硅酸盐学报

粉煤灰混凝土冻融性能智能预测方法

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潘磊 ¹^,² , 傅宇方 ¹^,² , 王元丰 ³

硅酸盐学报 | 混凝土耐久性机理表征与预测方法专题—研究论文 2026,54(2): 793-810

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硅酸盐学报 |混凝土耐久性机理表征与预测方法专题—研究论文 2026 , 54 (2) : 793 -810

粉煤灰混凝土冻融性能智能预测方法

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潘磊¹^,², 傅宇方¹^,², 王元丰³

作者信息

^1．交通运输部公路科学研究院，北京 100088

^2．北京大杜社公路材料腐蚀与工程安全国家野外科学观测研究站，北京 101103

^3．北京交通大学土木建筑工程学院，北京 100044

通讯作者:

傅宇方（1972—），男，博士，研究员。

作者简介:

潘磊（1991—），男，博士，助理研究员。

Intelligent Prediction Method for Freeze-Thaw Performance of Fly Ash Concrete

Lei PAN¹^,², Yufang FU¹^,², Yuanfeng WANG³

Affiliations

^1.Research Institute of Highway, Ministry of Transport, Beijing 100088, China

^2.National Field Observation and Research Station of Highway Material Corrosion and Engineering Safety, Beijing Dadushe, Beijing 101103, China

^3.School of Civil Engineering, Beijing Jiaotong University, Beijing 100044, China

Corresponding Author:

FU Yufang (1972-), male, Ph.D., Professor. E-mail: yf.fu@rioh.cn

PAN Lei (1991-), male, Ph.D., Assistant Researcher. E-mail: l.pan@rioh.cn

出版时间: 2026-02-20 doi: 10.14062/j.issn.0454-5648.20250706

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摘要

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冻融是寒区混凝土结构耐久性失效的主要诱因之一，而传统冻融性能预测方法存在效率低、精度不足等问题。针对现代混凝土结构对冻融耐久性预测需求，本工作提出了一种融合最大信息系数（MIC）、特征选择、智能优化算法和机器学习算法的智能预测方法体系。通过收集537组实验数据，构建了包含水泥用量、冻融循环次数等14个输入变量的数据库，运用MIC和Lasso回归算法实现特征选择。采用贝叶斯优化算法对随机森林、支持向量回归和极端梯度提升算法的超参数进行优化，分别建立粉煤灰混凝土质量损失率和相对动弹性模量的预测模型，并采用Shapley加法解释法和部分依赖图法，确定关键参数的贡献度与非线性效应。结果显示：1）采用特征选择方法可以有效提升模型的预测效果；2）极端梯度算法建立的预测模型具有最优预测精度和泛化能力，其决定系数R²值分别达0.9224和0.9127；3）获取了特征重要性和相关性分析结果，可为寒区粉煤灰混凝土配合比设计提供组分优化参考。

关键词

粉煤灰混凝土 / 冻融循环 / 机器学习 / 冻融性能预测 / 特征分析

Abstract

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Introduction

Freeze-thaw (F-T) damage, characterized by microcrack propagation and material degradation caused by repeated water-ice phase transitions, is a critical factor leading to durability degradation of concrete structures in cold regions. Fly ash (FA) as an industrial byproduct is widely used to replace cement, reducing carbon emissions, while enhancing mechanical properties. However, conventional prediction methods for freeze-thaw resistance of fly ash concrete (FAC), which rely on the empirical formulas or simplified mechanical models, often exhibit limitations in efficiency and accuracy. These limitations stem from their inability to capture the complex nonlinear interactions among multifactorial parameters such as material composition, environmental conditions, and loading histories. As modern engineering increasingly demands high-precision durability assessments for infrastructure lifespan prediction, there is an urgent need to develop advanced predictive tools. This study was to propose an artificial intelligence (AI)-based framework to predict F-T resistance (i.e., mass loss rate, MLR; relative dynamic modulus of elasticity, RDME) with high precision and interpretability, addressing a critical deficiency in material design and durability assessment.

