Prediction of Early Compressive Strength of Ultrahigh-Performance Concrete Using Machine Learning Methods

被引：13

作者：

Zhu, Hailiang ^{[1
,2
]}

Wu, Xiong ^{[3
,4
]}

Luo, Yaoling ^{[4
]}

Jia, Yue ^{[1
]}

Wang, Chong ^{[1
]}

Fang, Zheng ^{[1
]}

Zhuang, Xiaoying ^{[5
,6
]}

Zhou, Shuai ^{[1
]}

机构：

[1] Chongqing Univ, Coll Mat Sci & Engn, Chongqing 400045, Peoples R China

[2] Wuhan Univ Technol, Sch Mat Sci & Engn, Wuhan 430070, Peoples R China

[3] China West Construct Acad Bldg Mat, Chengdu 610015, Peoples R China

[4] Shanghai Tunnel Engn Co Ltd, Shanghai 200082, Peoples R China

[5] Leibniz Univ Hannover, Computat Sci & Simulat Technol, Inst Photon, Fac Math & Phys, Appelstr 11A, D-30167 Hannover, Germany

[6] Tongji Univ, Dept Geotech Engn, Coll Civil Engn, 1239 Si Ping Rd, Shanghai 200092, Peoples R China

来源：

INTERNATIONAL JOURNAL OF COMPUTATIONAL METHODS | 2023年 / 20卷 / 08期

基金：

中国国家自然科学基金;

关键词：

UHPC; prediction; ANN; SVM; 7-day compressive strength; FIBER-REINFORCED CONCRETE; ARTIFICIAL NEURAL-NETWORKS; MECHANICAL-PROPERTIES; MESHFREE METHOD; MIX DESIGN; MODEL; AGGREGATE; HYDRATION; AGE; MICROCAPSULES;

D O I：

10.1142/S0219876221410231

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

In this study, a new prediction model is proposed to predict the 7-day compressive strength of ultrahigh-performance concrete (UHPC) with different mix proportions using artificial neural network (ANN) and support vector machine (SVM). The predicted results are compared with the experimental results to verify the proposed model. Then, the importance of each component and the sensitivity of parameters are investigated. The research proves that the proposed model can estimate the 7-day compressive strength of UHPC based on the mix proportions.

引用

页数：23

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