Research Progress of Machine Learning Algorithm for Cement Strength Prediction
LI Ziqiang1, CUI Suping1,*, MA Zhongcheng2,3, WANG Yali1, WANG Jing2, LIU Yun2, QIAO Zhiyang1
1 College of Materials Science and Engineering, Beijing University of Technology, Beijing 100124, China 2 China Building Materials Academy Co., Ltd., Beijing 100024, China 3 China National Building Materials Group Corporation, Beijing 100036, China
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.
李自强, 崔素萍, 马忠诚, 王亚丽, 王晶, 刘云, 乔志杨. 机器学习算法用于水泥强度预测的研究进展[J]. 材料导报, 2025, 39(5): 23120133-12.
LI Ziqiang, CUI Suping, MA Zhongcheng, WANG Yali, WANG Jing, LIU Yun, QIAO Zhiyang. Research Progress of Machine Learning Algorithm for Cement Strength Prediction. Materials Reports, 2025, 39(5): 23120133-12.
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