Methods

A comprehensive database of 537 experimental datasets was constructed, including 14 input variables (i.e., cement content, FA dosage, water-to-binder ratio (W/B), F-T cycles (NFT)). To address the curse of mitigate multicollinearity, a hybrid feature selection strategy combining the Maximum Information Coefficient (MIC) and Lasso regression was implemented. The MIC quantified nonlinear correlations between input variables and target responses (i.e., mass loss rate, MLR, and relative dynamic elastic modulus, RDME), while the Lasso regression performed variable subset selection through L1 regularization. Three machine learning algorithms-Random Forest (RF), Support Vector Regression (SVR), and Extreme Gradient Boosting (XGBoost) were subsequently employed to develop prediction models. Hyperparameter optimization for each algorithm was conducted via the Bayesian optimization with 5-fold cross-validation to prevent overfitting. The model performance was rigorously evaluated using multiple metrics, including coefficient of determination (R²), mean absolute error (MAE), and root mean squared error (RMSE). Furthermore, the SHapley Additive exPlanations (SHAP) values and Partial Dependence Plots (PDP) were utilized to dissect the contribution hierarchy and nonlinear marginal effects of critical parameters on the FAC freeze-thaw performance, thereby enhancing the model interpretability.

Results and discussion

The XGBoost-T model (with feature selection) achieves the a maximum accuracy for MLR (R²=0.9224) and RDME (R²=0.9127), outperforming XGBoost model by 9.5% and 4.7%, respectively. The feature selection reduces a mean absolute error (MAE) by 26.1% (MLR) and 62.6% (RDME), proving its necessity. The study also analyzes the contributions and nonlinear effects of key parameters by the Shapley Additive Explanations (SHAP) and Partial Dependence Plots (PDP). The findings indicate that cement content and total binder content increase can reduce MLR and improve RDME, while water-to-binder ratio and freeze-thaw cycles have the opposite effects. The PDP analysis provides recommendations for optimizing FAC mix designs in cold regions, such as a water content range of 130-180 kg/m³ and a sand content range of 550-700 kg/m³ for optimal RDME.

Conclusions

This research introduced an AI-driven framework for predicting the freeze-thaw resistance of FAC, integrating feature engineering, intelligent optimization, and explainable machine learning. The framework could overcome the limitations of conventional empirical models via leveraging data-driven insights and high-dimensional nonlinear modeling. The XGBoost-based prediction model demonstrated state-of-the-art accuracy and robustness, while the PDP-derived parameter sensitivity analysis offered actionable strategies for enhancing FAC durability in cold environments. This study could contribute to the theoretical advancement of FAC durability design and underscore the transformative potential of artificial intelligence in civil engineering materials research. Future work could focus on expanding the database to include emerging cementitious materials and integrating multi-physics simulations for holistic durability assessments.

Key words

fly ash concrete / freeze-thaw cycles / machine learning / performance prediction / feature analysis

引用本文

潘磊, 傅宇方, 王元丰. 粉煤灰混凝土冻融性能智能预测方法. 硅酸盐学报, 2026 , 54 (2) : 793 -810 . DOI: 10.14062/j.issn.0454-5648.20250706

Lei PAN, Yufang FU, Yuanfeng WANG. Intelligent Prediction Method for Freeze-Thaw Performance of Fly Ash Concrete[J]. Journal of the Chinese Ceramic Society, 2026 , 54 (2) : 793 -810 . DOI: 10.14062/j.issn.0454-5648.20250706

基金

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国家自然科学基金(U24B20112)
中央级公益性科研院所基本科研业务费专项资金(2025-9047)

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2026年第54卷第2期

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文章信息

doi: 10.14062/j.issn.0454-5648.20250706

接收时间：2025-09-24
首发时间：2026-10-27
出版时间：2026-02-20

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出版历史

收稿日期：2025-09-24
修回日期：2025-10-21

基金

国家自然科学基金(U24B20112)

中央级公益性科研院所基本科研业务费专项资金(2025-9047)

作者信息

^1．交通运输部公路科学研究院，北京 100088

^2．北京大杜社公路材料腐蚀与工程安全国家野外科学观测研究站，北京 101103

^3．北京交通大学土木建筑工程学院，北京 100044

通讯作者:

傅宇方（1972—），男，博士，研究员。

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https://castjournals.cast.org.cn/joweb/gsyxb/CN/10.14062/j.issn.0454-5648.20250706

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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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