硅酸盐学报

AI赋能的混凝土材料基因数据库智能设计及应用

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刘楚瑶 ¹ , 罗梓轩 ¹ , 杨柳 ¹ , 胡金其 ¹ , 刘赞群 ² , 张增起 ³ , 杨文萃 ⁴ , 徐慧宁 ⁴ , 陈梦妮 ¹

硅酸盐学报 | 水泥基材料基因与智能设计专题—研究论文 2026,54(3): 909-921

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硅酸盐学报 |水泥基材料基因与智能设计专题—研究论文 2026 , 54 (3) : 909 -921

AI赋能的混凝土材料基因数据库智能设计及应用

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刘楚瑶¹, 罗梓轩¹, 杨柳¹, 胡金其¹, 刘赞群², 张增起³, 杨文萃⁴, 徐慧宁⁴, 陈梦妮¹

作者信息

^1．中南大学计算机学院，长沙 410075

^2．中南大学土木工程学院，长沙 410075

^3．北京科技大学冶金与生态工程学院，北京 100083

^4．哈尔滨工业大学交通科学与工程学院，哈尔滨 150090

通讯作者:

杨柳（1979—），女，博士，教授

刘赞群（1975—），男，博士，教授

杨文萃（1981—），女，博士，副教授

张增起（1991—），男，博士，教授。

作者简介:

刘楚瑶（2002—），女，硕士研究生。

AI-Powered Intelligent Design of Concrete Material Gene Database

Chuyao LIU¹, Zixuan LUO¹, Liu YANG¹, Jinqi HU¹, Zanqun LIU², Zengqi ZHANG³, Wencui YANG⁴, Huining XU⁴, Mengni CHEN¹

Affiliations

^1.School of Computer Science, Central South University, Changsha 410075, China

^2.School of Civil Engineering, Central South University, Changsha 410075, China

^3.School of Metallurgical and Ecological Engineering, University of Science and Technology Beijing, Beijing 100083, China

^4.School of Transportation Science and Engineering, Harbin Institute of Technology, Harbin 150090, China

Corresponding Author:

YANG Liu (1979-), female, Ph.D., Professor. E-mail: yangliu@csu.edu.cn

LIU Zanqun (1975-), male, Ph.D., Professor. E-mail: zanqun.liu@csu.edu.cn

YANG Wencui (1981-), female, Ph.D., Associate Professor. E-mail: yangwencui@hit.edu.cn

ZHANG Zengqi (1991-), male, Ph.D., Professor. E-mail: zhangzq@ustb.edu.cn

LIU Chuyao (2002-), female, Master candidate. E-mail: 244711047@csu.edu.cn

出版时间: 2026-03-20 doi: 10.14062/j.issn.0454-5648.20250728

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摘要

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针对混凝土数据多源、异构、多尺度的特点，本工作面向材料数据管理、基因智能分析到材料智能研发的典型任务，建立一套AI赋能的混凝土材料基因数据库智能设计框架，框架以标准化的混凝土材料基因关系型数据库与图数据库为数据存储基础，设计分层密度聚类算法提取对混凝土材料典型性能产生重要影响的基因组结构。利用机器学习正向设计混凝土材料典型性能预测方法，R²相较其他机器学习模型提升约4%，实现了从特征选择、模型预测到贡献量化的可解释性推理路径，设计融合ACO与NSGA-Ⅱ的、以性能目标驱动的混凝土材料配比逆向优化算法，突破传统配比经验依赖与单目标优化局限，通过全局搜索避免陷入局部最优解。系统化智能化框架旨在突破传统数据库在材料计算与智能分析方面的瓶颈，可为材料基因智能化研究提供借鉴。

关键词

混凝土 / 数据库智能设计 / 材料基因 / 材料性能 / 人工智能

Abstract

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Introduction

Concrete as the second-most consumed material globally after water plays a vital role in modern construction and infrastructure development due to its superior mechanical properties, longevity, and versatility. Conventional methodologies for concrete design predominantly rely on empirical approaches, often resulting in extended cycles for material development and suboptimal performance outcomes in practical applications. The advent of the Materials Genome Initiative (MGI), launched in the United States in 2011, highlights a need for data-driven approaches to material design. Concurrently, significant advancements in Artificial Intelligence (AI) have new avenues for enhancing material discovery and characterization. It is thus necessary to establish a robust and comprehensive database capable of accommodating the extensive, heterogeneous, and multi-scale data associated with various concrete properties and performance metrics. This study was to develop an innovative AI-powered intelligent design framework specifically tailored for a concrete material performance gene database. This framework could promise efficient data management and intelligent analytical capabilities and facilitate advanced material development and optimization.

