硅酸盐学报

熟料等水泥基材料数据库及其应用

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许成文 ¹ , 叶家元 ¹ , 高霖 ² , 高国贤 ³ , 任雪红 ¹ , 夏凌风 ⁴ , 王琳 ⁵ , 张文生 ¹

硅酸盐学报 | 综合评述 2025,53(5): 1328-1338

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硅酸盐学报 |综合评述 2025 , 53 (5) : 1328 -1338

熟料等水泥基材料数据库及其应用

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许成文¹, 叶家元¹, 高霖², 高国贤³, 任雪红¹, 夏凌风⁴, 王琳⁵, 张文生¹

作者信息

^1．中国建筑材料科学研究总院有限公司，绿色建筑材料国家重点实验室，北京 100024

^2．合肥水泥研究设计院有限公司，水泥制造绿色低碳技术安徽省重点实验室，合肥 230051

^3．中国科学院过程工程研究所，介科学与工程全国重点实验室，北京 100190

^4．中存大数据科技有限公司，北京 100024

^5．济南大学，山东省网络环境智能计算技术重点实验室，济南 250022

通讯作者:

张文生（1969—），男，博士，教授。

作者简介:

许成文（1992—），男，博士。

Databases of Cementitious Materials Including Clinker and Their Applications

Chengwen XU¹, Jiayuan YE¹, Lin GAO², Guoxian GAO³, Xuehong REN¹, Lingfeng XIA⁴, Lin WANG⁵, Wensheng ZHANG¹

Affiliations

^1.State Key Laboratory of Green Building Materials, China Building Materials Academy, Beijing 100024, China

^2.Anhui Key Laboratory of Green and Low-carbon Technology in Cement Manufacturing, Hefei Cement Research & Design Institute Corporation Ltd., Hefei 230051, China

^3.State Key Laboratory of Mesoscience and Engineering, Institute of Process Engineering, Chinese Academy of Sciences, Beijing 100190, China

^4.ZhongCun Big Data Technology Co., Ltd, Beijing 100024, China

^5.Shandong Provincial Key Laboratory of Ubiquitous Intelligent Computing, Jinan University, Jinan 250022, China

Corresponding Author:

ZHANG Wensheng (1969-), male, Ph.D., Professor. E-mail: wensheng_zhang01@163.com

XU Chengwen (1992-), male, Ph.D. E-mail: isaac1964620@126.com

出版时间: 2025-05-20 doi: 10.14062/j.issn.0454-5648.20240594

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摘要

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在“双碳”政策管控与人工智能浪潮的推动下，水泥基材料亟需进行以低碳化为主要目标的数字化转型，而建立水泥基材料数据库是数字化转型的首要任务。在科学文献中存在大量数据，可采用人工智能方式进行自动化提取与应用。本文综述了熟料、水泥、混凝土、建筑砂浆等水泥基材料数据库的现状，归纳了现有数据库收录的数据类别、应用方式、应用范围等信息，分析了文献数据挖掘等人工智能技术在水泥基材料数据搜集与数据库建设中的应用前景，指出了现有数据库存在的数据底座不统一、数据项考虑不周全、部分类别材料数据库不完善等问题，并探讨了其未来发展方向。

关键词

水泥基材料 / 数据库 / 数据挖掘 / 人工智能

Abstract

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In light of the policy of 'carbon peaking and carbon neutrality' and the advent of artificial intelligence, there is an urgent need for research and development in the field of cementitious materials to advance low-carbon alternatives. Given the lengthy research periods associated with traditional experimental methods, digital research and development has recently emerged as a dominant trend in the field. Currently, digital models are being developed for purposes such as performance prediction, composition optimization, and low-carbon innovation, with a primary focus on algorithm optimization. However, the success of these models relies heavily on the availability of accurate, high-quality databases, which serve as the foundation for model implementation. Utilizing such databases can significantly simplify model construction and reduce the need for extensive optimization procedures. Consequently, building comprehensive databases for cementitious materials research and development has become a key objective in this field.

This paper reviews the current status of databases for cementitious materials, including those related to clinker, cement, concrete, mineral admixtures, and construction mortar. The types of data, application methods, and scope of application encompassed by these databases are summarized. Databases for clinker were among the earliest to be developed, primarily cataloging diverse mineral properties, including hydration products. They include crystallographic data, thermodynamic data, and force field data, with some databases integrating models for analyzing cement hydration. Cement databases, while recording thermodynamic properties, cement types, and characteristics, have largely been focused on production control in cement plants. Moreover, the construction of databases for concrete and related materials has often been driven by specific research objectives, such as studying chemical composition, strength, and durability. Some researchers have also conducted property predictions based on their collected datasets.

