硅酸盐学报

数据驱动范式下化学激发硅铝质胶凝材料的研究进展

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王董雨 ¹ , 张祖华 ¹ , 施成 ¹ , 刘小青 ² , 蒋正武 ¹

硅酸盐学报 | 综合评述 2026,54(8): 2702-2714

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硅酸盐学报 |综合评述 2026 , 54 (8) : 2702 -2714

数据驱动范式下化学激发硅铝质胶凝材料的研究进展

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王董雨¹, 张祖华¹, 施成¹, 刘小青², 蒋正武¹

作者信息

^1．同济大学材料科学与工程学院，先进土木工程材料教育部重点实验室，上海 201804

^2．中国科学院宁波材料技术与工程研究所，海洋关键材料全国重点实验室，浙江宁波 315201

通讯作者:

张祖华（1983—），男，博士，教授。

作者简介:

王董雨（1997—），女，博士研究生。

Data-Driven Research on Chemico-Activated Aluminosilicate Cementitious Materials

Dongyu WANG¹, Zuhua ZHANG¹, Cheng SHI¹, Xiaoqing LIU², Zhengwu JIANG¹

Affiliations

^1.Key Laboratory of Advanced Civil Engineering Materials of Ministry of Education, School of Materials Science and Engineering, Tongji University, Shanghai 201804, China

^2.State Key Laboratory of Advanced Marine Materials, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, Zhejiang, China

Corresponding Author:

{ZHANG} Zuhua (1983-), male, Ph.D., Professor. E-mail: zhangzuhua@tongji.edu.cn

WANG Dongyu(1997-), female, Doctoral candidate. E-mail: wangdy@tongji.edu.cn

出版时间: 2026-07-16 doi: 10.14062/j.issn.0454-5648.20260108

文章导航

摘要

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以地质聚合物为代表的化学激发硅铝质胶凝材料，制备过程利用了化学激发活性工业固废工艺，能耗低、碳排少，产物力学性能与耐久性能优异，被视为最具发展潜力的绿色胶凝材料之一。然而，其原料组成复杂、聚合反应机制不明，要满足多目标性能调控，传统试错法所需实验量巨大。数据驱动范式依托人工智能与数据科学技术手段，通过建立高维数据与材料性能之间的映射关系，有效克服了传统实验方法高成本、长周期等局限，正引领材料研发从实验驱动向数据驱动演进。本文综述了数据驱动范式在地质聚合物早期性能、力学性能与耐久性能预测中的应用现状，介绍了元启发式算法驱动组成优化设计的最新研究进展，总结、展望了数据驱动下化学激发硅铝质胶凝材料应用与发展所面临的关键挑战和未来研究重点。

关键词

数据驱动 / 化学激发硅铝质胶凝材料 / 地质聚合物 / 性能预测 / 组成设计

Abstract

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In the context of global low-carbon and carbon-neutrality targets, chemico-activated aluminosilicate cementitious materials that are represented by geopolymers become one of the most promising green alternatives to ordinary Portland cement. Geopolymers are synthesized via the chemico-activation of reactive aluminosilicate solid wastes with an alkaline activator. Their manufacturing process is featured by low energy consumption and reduced carbon emissions through the valorization of industrial solid waste or low-energy natural mineral precursors. Furthermore, the reaction products are endowed with high early-age mechanical strength, excellent durability and fire resistance. However, the complexity of raw material composition, unclear polymerization mechanisms and intricate interdependencies among multiple variables result in low efficiency, poor reproducibility and accuracy of conventional trial-and-error, seriously restricting the multi-objective performance optimization and large-scale application of geopolymers.

Based on the materials initiative and rapid development of artificial intelligence, the data-driven paradigm initiates a fundamental shift in materials science research. Establishing the relationships between high-dimensional data (e.g., materials compositions, structure, physical and chemical properties) and the material performance through learning massive multi-source data can effectively address the limitations of the conventional methods, i.e., high cost, long time and numerous resources. It therefore provides intelligent and accelerated routes to revolutionize the design and investigation of chemically activated aluminosilicate cementitious materials, leading a paradigm shift from experimental-driven to data-driven approach. This review represents research progress on data-driven approaches in geopolymer, with emphasis on three aspects, i.e., 1) machine learning methodologies for performance prediction; 2) application of machine learning in targeted performance prediction; and 3) metaheuristic algorithms-based composition optimization.

This review proposes a complete data-driven workflow for geopolymer research, including target definition, dataset collection, data processing, feature engineering, model construction and application. Based on the application scenarios, the existing models can be divided into four categories, i.e., neural networks, ensemble learning, traditional regression and statistical learning, and symbolic regression combined with neuro-fuzzy systems. Each category offers distinct advantages and disadvantages. Neural networks capture nonlinear relationships, but require large dataset and lack interpretability. Ensemble learning handles high-dimensional mixed data with robustness, but risks overfitting with small samples. Traditional regression combined with neuro-fuzzy systems works for small to medium dataset, but faces an exponential rule growth under high-dimensional inputs. In terms of targeted performance prediction, machine learning is effectively applied to early-age properties, mechanical properties, and durability. For early-age behaviors, machine learning models can accurately predict geopolymerization heat flow, peak time, setting time and slump. For mechanical properties, compressive strength prediction is the most mature predicted performance, and the prediction is extended to flexural strength, splitting tensile strength, elastic modulus, etc.. For durability, the initial research mainly focuses on drying shrinkage, autogenous shrinkage, carbonation, while some studies on chloride penetration, sulfate attack and long-term durability are relatively scarce.

