Prediction of Compressive Strength of Carbon Nanotube Reinforced Concrete Based on Multi-Dimensional Database

The incorporation of carbon nanotubes (CNTs) enhances the mechanical properties of cement-based materials by inhibiting micro-crack propagation. Machine learning provides an efficient approach for predicting the compressive strength of CNT-reinforced concrete, yet existing studies often lack important features and rely on less adaptive models. To address these issues, a multi-dimensional database (429 experimental data points) covering 11 factors (including cement mix ratio, CNT morphology, and dispersion process) was constructed. A hierarchical model verification and optimization was conducted: traditional regression models (Multiple Linear Regression, Multiple Polynomial Regression (MPR), Multivariate Adaptive Regression Splines), mainstream model (Support Vector Regression (SVR)), and ensemble learning models (Random Forest, eXtreme Gradient Boosting (XGB), Light Gradient Boosting Machine optimized by Particle Swarm Optimization (PSO)/Bayesian Optimization (BO)) are trained, compared, and evaluated. MPR performs best (test set R2 = 0.856) among traditional regression models, while SVR (test set R2 = 0.824) is less accurate. The highest accuracy in ensemble models is achieved by the PSO-optimized XGB model, with R2 = 0.910 (test set). PSO outperforms BO in optimization precision, while BO is much more efficient. Water–cement ratio, age, and sand–cement ratio are the primary influencing factors for strength. Among CNT parameters, the inner diameter has greater impact than the length and outer diameter. Optimal CNT parameters are CNT–cement mass ratio 0.1–0.3%, inner diameter ≥ 7.132 nm, and length 1–15 μm. Surfactant polycarboxylate can increase strength, while OH− functional groups can decrease it. These findings, integrated into the high-precision PSO-XGB model, provide a powerful tool for optimizing the mix design of CNT-reinforced concrete, accelerating its development and application in the industry.