing sustainable LWHSC by effectively combining SCMs with machine learning.