{"title":"基于可解释机器学习的纳米二氧化硅改性混凝土多目标优化。","authors":"Yue Gu, Ruyan Fan, Yikun Li, Jiaqiang Zhao, Zijian Song, Hongqiang Chu","doi":"10.3390/nano15181423","DOIUrl":null,"url":null,"abstract":"<p><p>Nano-silica modified concrete (NSC) has been widely applied in engineering practice. However, conventional manual mix proportion design is both time-consuming and costly. In this study, four machine learning models-XGBoost, CatBoost, Random Forest, and AdaBoost-were trained to predict the compressive strength of NSC. Based on the best-performing model, the NSGA-II algorithm was employed to develop a multi-objective optimization framework, considering compressive strength, cost, and carbon emissions as objectives. The results indicated that XGBoost achieved the highest accuracy, with R<sup>2</sup> = 0.99 and RMSE = 1.80 MPa. Feature importance analysis further revealed that nano-silica content was strongly correlated with strength (0.82) and cost (0.85). Using NSGA-II, a set of Pareto-optimal solutions was generated. The NSGA-II algorithm produced Pareto-optimal solutions, highlighting the trade-offs among the three objectives. This integrated approach effectively reduces experimental workload and provides a valuable reference for sustainable NSC mix proportion design.</p>","PeriodicalId":18966,"journal":{"name":"Nanomaterials","volume":"15 18","pages":""},"PeriodicalIF":4.3000,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12472255/pdf/","citationCount":"0","resultStr":"{\"title\":\"Multi-Objective Optimization for Nano-Silica-Modified Concrete Based on Explainable Machine Learning.\",\"authors\":\"Yue Gu, Ruyan Fan, Yikun Li, Jiaqiang Zhao, Zijian Song, Hongqiang Chu\",\"doi\":\"10.3390/nano15181423\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Nano-silica modified concrete (NSC) has been widely applied in engineering practice. However, conventional manual mix proportion design is both time-consuming and costly. In this study, four machine learning models-XGBoost, CatBoost, Random Forest, and AdaBoost-were trained to predict the compressive strength of NSC. Based on the best-performing model, the NSGA-II algorithm was employed to develop a multi-objective optimization framework, considering compressive strength, cost, and carbon emissions as objectives. The results indicated that XGBoost achieved the highest accuracy, with R<sup>2</sup> = 0.99 and RMSE = 1.80 MPa. Feature importance analysis further revealed that nano-silica content was strongly correlated with strength (0.82) and cost (0.85). Using NSGA-II, a set of Pareto-optimal solutions was generated. The NSGA-II algorithm produced Pareto-optimal solutions, highlighting the trade-offs among the three objectives. This integrated approach effectively reduces experimental workload and provides a valuable reference for sustainable NSC mix proportion design.</p>\",\"PeriodicalId\":18966,\"journal\":{\"name\":\"Nanomaterials\",\"volume\":\"15 18\",\"pages\":\"\"},\"PeriodicalIF\":4.3000,\"publicationDate\":\"2025-09-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12472255/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Nanomaterials\",\"FirstCategoryId\":\"88\",\"ListUrlMain\":\"https://doi.org/10.3390/nano15181423\",\"RegionNum\":3,\"RegionCategory\":\"材料科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"CHEMISTRY, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Nanomaterials","FirstCategoryId":"88","ListUrlMain":"https://doi.org/10.3390/nano15181423","RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"CHEMISTRY, MULTIDISCIPLINARY","Score":null,"Total":0}
Multi-Objective Optimization for Nano-Silica-Modified Concrete Based on Explainable Machine Learning.
Nano-silica modified concrete (NSC) has been widely applied in engineering practice. However, conventional manual mix proportion design is both time-consuming and costly. In this study, four machine learning models-XGBoost, CatBoost, Random Forest, and AdaBoost-were trained to predict the compressive strength of NSC. Based on the best-performing model, the NSGA-II algorithm was employed to develop a multi-objective optimization framework, considering compressive strength, cost, and carbon emissions as objectives. The results indicated that XGBoost achieved the highest accuracy, with R2 = 0.99 and RMSE = 1.80 MPa. Feature importance analysis further revealed that nano-silica content was strongly correlated with strength (0.82) and cost (0.85). Using NSGA-II, a set of Pareto-optimal solutions was generated. The NSGA-II algorithm produced Pareto-optimal solutions, highlighting the trade-offs among the three objectives. This integrated approach effectively reduces experimental workload and provides a valuable reference for sustainable NSC mix proportion design.
期刊介绍：
Nanomaterials (ISSN 2076-4991) is an international and interdisciplinary scholarly open access journal. It publishes reviews, regular research papers, communications, and short notes that are relevant to any field of study that involves nanomaterials, with respect to their science and application. Thus, theoretical and experimental articles will be accepted, along with articles that deal with the synthesis and use of nanomaterials. Articles that synthesize information from multiple fields, and which place discoveries within a broader context, will be preferred. There is no restriction on the length of the papers. Our aim is to encourage scientists to publish their experimental and theoretical research in as much detail as possible. Full experimental or methodical details, or both, must be provided for research articles. Computed data or files regarding the full details of the experimental procedure, if unable to be published in a normal way, can be deposited as supplementary material. Nanomaterials is dedicated to a high scientific standard. All manuscripts undergo a rigorous reviewing process and decisions are based on the recommendations of independent reviewers.
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