Prediction of Micro-pores in Carbon Nanotube Solid Waste Concrete Based on Machine Learning

Promoting the advanced recycling and utilization of solid waste resources is crucial for achieving sustainable green building development, with the introduction of nanomaterials emerging as a cutting-edge approach to enhance the overall performance of solid waste concrete. Since porosity is a key determinant of concrete performance and durability, carbon nanotubes (CNTs) can reduce porosity by optimizing microstructure; however, determining their optimal dosage remains challenging under limited experimental conditions. This study investigates pore evolution patterns through compressive strength tests and mercury compression tests. By integrating multi-source data, we developed a weighted XGBoost neighborhood data-enhanced multi-interpolation fusion model based on physical constraints, establishing a porosity prediction model with boundary extrapolation constraints. Results demonstrate that recycled concrete containing 0.102% CNTs exhibits optimal density and performance, with porosity showing a V-shaped trend. SHAP analysis confirmed the nucleating and filling effects of appropriate CNTs concentrations, with the dataset demonstrating excellent fit (R² = 0.8623) and good generalization capability (R_RMSE = 2.0778%). This approach achieves a balance between predictive accuracy and physical interpretability even with small sample sizes.