Enhancing dimensional stability in cement pastes: a machine learning approach with metaheuristic-optimized categorical boosting

Abstract Low hydration activity and challenging management of delayed expansion restrict the application of fly ash $$\left(FA\right)$$ F A and $$MgO$$ MgO expansive additive $$\left(MEA\right)$$ M E A in cement pastes. Very few research on machine learning techniques for volume expansion $$\left({V}_{e}\right)$$ V e in such systems have been conducted already. This study develops a machine learning framework to predict the volume expansion $$\left({V}_{e}\right)$$ V e of cement pastes incorporating fly ash $$\left(FA\right)$$ F A and $$MgO$$ MgO expansive additive $$\left(MEA\right)$$ 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, $${V}_{e}$$ V e (%). A Categorical Boosting $$\left(CatBoost\right)$$ C a t B o o s t model was optimized using two recent metaheuristic algorithms, the Starfish Optimization Algorithm $$\left(StOA\right)$$ S t O A </jats