Multi-objective Optimization Based on the RSM-MOPSO-GA Algorithm and Synergistic Enhancement Mechanism of High-Performance Porous Concrete

Abstract

Porous concrete (PC) can effectively mitigate various environmental problems associated with road space. Exploring high-performance porous concrete (HPPC) is essential to expanding its applications. However, the current single methods for improvement have almost reached a bottleneck, particularly in terms of mechanical properties. Therefore, this research seeks further breakthroughs from synergistic enhancement and multi-objective optimization. The optimization variables were identified through single-factor experiments, and the optimal solutions for the optimization objectives were subsequently obtained using the response surface methodology (RSM). To address the inherent limitation of RSM in delivering only a single optimal solution, this paper proposed a novel RSM-MOPSO-GA hybrid optimization algorithm. Meanwhile, the synergistic enhancement mechanisms were elucidated through microstructural analysis. The results indicate that the individual enhancement effects of basalt fiber (BF), nano-SiO₂ (NS), and waterborne epoxy resin (WER) are limited. However, the RSM-based optimization significantly improved the performance of HPPC, with compressive strength and flexural strength increased by 51.4% and 69.8%, respectively, and the permeability coefficient enhanced by 33.8%. Furthermore, the application of the RSM-MOPSO-GA algorithm produced a stable Pareto front containing 50 individuals for users' decision-making. The interaction between WER and NS at the microscale, combined with the reinforcement of BF at the mesoscale, establishes a synergistic enhancement mechanism. The research findings provide both a theoretical foundation and experimental basis for the further application of HPPC. Additionally, it also offers a novel solution to address the challenges of multi-objective optimization in concrete performance.