Experimental and explainable machine learning assessment of flexural strength in cement-free plastic–sand composites

The extensive consumption of cement-based materials and the growing accumulation of plastic waste present serious environmental challenges due to high carbon emissions and long-term pollution. To address these issues, this study proposes a cement-free construction approach in which cement is completely eliminated, and waste plastic is utilized as a binding material to develop plastic–sand composites. Unlike existing studies that primarily focus on partial cement replacement, this research integrates composition-based material development, basalt fibre reinforcement, flexural-strength (F-S) characterization, and explainable machine learning within a unified framework. A comprehensive experimental programme was conducted to evaluate the influence of sand size, plastic type and content, and basalt fibre reinforcement on F-S. Experimental results showed that finer sand produced higher F-S (up to 4.26 MPa), commercial plastic-based composites outperformed waste plastic composites (maximum 4.91 MPa), and the incorporation of basalt fibres significantly enhanced F-S by approximately 35%. Data-driven models, including support vector regression (SVR), extreme gradient boosting (XGB), and gene expression programming (GEP), were developed using the experimental dataset. Model evaluation using statistical indicators and k-fold cross-validation demonstrated high predictive accuracy, with XGB achieving the best performance (R² = 0.99, RMSE = 0.113 MPa). Explainable artificial intelligence analysis using SHAP quantified the relative contribution of each input parameter, identifying basalt fibre content and sand size as the most influential parameters governing flexural behaviour, while GEP provided empirical equations for transparent strength prediction. The proposed framework demonstrates the feasibility of cement-free composites combined with explainable AI for sustainable and performance-oriented construction materials.