Metaheuristic algorithms play a significant role on geopolymer composition design. Firstly, metaheuristic algorithms serve to optimize hyperparameters and improve prediction accuracy. Subsequently, the well-trained predictive models can be leveraged to facilitate both forward guidance and inverse mix design. Based on the trained model, the importance of material composition, preparation parameters, and curing conditions to prediction accuracy can be quantitatively assessed. Furthermore, metaheuristic algorithms can also apply to reverse geopolymer mixture design targeting single or multiple objectives. For instance, a Pareto frontier is constructed via leveraging NSGA-Ⅱ to balance high strengths, low carbon and low cost, and the optimal solution can be further selected via TOPSIS. This approach pioneers an effective pathway for the inverse design of geopolymers and establishes a versatile decision-support tool that is poised to become a new standard for the accelerated development of high-performance and eco-efficient cementitious materials.

Summary and prospects

Despite progress on applying data-driven methods to chemically activated aluminosilicate cementitious materials, several critical challenges remain. Firstly, the inherent complexity and variability of raw materials, combined with the absence of standardized design specifications, lead to high-dimensional and redundant datasets that compromise the model performance. To address this issue, it is necessary to establish standardized open databases covering precursor composition, reactivity, activator parameters, and curing conditions, as well as the knowledge-embedded hybrid models that incorporate geopolymerization mechanisms and thermodynamic constraints. Secondly, data scarcity is a pervasive challenge in this field, with most datasets characterized by small sample sizes, severely limiting model generalizability. This issue can be alleviated through multi-source data fusion, data augmentation techniques, and transfer learning for small-sample scenarios. Lastly, the existing inverse design relies primarily on a two-step prediction-then-optimization framework, whereas deep generative models for direct material design remain largely underexplored. Integrating these advanced models with reaction kinetics and microstructure evolution can be essential to realize end-to-end inverse design.

The evolution of artificial intelligence technology offers a significant potential for advancing chemico-activated aluminosilicate cementitious materials technology. This promises to serve as a robust reference for the deep integration of artificial intelligence into future material development. Future efforts should focus on developing standardized workflows, improving model interpretability and generalizability, as well as leveraging multi-scale simulations and deep generative models to accelerate material innovation. Data-driven methods will enable more accurate prediction and design of chemico-activated aluminosilicate materials due to these advancements, thereby contributing to the sustainable transformation of the construction industry.

Key words

data-driven / chemico-activated aluminosilicate cementitious materials / geopolymer / performance prediction / mixture design

引用本文

王董雨, 张祖华, 施成, 刘小青, 蒋正武. 数据驱动范式下化学激发硅铝质胶凝材料的研究进展. 硅酸盐学报, 2026 , 54 (8) : 2702 -2714 . DOI: 10.14062/j.issn.0454-5648.20260108

Dongyu WANG, Zuhua ZHANG, Cheng SHI, Xiaoqing LIU, Zhengwu JIANG. Data-Driven Research on Chemico-Activated Aluminosilicate Cementitious Materials[J]. Journal of the Chinese Ceramic Society, 2026 , 54 (8) : 2702 -2714 . DOI: 10.14062/j.issn.0454-5648.20260108

基金

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国家自然科学基金(52378257)
中央高校基本科研业务费专项资金资助(22120250415)
中国科学院宁波材料技术与工程研究所海洋关键材料重点实验室开放基金(2024K08)
上海百奥恒新材料有限公司地聚合物基因组项目

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2026年第54卷第8期

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文章信息

doi: 10.14062/j.issn.0454-5648.20260108

接收时间：2026-02-28
首发时间：2026-10-22
出版时间：2026-07-16

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收稿日期：2026-02-28
修回日期：2026-03-19

基金

国家自然科学基金(52378257)

中央高校基本科研业务费专项资金资助(22120250415)

中国科学院宁波材料技术与工程研究所海洋关键材料重点实验室开放基金(2024K08)

上海百奥恒新材料有限公司地聚合物基因组项目

作者信息

^1．同济大学材料科学与工程学院，先进土木工程材料教育部重点实验室，上海 201804

^2．中国科学院宁波材料技术与工程研究所，海洋关键材料全国重点实验室，浙江宁波 315201

通讯作者:

张祖华（1983—），男，博士，教授。

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https://castjournals.cast.org.cn/joweb/gsyxb/CN/10.14062/j.issn.0454-5648.20260108

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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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