硅酸盐学报

基于机器学习技术的超高性能混凝土性能预测与精细化设计

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余睿 ¹ , 陈飞翔 ¹^,² , 范定强 ³ , 徐汪洋 ¹ , 张玲艳 ¹ , 李旺 ¹ , 吉朵朵 ¹

硅酸盐学报 | 研究论文 2025,53(5): 1165-1176

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硅酸盐学报 |研究论文 2025 , 53 (5) : 1165 -1176

基于机器学习技术的超高性能混凝土性能预测与精细化设计

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余睿¹, 陈飞翔¹^,², 范定强³, 徐汪洋¹, 张玲艳¹, 李旺¹, 吉朵朵¹

作者信息

^1．硅酸盐建筑材料国家重点实验室，中国武汉 430070

^2．中交第二航务工程局有限公司，中国武汉 430040

^3．香港理工大学土木与环境工程系，中国香港 999077

通讯作者:

范定强（1996—），男，硕士。

作者简介:

余睿（1986—），男，博士，研究员。

Machine Learning-Based Performance Prediction and Precision Design of Ultra-High Performance Concrete

Rui YU¹, Feixiang CHEN¹^,², Dingqiang FAN³, Wangyang XU¹, Lingyan ZHANG¹, Wang LI¹, Duoduo JI¹

Affiliations

^1.State Key Laboratory of Silicate Materials for Architectures, Wuhan 430070, China

^2.CCCC Second Harbor Engineering Company LTD., Wuhan 430040, China

^3.Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong 999077, China

Corresponding Author:

FAN Dingqiang (1996-), male, Master. E-mail: ding-qiang.fan@connect.polyu.hk

YU Rui (1986-), male, Ph.D., Professor. E-mail: r.yu@whut.edu.cn

出版时间: 2025-05-20 doi: 10.14062/j.issn.0454-5648.20240648

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摘要

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计算机技术的快速发展推动了建筑行业的信息化与智能化进程。对此，研究了机器学习（ML）技术在超高性能混凝土（UHPC）材料性能预测和精细化设计中的应用。针对ML训练数据库常存在数据缺失和异常值等问题，提出了一个综合的UHPC数据处理和分析框架，包括数据清洗、准备和模型训练模块，旨在提高训练精度和模型泛化能力。采用改进的链式方程多重插补法实现了缺失数据的插补，并使用孤立森林算法筛选异常数据，从而提升了数据库质量。基于优化后的数据库，建立了贝叶斯超参数优化的XGBoost模型，实现了对UHPC多项性能的精准预测（R²>0.95）。最后，提出了2种利用ML技术精细化设计UHPC配合比思路：其一为采用物理堆积理论进行配比初步设计，再利用建立的ML模型进行多项性能预测，进一步优化配合比；其二是将建立的ML预测模型与遗传算法相结合，以实现UHPC的多目标优化设计。研究成果为UHPC的智能化研发提供了新思路和方法，推动了UHPC技术的发展与应用，并促进了工程材料与ML技术的深度融合。

关键词

超高性能混凝土 / 机器学习 / 性能预测 / 多目标优化

Abstract

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Introduction

Ultra-high performance concrete (UHPC) has garnered widespread attention in the field of civil engineering due to its exceptional mechanical properties, durability, and workability. These superior properties are primarily attributed to its dense microstructure and unique material composition, which typically includes cement, silica fume, quartz powder, and superplasticizers, along with reinforcing materials like steel fibers. The remarkable performance of UHPC not only meets the demands for high-performance materials in modern engineering but also contributes to reducing the life-cycle costs of structures by minimizing section sizes and extending service life.

However, the development and application of UHPC face several challenges. The complexity of the preparation process, stringent requirements for raw materials, and the high sensitivity of UHPC properties to mix design variations increase both production costs and technical difficulties. Traditional experimental design methods struggle to efficiently optimize UHPC mixtures, and issues such as shrinkage and autogenous cracking require precise design solutions. These challenges make the composition design and performance prediction of UHPC a focal point in the field. With advancements in computer technology and data science, machine learning (ML) offers new tools and approaches for UHPC research and development. Therefore, this study aims to promote the application of ML technology in the UHPC field, thereby advancing the intelligent development of advanced construction materials.

