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材料导报  2026, Vol. 40 Issue (9): 25090030-8    https://doi.org/10.11896/cldb.25090030
  无机非金属及其复合材料 |
早期二氧化碳捕集下矿物掺合料混凝土碳化深度预测模型
周明1, 王非凡1, 温小栋1,*, 管小军2, 翁功伟2
1 宁波工程学院全省深海基础智能建造与运维重点实验室,浙江 宁波 315211
2 宁波建工工程集团股份有限公司,浙江 宁波 315211
Prediction Model for Carbonation Depth of Mineral Admixture Concrete UnderEarly-age CO2 Capture
ZHOU Ming1, WANG Feifan1, WEN Xiaodong1,*, GUAN Xiaojun2, WENG Gongwei2
1 Zhejiang Key Laboratory of Intelligent Construction and Operation & Maintenance for Deep-Sea Foundations, Ningbo University of Technology, Ningbo 315211, Zhejiang, China
2 Ningbo Construction Engineering Group Co., Ltd., Ningbo 315211, Zhejiang, China
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摘要 矿物掺合料混凝土碳捕集技术因固碳潜力大、工业固废利用率高,成为负排放技术的研究热点。然而早龄期碳化会显著增加钢筋锈蚀风险,现有碳化模型是基于28 d标养试件提出并建立的,对早龄期暴露、高掺量矿物掺合料混凝土的预测存在严重保守偏差。为此,本工作制备了15组粉煤灰/矿渣混凝土试件,经14 d标养后开展加速碳化试验。基于黄士元模型框架,首次引入矿物掺合料碳化速度影响系数(Km) 表征掺合料类型与掺量的早龄期效应,并采用具有全局优化优势的粒子群算法(PSO) 进行参数辨识。结果表明:新模型显著降低预测误差(相对误差均值0.189 vs.修正黄士元模型0.374),独立验证证实其对于包含硅灰、矿粉、粉煤灰等掺合料及更宽水胶比(W/B)范围的混凝土仍具有良好普适性(实测/预测值=0.93±0.15),Km 表达式揭示了矿物掺合料种类及掺量主导扩散加速效应,与国际标准方法对比进一步凸显了本模型在可解释性和工程适用性方面的优势。该模型可为碳捕集混凝土的早龄期钢筋锈蚀风险管控提供理论工具。
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周明
王非凡
温小栋
管小军
翁功伟
关键词:  碳捕集  矿物掺合料  早龄期  粒子群优化  预测模型    
Abstract: Mineral admixture concrete carbon capture technology has emerged as a research focus in negative emission technologies due to its substantial carbon sequestration potential and high utilization rate of industrial solid waste. However, early-age carbonation significantly increases the risk of steel reinforcement corrosion. Existing carbonation models, primarily based on 28-day standard-cured specimens, exhibit considerable conservative bias when predicting the carbonation behavior of concrete containing high-volume mineral admixtures and subjected to early-age exposure. To address this limitation, fifteen groups of fly ash (FA) and ground granulated blast furnace slag (GGBS) concrete specimens were prepared and subjected to accelerated carbonation tests after 14 days of standard curing. Building upon the Huang Shiyuan model framework, a Mineral Admixture Carbonation Rate Influence Coefficient (Km) was introduced for the first time to characterize the early-age effects of admixture type and dosage, and the Particle Swarm Optimization (PSO) algorithm, with global search capability in multi-parameter nonlinear systems, was used to optimize the parameters. The newly introduced Km coefficient quantitatively captures the differential effects of FA and GGBS, supported by microstructural reasoning. Independent validation confirmed its general applicability even for concrete incorporating FA, GGBS, and a wider range of water-to-binder ratios (W/B) (measured-to-predicted ratio: 0.93±0.15). This model provides a theoretical tool for managing the early-age steel reinforcement corrosion risk in carbon capture concrete.
Key words:  carbon capture    mineral admixture    early-age    particle swarm optimization    prediction mode
收稿日期:  2026-05-10      出版日期:  2026-05-10      发布日期:  2026-05-18
ZTFLH:  TU235  
基金资助: 宁波市2035重大专项(2024Z258);宁波市国际合作项目(2024H023);尖兵领雁项目(2024C03286(SD2));国家级大学生创新创业训练计划项目(202411058014)
引用本文:    
周明, 王非凡, 温小栋, 管小军, 翁功伟. 早期二氧化碳捕集下矿物掺合料混凝土碳化深度预测模型[J]. 材料导报, 2026, 40(9): 25090030-8.
ZHOU Ming, WANG Feifan, WEN Xiaodong, GUAN Xiaojun, WENG Gongwei. Prediction Model for Carbonation Depth of Mineral Admixture Concrete UnderEarly-age CO2 Capture. Materials Reports, 2026, 40(9): 25090030-8.
链接本文:  
https://www.mater-rep.com/CN/10.11896/cldb.25090030  或          https://www.mater-rep.com/CN/Y2026/V40/I9/25090030
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