Despite the existence of numerous databases on cementitious materials, a significant volume of relevant data within scientific literature remains underutilized. The advancement of artificial intelligence in natural language processing has enabled the adaptation of data extraction algorithms across various domains, including metals, medicine, and biology. Named entity recognition and textual relationship extraction—two critical components of literature data mining—can be implemented through AI algorithms such as ChemDataExtractor and BERT. ChemDataExtractor and related algorithms have demonstrated accurate chemical data extraction from compounds and semi-supervised relationship extraction. Similarly, BERT, like ChatGPT, is a state-of-the-art language model developed using the Transformer architecture and has been successfully applied in automated text data extraction. However, the complex mineralogical and chemical composition, multi-scale particle characteristics, and hydration processes of cementitious materials pose challenges to the direct application of these algorithms.

Summary and prospects

The construction of comprehensive databases represents the cornerstone of the digital transformation of low-carbon cementitious material research and development. Although existing databases contain extensive datasets on cementitious materials, several challenges persist, such as non-standardized database structures, insufficiently considered data categories, and incomplete material coverage. To address these issues, future database development should prioritize unifying data formats and linking upstream and downstream processes to create a cohesive and interconnected database. Such a unified database would enable the establishment of a fully connected data chain for cementitious materials, enhancing the accuracy of predictions, supporting reverse design processes, and saving time on data cleaning.

Particular emphasis should also be placed on refining databases for related materials, such as mineral admixtures. These refined databases would provide critical data for improving the durability of cementitious materials and reducing their carbon emissions. Furthermore, combining artificial intelligence algorithms, such as ChemDataExtractor and BERT, with domain expert knowledge holds significant potential for advancing literature mining techniques tailored to cementitious materials. This process could begin by focusing on individual performance attributes and gradually expand to encompass the comprehensive extraction of data for all cementitious materials.

The future of cementitious materials database development is promising, with the potential to drive innovations in low-carbon materials research and development, ultimately contributing to achieving global carbon neutrality goals.

Key words

cement-based material / database / data mining / artificial intelligence

引用本文

许成文, 叶家元, 高霖, 高国贤, 任雪红, 夏凌风, 王琳, 张文生. 熟料等水泥基材料数据库及其应用. 硅酸盐学报, 2025 , 53 (5) : 1328 -1338 . DOI: 10.14062/j.issn.0454-5648.20240594

Chengwen XU, Jiayuan YE, Lin GAO, Guoxian GAO, Xuehong REN, Lingfeng XIA, Lin WANG, Wensheng ZHANG. Databases of Cementitious Materials Including Clinker and Their Applications[J]. Journal of the Chinese Ceramic Society, 2025 , 53 (5) : 1328 -1338 . DOI: 10.14062/j.issn.0454-5648.20240594

基金

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中国建材集团原创技术策源地“揭榜挂帅”任务-“水泥基材料数字化研发”项目(2021YCJS01)

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排序方式：

2025年第53卷第5期

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文章信息

doi: 10.14062/j.issn.0454-5648.20240594

接收时间：2024-09-13
首发时间：2026-10-22
出版时间：2025-05-20

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出版历史

收稿日期：2024-09-13
修回日期：2024-10-24

基金

中国建材集团原创技术策源地“揭榜挂帅”任务-“水泥基材料数字化研发”项目(2021YCJS01)

作者信息

^1．中国建筑材料科学研究总院有限公司，绿色建筑材料国家重点实验室，北京 100024

^2．合肥水泥研究设计院有限公司，水泥制造绿色低碳技术安徽省重点实验室，合肥 230051

^3．中国科学院过程工程研究所，介科学与工程全国重点实验室，北京 100190

^4．中存大数据科技有限公司，北京 100024

^5．济南大学，山东省网络环境智能计算技术重点实验室，济南 250022

通讯作者:

张文生（1969—），男，博士，教授。

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https://castjournals.cast.org.cn/joweb/gsyxb/CN/10.14062/j.issn.0454-5648.20240594

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2种不同金属材料的力学参数

科 Family	属数 Number of genus	种数 Number of species	占总种数比例 Percentage of total species (%)	属 Genus	种数 Number of species	占总种数比例 Percentage of total species (%)
鹅膏菌科Amanitaceae	2	11	5.26	鹅膏菌属 Amanita	10	4.78
小菇科 Mycenaceae	2	12	5.74	丝盖伞属 Inocybe	5	2.39
多孔菌科 Polyporaceae	8	14	6.70	蜡蘑属 Laccaria	5	2.39
红菇科 Russulaceae	3	23	11.00	小皮伞属 Marasmius	6	2.87
				小菇属 Mycena	11	5.26
				光柄菇属 Pluteus	5	2.39
				红菇属 Russula	17	8.13
				栓菌属 Trametes	5	2.39

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