Sustainable foam glass property prediction using machine learning: A comprehensive comparison of predictive methods and techniques

• GPR achieved the highest correlation coefficients and lowest error metrics, excelling in capturing complex relationships. • While GB and RF models performed well, they fell short of GPR in prediction accuracy and error reduction. • Critical variables affecting FG properties include density, foam agent content, and sintering temperature. Foam glass (FG) is characterized by its lightweight structure and exceptional insulating properties, making it a highly suitable material for a wide range of applications. It is produced by foaming molten glass, resulting in a cellular structure that enhances its insulation and impact-resistant properties. Due to its sustainability and durability, FG is increasingly used in the construction, automotive, and packaging sectors. In this context, the current study proposes a novel approach by developing a thoughtful system for assessing performance and intelligent design utilizing ML models such as Gradient Boosting (GB), Random Forest (RF), Gaussian Process Regression (GPR), and Linear Regression (LR) to predict porosity and compressive strength (CS) of FG. The dataset comprises 214 data points, encompassing input variables such as glass particle diameter, foam agent content, heating rate, holding time, sintering temperature, and dry density, with output parameters of porosity and CS. Data preprocessing involved Pearson correlation analysis to address multicollinearity and reveal nonlinear relationships among variables. Model performance was evaluated through R-values, mean absolute error, and root mean square error metrics, demonstrating that the GPR model achieved superior prediction accuracy with R-values of 0.91 and 0.82 for porosity and CS, respectively. The GB model followed closely, while the RF and LR models showed lower accuracy. Partial dependence plots and global feature importance analyses highlighted density and foam agent content as critical factors influencing FG properties. By achieving the most precise predictions with minimal error distributions, the GPR model offers actionable insights into FG design. These findings enable the optimization of FG production by providing reliable tools for predicting and controlling porosity and CS, reducing material waste, enhancing product quality, and streamlining manufacturing processes. This study demonstrates the potential of advanced ML techniques to bridge the gap between predictive modeling and practical applications in the digital design of FG.