{"title":"利用元启发式优化技术增强机器学习模型，准确预测 FRP 加固混凝土板的 PSC","authors":"Nandini Urkude, M.S. Hora, Utkarsh","doi":"10.1080/15376494.2024.2394985","DOIUrl":null,"url":null,"abstract":"The present research examines the utilization of metaheuristic optimization algorithms to enhance the predictive accuracy of the Extreme Gradient Boosting (XGBoost) machine learning model in predic...","PeriodicalId":18249,"journal":{"name":"Mechanics of Advanced Materials and Structures","volume":"36 1","pages":""},"PeriodicalIF":3.6000,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Enhancing machine learning models with metaheuristic Optimization techniques for accurate prediction of PSC in FRP-reinforced concrete slabs\",\"authors\":\"Nandini Urkude, M.S. Hora, Utkarsh\",\"doi\":\"10.1080/15376494.2024.2394985\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The present research examines the utilization of metaheuristic optimization algorithms to enhance the predictive accuracy of the Extreme Gradient Boosting (XGBoost) machine learning model in predic...\",\"PeriodicalId\":18249,\"journal\":{\"name\":\"Mechanics of Advanced Materials and Structures\",\"volume\":\"36 1\",\"pages\":\"\"},\"PeriodicalIF\":3.6000,\"publicationDate\":\"2024-08-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Mechanics of Advanced Materials and Structures\",\"FirstCategoryId\":\"88\",\"ListUrlMain\":\"https://doi.org/10.1080/15376494.2024.2394985\",\"RegionNum\":3,\"RegionCategory\":\"材料科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"MATERIALS SCIENCE, CHARACTERIZATION & TESTING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Mechanics of Advanced Materials and Structures","FirstCategoryId":"88","ListUrlMain":"https://doi.org/10.1080/15376494.2024.2394985","RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MATERIALS SCIENCE, CHARACTERIZATION & TESTING","Score":null,"Total":0}
Enhancing machine learning models with metaheuristic Optimization techniques for accurate prediction of PSC in FRP-reinforced concrete slabs
The present research examines the utilization of metaheuristic optimization algorithms to enhance the predictive accuracy of the Extreme Gradient Boosting (XGBoost) machine learning model in predic...
期刊介绍：
The central aim of Mechanics of Advanced Materials and Structures ( MAMS) is to promote the dissemination of significant developments and publish state-of-the-art reviews and technical discussions of previously published papers dealing with mechanics aspects of advanced materials and structures. Refereed contributions describing analytical, numerical, and experimental methods and hybrid approaches that combine theoretical and experimental techniques in the study of advanced materials and structures are published, along with critical surveys of the literature and discussions of papers in the field.
Mechanics of layered structures, with layers of any materials (metallic, foams, piezoelectric, composites, ceramic, functionally graded, etc.) at various scales, milli/micro/nano-meter is of MAMS interest. Applications to structures subjected to mechanical, thermal, electrical, magnetical, hygrothermal, etc., as well as any coupled combinations of these all are of interests for MAMS. That is mechanics of multi-fields problems at various scale are of interest for MAMS, including fluid-strictures interactions.
Static and dynamic as well linear and nonlinear problems are of interest; advanced development of new materials with applications to automotive, civil, marine, aeronautical, space and bioengineering structures at various scales are of interest of this journal. Of special interest are methods and techniques for a better understanding of mechanics of metamaterials as well as the interaction with additive manufacturing technologies.
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