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Multi-objective optimization based on the RSM-MOPSO-GA algorithm and synergistic enhancement mechanism of high-performance porous concrete
Journal of Cleaner Production ( IF 9.7 ) Pub Date : 2024-12-16 , DOI: 10.1016/j.jclepro.2024.144492
Guanglei Qu, Mulian Zheng, Chuan Lu, Jiakang Song, Dazhi Dong, Yueming Yuan

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.

中文翻译:


基于RSM-MOPSO-GA算法的多目标优化及高性能多孔混凝土协同增强机制



多孔混凝土 (PC) 可以有效缓解与道路空间相关的各种环境问题。探索高性能多孔混凝土 (HPPC) 对于扩展其应用至关重要。然而,目前单一的改进方法几乎达到了瓶颈,尤其是在机械性能方面。因此,本研究从协同增强和多目标优化中寻求进一步的突破。通过单因素实验确定优化变量,随后使用响应面法 (RSM) 获得优化目标的最优解。为了解决 RSM 仅提供单个最优解的固有局限性,本文提出了一种新的 RSM-MOPSO-GA 混合优化算法。同时,通过微观结构分析阐明了协同增强机制。结果表明,玄武岩纤维 (BF)、纳米 SiO₂ (NS) 和水性环氧树脂 (WER) 的单独增强效果是有限的。然而,基于 RSM 的优化显著提高了 HPPC 的性能,抗压强度和弯曲强度分别提高了 51.4% 和 69.8%,渗透系数提高了 33.8%。此外,RSM-MOPSO-GA 算法的应用产生了一个稳定的帕累托前线,其中包含 50 个个体供用户决策。微尺度 WER 和 NS 之间的相互作用,结合 BF 在中尺度的增强,建立了协同增强机制。研究结果为 HPPC 的进一步应用提供了理论基础和实验依据。 此外,它还提供了一种新颖的解决方案,以应对混凝土性能中多目标优化的挑战。
更新日期:2024-12-16
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