Abstract Low hydration activity and challenging management of delayed expansion restrict the application of fly ash (FA) F A and MgO MgO expansive additive (MEA) M E A in cement pastes. Very few research on machine learning techniques for volume expansion (V₄) V e in such systems have been conducted already. This study develops a machine learning framework to predict the volume expansion (V₄) V e of cement pastes incorporating fly ash (FA) F A and MgO MgO expansive additive (MEA) M E A, materials whose low hydration activity and delayed expansion complicate their practical use. A dataset of 170 samples compiled from published literature was utilized, comprising four input variables—Portland cement content (PC PC, %), fly ash content (FA FA, %), MgO MgO expansive additive (MEA MEA, %), and curing age (SA SA, days) —to predict the target variable, V₄ V e (%). A Categorical Boosting (CatBoost) C a t B o o s t model was optimized using two recent metaheuristic algorithms, the Starfish Optimization Algorithm (StOA) S t O A </jats
Esmaeili-Falak et al. (Thu,) studied this question.