机器学习算法用于水泥强度预测的研究进展

doi:10.11896/cldb.23120133

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Abstract The strength of cement is one of the important indicators of its performance. The traditional strength detection method based on manual timed sampling has a good accuracy, but there is a large lag, which can not regulate the cement production process in time. Machine learning enables targeted correlation analysis of multi-dimensional production data in the cement industry, for example, raw material and product test data, microstructure images and process operating parameters. The lagging problem of manual testing methods can be solved by establishing a cement strength prediction model based on machine learning. This paper summarizes the cement strength prediction model based on machine learning by sorting out the basic working principles and advantages of commonly used algorithms, and discusses its application effect and development direction, with a view to providing guidance for further optimization of the cement strength prediction model and its application in the cement industry.

Key words： cement strength big data analysis machine learning prediction model

Published: 10 March 2025 Online: 2025-03-18

CLC number:	TP181
	TQ172

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	Articles by authors
	LI Ziqiang
	CUI Suping
	MA Zhongcheng
	WANG Yali
	WANG Jing
	LIU Yun
	QIAO Zhiyang

Cite this article:

LI Ziqiang,CUI Suping,MA Zhongcheng, et al. Research Progress of Machine Learning Algorithm for Cement Strength Prediction[J]. Materials Reports, 2025, 39(5): 23120133-12.

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https://www.mater-rep.com/EN/10.11896/cldb.23120133 OR https://www.mater-rep.com/EN/Y2025/V39/I5/23120133

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