Methods

This framework system integrated the information on concrete material composition, mix parameters, environmental factors, etc., and extracted the topological structure and coupling relationships between gene features to establish a concrete material knowledge graph based on conventional relational databases, realizing the visualization of gene feature relationships. This work designed a "gene importance-driven hierarchical density clustering" algorithm for quantitative analysis of concrete material gene features, extracting gene structure with different action relationships, via addressing the complex influencing factors and multi-scale coupling characteristics of concrete material performance. On this basis, this work also designed a closed-loop reasoning path from feature selection, model prediction, and contribution quantification for typical concrete material performance to reduce the cycle of traditional test methods, lower experimental costs, and achieve rapid evaluation and iterative optimization of concrete performance. This could provide a scientific theoretical basis for material selection via the performance prediction model as a core model and combining with multi-objective optimization algorithms. The framework could give a systematic technical framework and theoretical support for intelligent material research via implementing the whole-process design from concrete material data management to intelligent development, promoting the deep integration and innovative development of material science research and civil engineering decision-making.

Results and Discussion

The implementation of the AI-enabled framework demonstrates substantial enhancements in the accuracy and reliability of concrete performance predictions. The algorithms effectively uncover intricate and complex relationships between various material constituents, and their resultant influence on the critical performance indicators, such as compressive strength, durability, and workability. The unique integration of multi-source data within the database facilitates enhances feature extraction and visual representation of critical metrics, effectively addressing the intricate coupling effects that often complicate conventional testing methodologies. Through rigorous testing, validation, and benchmarking against existing standards, the framework exhibits exceptionally high accuracy and reliability in predicting concrete properties. This robust performance ultimately supports the creation of optimized mix designs, propelling advancements in concrete technology and application.

Conclusions

This study designed the AI-powered Intelligent Design of Concrete Material Gene Database, providing a systematic and technically sound foundation for concrete material research, and significantly enhancing the integration of material science principles and informed engineering decision-making. This study established a high-quality concrete gene database that could facilitate rapid evaluations and predictions via effectively leveraging advanced AI technologies. Furthermore, this study supported innovative advancements in concrete material design, paving a way for more efficient, sustainable practices in construction. Future research endeavors could broaden the system's capabilities, focusing on cross-disciplinary applications and the extension of the database to include low-carbon, environmentally-friendly material options.

Key words

concrete / intelligent database design / material genome / material performance / artificial intelligence

引用本文

刘楚瑶, 罗梓轩, 杨柳, 胡金其, 刘赞群, 张增起, 杨文萃, 徐慧宁, 陈梦妮. AI赋能的混凝土材料基因数据库智能设计及应用. 硅酸盐学报, 2026 , 54 (3) : 909 -921 . DOI: 10.14062/j.issn.0454-5648.20250728

Chuyao LIU, Zixuan LUO, Liu YANG, Jinqi HU, Zanqun LIU, Zengqi ZHANG, Wencui YANG, Huining XU, Mengni CHEN. AI-Powered Intelligent Design of Concrete Material Gene Database[J]. Journal of the Chinese Ceramic Society, 2026 , 54 (3) : 909 -921 . DOI: 10.14062/j.issn.0454-5648.20250728

基金

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国家重点研发计划(2022YFB2602602)

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排序方式：

2026年第54卷第3期

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文章信息

doi: 10.14062/j.issn.0454-5648.20250728

接收时间：2025-10-09
首发时间：2026-10-27
出版时间：2026-03-20

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出版历史

收稿日期：2025-10-09
修回日期：2025-10-30

基金

国家重点研发计划(2022YFB2602602)

作者信息

^1．中南大学计算机学院，长沙 410075

^2．中南大学土木工程学院，长沙 410075

^3．北京科技大学冶金与生态工程学院，北京 100083

^4．哈尔滨工业大学交通科学与工程学院，哈尔滨 150090

通讯作者:

杨柳（1979—），女，博士，教授

刘赞群（1975—），男，博士，教授

杨文萃（1981—），女，博士，副教授

张增起（1991—），男，博士，教授。

参考文献

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https://castjournals.cast.org.cn/joweb/gsyxb/CN/10.14062/j.issn.0454-5648.20250728

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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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