Methods

The study introduced a comprehensive ML-based framework for UHPC performance prediction and precise design. The framework began with data cleaning, where the multiple imputation by chained equation (MICE) method with a predictive mean matching (PMM) kernel was employed to impute missing data in the UHPC database. Subsequently, the isolation forest algorithm was applied to identify and eliminate outlier data, thereby improving the quality of the dataset. The cleaned and optimized dataset was then used to train an XGBoost model, optimized via Bayesian hyperparameter tuning, to accurately predict various UHPC properties. Two AI-driven approaches for UHPC mix design were also proposed: one that combined the MAA model with ML predictions for multi-performance optimization, and another that integrated the ML model with genetic algorithm (GA) for multi-objective optimization.

Results and discussion

The application of MICE combined with PMM resulted in a substantial improvement in the accuracy of the imputed data, as evidenced by the reduced average deviation between imputed and original data values. This enhanced imputation process allowed for a more reliable dataset, which directly contributed to the performance of the XGBoost prediction models. Outlier detection via the isolation forest algorithm effectively removed data points that exhibited significant deviation from the norm, particularly in compressive strength and density measurements. The refined dataset led to more accurate and consistent predictions across all performance metrics. After hyperparameter optimization, the XGBoost model demonstrated exceptional predictive capabilities, with notable improvements in performance metrics. The predictive accuracy of the model was further validated against experimental data, confirming its effectiveness in real-world applications. These results highlight the potential of ML techniques in advancing the field of UHPC research. The integration of MICE and PMM into the data preprocessing pipeline ensured more accurate and reliable datasets, which were crucial for developing robust predictive models. The success of the XGBoost model, particularly after Bayesian optimization, underscores the importance of hyperparameter tuning in enhancing model performance.

Furthermore, the study proposes two ML-assisted design strategies for UHPC: 1) a combined physical packing theory and ML prediction model for initial mixture design, and 2) a multi-objective optimization approach using a metaheuristic algorithm for fine-tuning UHPC compositions. These strategies provide a framework for the intelligent and efficient design of UHPC materials, aligning with the growing demand for high-performance, sustainable construction materials.

Conclusions

This research demonstrates the significant potential of ML in predicting and optimizing the performance of UHPC. By addressing data challenges and enhancing prediction models, the study provides valuable insights into the application of ML in UHPC design. The proposed ML-assisted design strategies offer a practical approach to developing UHPC with tailored properties, paving the way for more sustainable and efficient construction practices.

Key words

ultra-high performance concrete / machine learning / performance predictions / multi-objective optimization

引用本文

余睿, 陈飞翔, 范定强, 徐汪洋, 张玲艳, 李旺, 吉朵朵. 基于机器学习技术的超高性能混凝土性能预测与精细化设计. 硅酸盐学报, 2025 , 53 (5) : 1165 -1176 . DOI: 10.14062/j.issn.0454-5648.20240648

Rui YU, Feixiang CHEN, Dingqiang FAN, Wangyang XU, Lingyan ZHANG, Wang LI, Duoduo JI. Machine Learning-Based Performance Prediction and Precision Design of Ultra-High Performance Concrete[J]. Journal of the Chinese Ceramic Society, 2025 , 53 (5) : 1165 -1176 . DOI: 10.14062/j.issn.0454-5648.20240648

基金

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国家自然科学基金(52178249)
广东省自然科学基金青年提升项目(2024A1515030243)
武汉市知识创新专项(2023010201010094)

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2025年第53卷第5期

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文章信息

doi: 10.14062/j.issn.0454-5648.20240648

接收时间：2024-10-10
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：2024-10-10
修回日期：2025-01-05

基金

国家自然科学基金(52178249)

广东省自然科学基金青年提升项目(2024A1515030243)

武汉市知识创新专项(2023010201010094)

作者信息

^1．硅酸盐建筑材料国家重点实验室，中国武汉 430070

^2．中交第二航务工程局有限公司，中国武汉 430040

^3．香港理工大学土木与环境工程系，中国香港 999077

通讯作者:

范定强（1996—），男，硕士。

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https://castjournals.cast.org.cn/joweb/gsyxb/CN/10.14062/j.issn.0454-5648.20240648

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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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