硅酸盐学报

水泥基材料中钢筋腐蚀智能预测与风险分级

PDF下载

吕余辉 ¹ , 纪豪栋 ¹ , 余倩倩 ² , 叶海隆 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): 947-956

收起

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) : 947 -956

水泥基材料中钢筋腐蚀智能预测与风险分级

全屏

吕余辉¹, 纪豪栋¹, 余倩倩², 叶海隆¹

作者信息

^1．香港大学土木工程系，香港 999077

^2．同济大学土木工程学院建筑工程系，上海 200092

通讯作者:

叶海隆（1988—），男，博士，副教授。

作者简介:

吕余辉（1991—），女，博士。

Intelligent Prediction and Risk Classification of Steel Corrosion in Cementitious Materials

Yuhui LYU¹, Haodong JI¹, Qianqian YU², Hailong YE¹

Affiliations

^1.Department of civil engineering, The University of Hong Kong, Hong Kong 999077, China

^2.Department of Building Engineering, College of Civil Engineering, Tongji University, Shanghai 200092, China

Corresponding Author:

YE Hailong (1988-), male, Ph.D., Associate Professor. E-mail: hlye@hku.hk

LYU Yuhui (1991-), female, Ph.D. E-mail: yuhuilyu@hku.hk

出版时间: 2026-03-20 doi: 10.14062/j.issn.0454-5648.20250680

文章导航

摘要

收起

提出了一种融合物理先验知识与不确定性量化的高斯过程回归（GPR）框架，用于预测水泥基材料中钢筋腐蚀速率，并拓展至腐蚀风险等级划分与工程响应决策。通过在模型中嵌入电化学相关参数并开展不确定性评估，结果显示，该框架在预测精度、泛化能力及边界条件下的稳定性方面均优于传统GPR，尤其在高腐蚀环境中能够有效降低预测的系统性偏差。进一步分析表明，当水泥基材料孔溶液的氯离子与氢氧根离子浓度比处于0.2～0.6的区间时，模型的误差与不确定性显著增加，建议将该区间作为后续实验的重点关注范围。基于预测值及其置信区间，构建了包括常规监测、确认复核、谨慎使用与紧急应对的风险响应策略，以支撑耐久性评估与工程决策。

关键词

水泥基材料 / 钢筋腐蚀 / 高斯过程回归 / 工程决策

Abstract

收起

Introduction

Steel corrosion is a critical deterioration phenomenon in concrete structures, particularly under harsh environmental conditions involving carbonation and chloride ingress. Accurate corrosion rate prediction is essential for structural life assessment and maintenance planning. The existing methods include empirical and electrochemical models; they suffer from a limited adaptability or a high computational complexity. Machine learning (ML) approaches show promise but typically focus on point estimates, while neglecting prediction uncertainty, which is crucial for reliable engineering decision-making. This study was to develop a Gaussian Process Regression (GPR) framework that could integrate physical prior knowledge with uncertainty quantification for steel corrosion rate prediction in cementitious materials, addressing two key questions, i.e., 1) how to enhance GPR accuracy and robustness for corrosion prediction, and 2) whether GPR uncertainty outputs could effectively support corrosion risk classification and engineering decisions.

Methodology

The study utilized 180 experimental datasets from mortar samples embedded steel exposed to 6 relative humidity (RH) conditions after carbonation treatment. Each dataset contained 15 corrosion-related features categorized as mixture parameters (i.e., cement, slag, fly ash, and silica fume proportions, water-to-binder ratio, and chloride content), material properties (i.e., free chloride content, pH value, and porosity), environmental parameters (i.e., RH, saturation, and water content), and electrochemical parameters (i.e., corrosion potential, electrical resistivity, and [Cl^-]/[OH^-]).

The GPR model incorporated physical prior knowledge via setting the mean function as a linear relationship with a logarithmic chloride-to-hydroxide ratio, i.e., m(x) = a · lg([Cl^- ] / [OH^- ]) + b , based on the electrochemical corrosion theory. A Gaussian radial basis function served as a covariance function to capture smooth corrosion behavior variations.

