ARTICLE
Predicting slump for high-performance concrete using decision tree and support vector regression approaches coupled with phasor particle swarm optimization algorithm
Qingmei Sun,
Yu Gongping,
Corresponding Author
Qingmei Sun
Zhejiang Guangsha Vocational and Technical University of construction, Dongyang, Zhejiang, China
Correspondence
Qingmei Sun, Zhejiang Guangsha Vocational and Technical University of construction, Dongyang, Zhejiang 322100, China.
Email: [email protected]
Search for more papers by this authorYu Gongping
Zhongtian Construction Group Co., Ltd, Dongyang, Zhejiang, China
Search for more papers by this authorQingmei Sun,
Yu Gongping,
Corresponding Author
Qingmei Sun
Zhejiang Guangsha Vocational and Technical University of construction, Dongyang, Zhejiang, China
Correspondence
Qingmei Sun, Zhejiang Guangsha Vocational and Technical University of construction, Dongyang, Zhejiang 322100, China.
Email: [email protected]
Search for more papers by this authorYu Gongping
Zhongtian Construction Group Co., Ltd, Dongyang, Zhejiang, China
Search for more papers by this authorAbstract
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.
Open Research
DATA AVAILABILITY STATEMENT
The data that support the findings of this study are available from the corresponding author upon reasonable request.
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