基于机器学习回归模型的三峡大坝混凝土强度预测

doi:10.11896/cldb.22010068

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Abstract Artificial neural network, decision tree and support vector machine are commonly used machine learning algorithms for concrete strength prediction. For learning and applying of the experience about the raw material selection and mixture design of Three Gorges Dam mass concrete, and providing guidance for dam maintenance and construction of other water conservancy projects, this study established a relationship between material properties, mixture design and concrete strength based on the 28-day compressive strength data of concrete used in the main project of Three Gorges Dam. Combined with random forest characteristic weight ranking and statistical analysis, cement dosage, concrete temperature and water-to-cement ratio were found to be the three key characteristic parameters dominating the compressive strength of the Three Gorges Dam concrete. Meanwhile, the prediction efficiency of the commonly used machine learning algorithms on 28-day concrete strength of the Three Gorges Dam were discussed. According to the prediction results of the three different kinds of machine learning algorithms after comprehensive optimization of the fixed characteristic parameters, general parameters and hyper-parameters, the Epsilon-support vector regression (SVR) algorithm with systematic comprehensive optimization was found to be the best in prediction.

Key words： concrete Three Gorges project compressive strength machine learning model optimization

Published: Online: 2023-02-08

CLC number:	TP181
	TP183
	TU502+.6
	TU528.01

Fund:Joint Program of National Natural Science Foundation of China (U2040222) and Open Foundation Program of State Key Laboratory of High Performance Civil Engineering Materials (2020CEM011).

Corresponding Authors: liujiaping@cnjsjk.cn

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	XU Xiaohang
	HU Zhangli
	LIU Jiaping
	LI Wenwei
	LIU Jianzhong

Cite this article:

XU Xiaohang,HU Zhangli,LIU Jiaping, et al. Concrete Strength Prediction of the Three Gorges Dam Based on Machine Learning Regression Model[J]. Materials Reports, 2023, 37(2): 22010068-9.

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http://www.mater-rep.com/EN/10.11896/cldb.22010068 OR http://www.mater-rep.com/EN/Y2023/V37/I2/22010068

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