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Cement-based grouting material development and prediction of material properties using PSO-RBF machine learning

文献类型：期刊论文

作者Liu, Xuewei3; Wang, Sai1,3; Liu, Bin3; Liu, Quansheng2; Zhou, Yuan3; Chen, Juxiang1,3; Luo, Jin1
刊名CONSTRUCTION AND BUILDING MATERIALS
出版日期2024-02-23
卷号417页码:18
关键词Grouting material Machine learning Cement Mechanical property Prediction model
ISSN号0950-0618
DOI10.1016/j.conbuildmat.2024.135328
英文摘要Grouting technique is one of the main methods to improve mechanical properties of fractured rock. The study of grouting material is essential for improving the effect of grouting. To develop a new low water -cement ratio cement -based grouting material, the influence of additives on cement strength and Pearson correlation analysis method was adopted to obtain main material compositions. Then, the coupled particle swarm optimization algorithm and radial basis function (PSO-RBF) model was established for material properties prediction with proportion as input. The prediction results show that the proposed PSO-RBF model has a higher accuracy compared to the RF, BP, and RBF models. Furthermore, combined the results of PSO-RBF with entropy weight method, the optimal proportion of grouting material was developed. The results of mechanical properties indicated that this proposed cement -based material has the characteristics of reducing water -cement ratio and porosity and increasing strength, fluidity, and contact angle. The proposed material proportion intelligent optimization approach and grouting material can provide a reference for material design and engineering application.
资助项目National Natural Science Foundation of China[U22A20234] ; National Natural Science Foundation of China[42277170] ; Hubei Province Key Research and Development Project[2023BCB121]
WOS研究方向Construction & Building Technology ; Engineering ; Materials Science
语种英语
WOS记录号WOS:001183498500001
出版者ELSEVIER SCI LTD
源URL[http://119.78.100.198/handle/2S6PX9GI/40786]  
专题中科院武汉岩土力学所
通讯作者Liu, Bin
作者单位1.China Univ Geosci, Fac Engn, Wuhan 430074, Peoples R China
2.Wuhan Univ, Sch Civil Engn, Key Lab Safety Geotech & Struct Engn Hubei Prov, Wuhan 430072, Peoples R China
3.Chinese Acad Sci, Inst Rock & Soil Mech, State Key Lab Geomech & Geotech Engn, Wuhan 430071, Peoples R China
推荐引用方式
GB/T 7714		 Liu, Xuewei,Wang, Sai,Liu, Bin,et al. Cement-based grouting material development and prediction of material properties using PSO-RBF machine learning[J]. CONSTRUCTION AND BUILDING MATERIALS,2024,417:18.
APA Liu, Xuewei.,Wang, Sai.,Liu, Bin.,Liu, Quansheng.,Zhou, Yuan.,...&Luo, Jin.(2024).Cement-based grouting material development and prediction of material properties using PSO-RBF machine learning.CONSTRUCTION AND BUILDING MATERIALS,417,18.
MLA Liu, Xuewei,et al."Cement-based grouting material development and prediction of material properties using PSO-RBF machine learning".CONSTRUCTION AND BUILDING MATERIALS 417(2024):18.

入库方式： OAI收割

来源：武汉岩土力学研究所

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