Prediction of Early Compressive Strength of Ultrahigh-Performance Concrete Using Machine Learning Methods-Reference-Cited by-同舟云学术

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

Published:2022-03-08 Issue: Volume: Page:
ISSN:0219-8762
Container-title:International Journal of Computational Methods
language:en
Short-container-title:Int. J. Comput. Methods

Author:

Zhu Hailiang¹²,Wu Xiong³⁴,Luo Yaoling⁴,Jia Yue¹,Wang Chong¹,Fang Zheng¹,Zhuang Xiaoying⁵⁶,Zhou Shuai¹

Affiliation:

1. College of Materials Science and Engineering, Chongqing University, Chongqing 400045, China

2. Shanghai Tunnel Engineering Company, Ltd., Shanghai 200082, China

3. School of Materials Science and Engineering, Wuhan University of Technology, Wuhan 430070, China

4. China West Construction Academy of Building Materials, Chengdu 610015, China

5. Computational Science and Simulation Technology, Institute of Photonics, Faculty of Mathematics and Physics, Leibniz Universität Hannover, Appelstr. 11A, 30167 Hannover, Germany

6. Department of Geotechnical Engineering, College of Civil Engineering, Tongji University, No. 1239, Si Ping Rd, Shanghai 200092, China

Abstract

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.

Funder

the National Natural Science Foundation of China

National Natural Science Foundation of China

Young Scientists Fund

State Key Laboratory of High Performance Civil Engineering Materials

Publisher

World Scientific Pub Co Pte Ltd

Subject

Computational Mathematics,Computer Science (miscellaneous)

Link

https://www.worldscientific.com/doi/pdf/10.1142/S0219876221410231

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