{"title":"利用决策树和支持向量回归方法以及相位粒子群优化算法预测高性能混凝土的坍落度","authors":"Qingmei Sun, Yu Gongping","doi":"10.1002/suco.202300450","DOIUrl":null,"url":null,"abstract":"The main focus of this study is to assess the slump characteristics of high‐performance concrete (HPC) using decision tree (DT) and support vector regression (SVR) models. In the first step, the models were solely fed via HPC samples to reproduce the slump rates. By coupling phasor particle swarm optimization (PPSO) to main models, hybrid DT‐PPSO and SVR‐PPSO frameworks, simulate the slump rates accurately. Using the correlation of determination and root mean square error (MAE) metrics for the DT, 96.04 and 5.097 were computed, respectively. SVR was obtained at 92.62 and 6.965, alternatively. In the hybrid approach, DT‐PPSO could improve by 3% and 55% in terms of correlation of determination and root MAE, respectively. DT‐PPSO appeared high‐accuracy model compared to others; however, a single DT had more desirable results than SVR. Overall, the advantages of this study encompass its methodological approach, comparative insights, and practical relevance, offering valuable contributions to the understanding and prediction of mechanical slump in HPC.","PeriodicalId":21988,"journal":{"name":"Structural Concrete","volume":null,"pages":null},"PeriodicalIF":3.0000,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Predicting slump for high‐performance concrete using decision tree and support vector regression approaches coupled with phasor particle swarm optimization algorithm\",\"authors\":\"Qingmei Sun, Yu Gongping\",\"doi\":\"10.1002/suco.202300450\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The main focus of this study is to assess the slump characteristics of high‐performance concrete (HPC) using decision tree (DT) and support vector regression (SVR) models. In the first step, the models were solely fed via HPC samples to reproduce the slump rates. By coupling phasor particle swarm optimization (PPSO) to main models, hybrid DT‐PPSO and SVR‐PPSO frameworks, simulate the slump rates accurately. Using the correlation of determination and root mean square error (MAE) metrics for the DT, 96.04 and 5.097 were computed, respectively. SVR was obtained at 92.62 and 6.965, alternatively. In the hybrid approach, DT‐PPSO could improve by 3% and 55% in terms of correlation of determination and root MAE, respectively. DT‐PPSO appeared high‐accuracy model compared to others; however, a single DT had more desirable results than SVR. Overall, the advantages of this study encompass its methodological approach, comparative insights, and practical relevance, offering valuable contributions to the understanding and prediction of mechanical slump in HPC.\",\"PeriodicalId\":21988,\"journal\":{\"name\":\"Structural Concrete\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.0000,\"publicationDate\":\"2024-08-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Structural Concrete\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1002/suco.202300450\",\"RegionNum\":3,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"CONSTRUCTION & BUILDING TECHNOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Structural Concrete","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1002/suco.202300450","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"CONSTRUCTION & BUILDING TECHNOLOGY","Score":null,"Total":0}
Predicting slump for high‐performance concrete using decision tree and support vector regression approaches coupled with phasor particle swarm optimization algorithm
The main focus of this study is to assess the slump characteristics of high‐performance concrete (HPC) using decision tree (DT) and support vector regression (SVR) models. In the first step, the models were solely fed via HPC samples to reproduce the slump rates. By coupling phasor particle swarm optimization (PPSO) to main models, hybrid DT‐PPSO and SVR‐PPSO frameworks, simulate the slump rates accurately. Using the correlation of determination and root mean square error (MAE) metrics for the DT, 96.04 and 5.097 were computed, respectively. SVR was obtained at 92.62 and 6.965, alternatively. In the hybrid approach, DT‐PPSO could improve by 3% and 55% in terms of correlation of determination and root MAE, respectively. DT‐PPSO appeared high‐accuracy model compared to others; however, a single DT had more desirable results than SVR. Overall, the advantages of this study encompass its methodological approach, comparative insights, and practical relevance, offering valuable contributions to the understanding and prediction of mechanical slump in HPC.
期刊介绍:
Structural Concrete, the official journal of the fib, provides conceptual and procedural guidance in the field of concrete construction, and features peer-reviewed papers, keynote research and industry news covering all aspects of the design, construction, performance in service and demolition of concrete structures.
Main topics:
design, construction, performance in service, conservation (assessment, maintenance, strengthening) and demolition of concrete structures
research about the behaviour of concrete structures
development of design methods
fib Model Code
sustainability of concrete structures.