水工沥青混凝土面板配合比多目标智能优化设计方法

IF 5.9 2区 工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY
Hanye Xiong , Zhenzhong Shen , Yiqing Sun , Yaxin Feng , Hongwei Zhang
{"title":"水工沥青混凝土面板配合比多目标智能优化设计方法","authors":"Hanye Xiong ,&nbsp;Zhenzhong Shen ,&nbsp;Yiqing Sun ,&nbsp;Yaxin Feng ,&nbsp;Hongwei Zhang","doi":"10.1016/j.asej.2025.103781","DOIUrl":null,"url":null,"abstract":"<div><div>Hydraulic asphalt concrete facing serves as a critical impervious structure, ensuring the long-term and stable operation of the upper reservoir in pumped-storage power station. However, conventional mix design theories still rely heavily on empirical criteria focused on optimizing single material properties, which fall short of meeting the increasingly multi-dimensional demands of modern engineering design. To facilitate the intelligent and efficient construction of impervious facings, this research proposes a multi-objective mix design approach that integrates statistical experimental optimization with metaheuristic search techniques, considering material performance, economic cost, and environmental sustainability. First, response surface models were established between mix parameters and material properties using the central composite design (CCD) method. The significance and predictive accuracy of the models were validated through analysis of variance (ANOVA) and residual analysis. Subsequently, a set of material performance constraints was formulated based on engineering requirements, and a multi-objective optimization model was constructed with economic cost and carbon emissions as dual objectives. The Non-dominated sorting whale optimization algorithm (NSWOA) was then employed to derive the Pareto optimal solutions iteratively. Finally, the technique for order preference by similarity to ideal solution (TOPSIS) was applied to perform decision analysis on the Pareto optimal front, enabling the selection of optimal compromise mix proportions under different preferences. Specifically, the optimal trade-off solution between resource-saving and environmentally-friendly corresponds to an asphalt content of 6.705%, a filler content of 12.680%, and a gradation index of 0.264. The results provide a valuable theoretical reference for the efficient and sustainable design of asphalt concrete facings.</div></div>","PeriodicalId":48648,"journal":{"name":"Ain Shams Engineering Journal","volume":"16 12","pages":"Article 103781"},"PeriodicalIF":5.9000,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Multi-objective intelligent optimization design method for mix proportions of hydraulic asphalt concrete facings\",\"authors\":\"Hanye Xiong ,&nbsp;Zhenzhong Shen ,&nbsp;Yiqing Sun ,&nbsp;Yaxin Feng ,&nbsp;Hongwei Zhang\",\"doi\":\"10.1016/j.asej.2025.103781\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Hydraulic asphalt concrete facing serves as a critical impervious structure, ensuring the long-term and stable operation of the upper reservoir in pumped-storage power station. However, conventional mix design theories still rely heavily on empirical criteria focused on optimizing single material properties, which fall short of meeting the increasingly multi-dimensional demands of modern engineering design. To facilitate the intelligent and efficient construction of impervious facings, this research proposes a multi-objective mix design approach that integrates statistical experimental optimization with metaheuristic search techniques, considering material performance, economic cost, and environmental sustainability. First, response surface models were established between mix parameters and material properties using the central composite design (CCD) method. The significance and predictive accuracy of the models were validated through analysis of variance (ANOVA) and residual analysis. Subsequently, a set of material performance constraints was formulated based on engineering requirements, and a multi-objective optimization model was constructed with economic cost and carbon emissions as dual objectives. The Non-dominated sorting whale optimization algorithm (NSWOA) was then employed to derive the Pareto optimal solutions iteratively. Finally, the technique for order preference by similarity to ideal solution (TOPSIS) was applied to perform decision analysis on the Pareto optimal front, enabling the selection of optimal compromise mix proportions under different preferences. Specifically, the optimal trade-off solution between resource-saving and environmentally-friendly corresponds to an asphalt content of 6.705%, a filler content of 12.680%, and a gradation index of 0.264. The results provide a valuable theoretical reference for the efficient and sustainable design of asphalt concrete facings.</div></div>\",\"PeriodicalId\":48648,\"journal\":{\"name\":\"Ain Shams Engineering Journal\",\"volume\":\"16 12\",\"pages\":\"Article 103781\"},\"PeriodicalIF\":5.9000,\"publicationDate\":\"2025-09-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Ain Shams Engineering Journal\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2090447925005222\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Ain Shams Engineering Journal","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2090447925005222","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0

