Multi-objective optimization of low-noise pervious concrete using a stacking ensemble learning and NSGA-II approach (opens in new tab)

The inherent performance conflicts among the acoustic, mechanical, and hydraulic properties of pervious concrete represent a core obstacle to its application as a low-noise pavement material. To address this challenge, this paper proposes a multi-objective synergistic optimization method based on Stacking ensemble learning and the NSGA-II algorithm to proactively optimize mix proportions, thereby achieving a balance and enhancement of multiple performance metrics. A comprehensive database, co...