Results and discussion

The performance evaluation using 10-fold cross-validation demonstrates that the physical prior GPR consistently outperforms conventional GPR across all the feature combinations. The model shows a superior prediction accuracy (i.e., higher R²), a better uncertainty quantification (i.e., lower negative log-likelihood), and more reliable confidence intervals (i.e., higher prediction interval coverage probability).

The corrosion rates are classified into four levels, i.e., low (<0.1 μA/cm²), low-medium (0.1-0.5 μA/cm²), medium-high (0.5-1.0 μA/cm²), and high (>1.0 μA/cm²). The model achieves a high accuracy in identifying severe corrosion conditions, with most of the 159 high corrosion samples correctly classified, though some confusion exists between intermediate levels.

An uncertainty-driven decision strategy is developed using confidence interval width (threshold: 4 μA/cm²) combined with predicted corrosion levels to generate four response categories, i.e., immediate intervention (i.e., high corrosion + low uncertainty), manual review (i.e., high corrosion + high uncertainty), continuous monitoring (i.e., low corrosion + high uncertainty), and accept current condition (i.e., low corrosion + low uncertainty). Approximately 36% of samples are flagged for review or monitoring due to the high uncertainty, demonstrating a value of uncertainty assessment in risk management.

Conclusions

This work presented a framework combining physical knowledge with probabilistic modeling for trustworthy AI-based corrosion prediction. Key contributions could include 1) effective integration of electrochemical mechanisms into GPR architecture, improving model robustness and interpretability; 2) development of a joint corrosion level and uncertainty classification system; 3) establishment of an uncertainty-based engineering decision framework; and (4) validation that uncertainty quantification effectively could identify potentially problematic predictions requiring additional review.

The framework could advance AI applications in structural health monitoring from simple prediction tools to comprehensive decision support systems, providing a foundation for reliable, interpretable infrastructure maintenance strategies. The approach demonstrated a promising potential for enhancing structural safety through improved corrosion risk assessment and management in challenging service environments.

Key words

cementitious materials / steel corrosion / gaussian process regression / engineering decision-making

引用本文

吕余辉, 纪豪栋, 余倩倩, 叶海隆. 水泥基材料中钢筋腐蚀智能预测与风险分级. 硅酸盐学报, 2026 , 54 (3) : 947 -956 . DOI: 10.14062/j.issn.0454-5648.20250680

Yuhui LYU, Haodong JI, Qianqian YU, Hailong YE. Intelligent Prediction and Risk Classification of Steel Corrosion in Cementitious Materials[J]. Journal of the Chinese Ceramic Society, 2026 , 54 (3) : 947 -956 . DOI: 10.14062/j.issn.0454-5648.20250680

基金

收起

国家重点研发计划“工程科学与综合交叉”重点专项(2024YFF0510400)

参考文献

收起

文献

收起

参考文献引证文献

排序方式：

2026年第54卷第3期

PDF下载

引用本文

BibTeX

文章信息

doi: 10.14062/j.issn.0454-5648.20250680

接收时间：2025-09-15
首发时间：2026-10-27
出版时间：2026-03-20

补充材料

相关文章

文章信息

作者

出版历史

收稿日期：2025-09-15
修回日期：2025-10-02

基金

国家重点研发计划“工程科学与综合交叉”重点专项(2024YFF0510400)

作者信息

^1．香港大学土木工程系，香港 999077

^2．同济大学土木工程学院建筑工程系，上海 200092

通讯作者:

叶海隆（1988—），男，博士，副教授。

参考文献

分享链接

https://castjournals.cast.org.cn/joweb/gsyxb/CN/10.14062/j.issn.0454-5648.20250680

分享至

全文二维码

扫描看全文

引用本文

BibTeX

本文的引用情况

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

关闭全屏

BibTeX
EndNote
RefWorks
TxT