Multi-objective optimization of low-noise pervious concrete using a stacking ensemble learning and NSGA-II approach

Abstract

Abstract 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, comprising both proprietary experimental data and data from the literature, was first established to systematically train and construct a high-precision predictive model for the sound absorption performance of pervious concrete. Subsequently, this model was combined with previously established models for compressive strength and permeability to serve as the fitness functions for the NSGA-II genetic algorithm, which performed a multi-objective search for optima. The accuracy and reliability of the optimization results were then confirmed through experimental validation. Results indicate that aggregate gradation has a significant impact on the sound absorption of pervious concrete, with a relative performance improvement of 95.7% between the optimal and poorest gradations. The constructed Stacking ensemble learning model achieved a coefficient of determination (R 2 ) of 0.97, outperforming all individual models with minimal fluctuation. The proposed multi-objective optimization framework successfully resolved the intrinsic conflict between permeability, compressive strength, and sound absorption. The optimized mix proportion solution (O3) not only satisfied the standards for permeability and strength but also achieved superior sound absorption performance that surpassed all single-sized aggregate groups, with an error of only 8.9% between the model’s prediction and the experimental value.