Machine learning for the optimization of porosity-hygroscopy correlations of porous geopolymers in indoor thermal comfort: A hybrid feature selection approach

Geopolymers are recognized as sustainable and environmentally friendly materials with a notable hygroscopic capacity that provides several advantages, particularly concerning thermal comfort. Optimizing the selection of variables with the most significant impact is essential for enhanced performance. However, conducting experimental tests to establish porosity hygroscopy correlations is costly regarding labor, time, and material resources. This study aims to employ a hybrid feature selection technique based on a multi-objective algorithm incorporating RReliefF and NSGA-II to streamline the geopolymer matrices by automatically selecting the most impactful and significant variables for their hygroscopic properties. Upon evaluating this feature selection method with laboratory-collected data, the Root Mean Squared Error (RMSE), Mean Squared Error (MSE), and Mean Absolute Error (MAE) results are significantly superior to those of other existing methods. These results underscore the importance of intrinsic parameters such as porosity, tortuosity, and pore diameter, along with external parameters like temperature and humidity, which directly affect hygroscopy. Consequently, this approach is expected to reduce experimental efforts and expedite the development of new geopolymer materials.