摘要

水力沥青混凝土面板是抽水蓄能电站上部水库长期稳定运行的重要防渗结构。然而,传统的配合比设计理论仍然严重依赖于以优化单一材料性能为重点的经验准则,无法满足现代工程设计日益多维化的需求。为了促进防渗面层的智能高效施工,本研究提出了一种多目标混合设计方法,将统计实验优化与元启发式搜索技术相结合,考虑材料性能、经济成本和环境可持续性。首先,采用中心复合设计(CCD)方法建立了混合参数与材料性能之间的响应面模型;通过方差分析(ANOVA)和残差分析验证模型的显著性和预测准确性。随后,根据工程需求制定了一套材料性能约束条件,构建了以经济成本和碳排放为双目标的多目标优化模型。采用非支配排序鲸优化算法(NSWOA)迭代求解Pareto最优解。最后,运用TOPSIS (order preference by similarity to ideal solution)方法对Pareto最优前沿进行决策分析,实现了不同偏好下的最优妥协组合比例的选择。其中,资源节约型与环境友好型的最优权衡方案对应的沥青掺量为6.705%,填料掺量为12.680%,级配指数为0.264。研究结果为沥青路面的高效可持续设计提供了有价值的理论参考。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multi-objective intelligent optimization design method for mix proportions of hydraulic asphalt concrete facings
Hydraulic asphalt concrete facing serves as a critical impervious structure, ensuring the long-term and stable operation of the upper reservoir in pumped-storage power station. However, conventional mix design theories still rely heavily on empirical criteria focused on optimizing single material properties, which fall short of meeting the increasingly multi-dimensional demands of modern engineering design. To facilitate the intelligent and efficient construction of impervious facings, this research proposes a multi-objective mix design approach that integrates statistical experimental optimization with metaheuristic search techniques, considering material performance, economic cost, and environmental sustainability. First, response surface models were established between mix parameters and material properties using the central composite design (CCD) method. The significance and predictive accuracy of the models were validated through analysis of variance (ANOVA) and residual analysis. Subsequently, a set of material performance constraints was formulated based on engineering requirements, and a multi-objective optimization model was constructed with economic cost and carbon emissions as dual objectives. The Non-dominated sorting whale optimization algorithm (NSWOA) was then employed to derive the Pareto optimal solutions iteratively. Finally, the technique for order preference by similarity to ideal solution (TOPSIS) was applied to perform decision analysis on the Pareto optimal front, enabling the selection of optimal compromise mix proportions under different preferences. Specifically, the optimal trade-off solution between resource-saving and environmentally-friendly corresponds to an asphalt content of 6.705%, a filler content of 12.680%, and a gradation index of 0.264. The results provide a valuable theoretical reference for the efficient and sustainable design of asphalt concrete facings.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Ain Shams Engineering Journal
Ain Shams Engineering Journal Engineering-General Engineering
CiteScore
10.80
自引率
13.30%
发文量
441
审稿时长
49 weeks
期刊介绍: in Shams Engineering Journal is an international journal devoted to publication of peer reviewed original high-quality research papers and review papers in both traditional topics and those of emerging science and technology. Areas of both theoretical and fundamental interest as well as those concerning industrial applications, emerging instrumental techniques and those which have some practical application to an aspect of human endeavor, such as the preservation of the environment, health, waste disposal are welcome. The overall focus is on original and rigorous scientific research results which have generic significance. Ain Shams Engineering Journal focuses upon aspects of mechanical engineering, electrical engineering, civil engineering, chemical engineering, petroleum engineering, environmental engineering, architectural and urban planning engineering. Papers in which knowledge from other disciplines is integrated with engineering are especially welcome like nanotechnology, material sciences, and computational methods as well as applied basic sciences: engineering mathematics, physics and chemistry.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信