From Research Trend to Performance Prediction: Metaheuristic-Driven Machine Learning Optimization for Cement Pastes Containing Bio-Based Phase Change Materials.

Abstract

This study presents an integrated approach combining bibliometric analysis and machine learning to explore research trends and predict the performance of cement pastes containing bio-based phase change materials. A bibliometric review of 5928 articles from the Web of Science Core Collection was conducted using CiteSpace (v.6.3.R1) to identify research hotspots. A dataset of 100 experimental samples was compiled, including nine input variables and three output properties identified as thermal conductivity (Tc), latent heat capacity (LH) and compressive strength (CS). Four machine learning algorithms (SVR, RF, XGBoost, and CatBoost) were optimized using five metaheuristic algorithms (GA, PSO, WOA, GWO, and FFA), resulting in 24 optimized hybrid models. Of all the models considered, CatBoost-WOA achieved the best overall performance, with R² values of 0.927, 0.955, and 0.944, and RMSEs of 0.0057 W/m·K, 1.84 J/g, and 2.91 MPa for Tc, LH, and CS. Additionally, SVR-GWO and XGBoost-WOA also showed strong generalization and low error dispersion. The developed models provide a transferable and data-driven modeling pipeline for predicting the coupled thermal and mechanical behavior of cement pastes containing bio-based phase change materials.