硅酸盐学报

再生骨料混凝土徐变行为的关键影响参数及预测模型研究进展

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高畅 ¹ , 蒋志鹏 ² , 罗楚钰 ² , 汤金辉 ² , 胡张莉 ² , 王磊 ¹ , 刘加平 ²

硅酸盐学报 | 低碳水泥与固废资源化利用专题—综合评述 2026,54(5): 1685-1698

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硅酸盐学报 |低碳水泥与固废资源化利用专题—综合评述 2026 , 54 (5) : 1685 -1698

再生骨料混凝土徐变行为的关键影响参数及预测模型研究进展

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高畅¹, 蒋志鹏², 罗楚钰², 汤金辉², 胡张莉², 王磊¹, 刘加平²

作者信息

^1．长沙理工大学土木工程学院，长沙 410114

^2．东南大学材料科学与工程学院，南京 211189

通讯作者:

王磊（1979—），男，博士，教授。

作者简介:

高畅（1991—），女，博士。

Research Progress on Key Influencing Parameters and Prediction Models of Creep Behavior of Recycled Concrete

Chang GAO¹, Zhipeng JIANG², Chuyu LUO², Jinhui TANG², Zhangli HU², Lei WANG¹, Jiaping LIU²

Affiliations

^1.School of Civil Engineering, Changsha University of Science and Technology, Changsha 410114, China

^2.School of Materials Science and Engineering, Southeast University, Nanjing 211189, China

Corresponding Author:

WANG Lei (1979-), male, Ph.D., Professor. E-mail: Leiwang@csust.edu.cn

GAO Chang (1991-), female, Ph.D. E-mail: gc812@csust.edu.cn

出版时间: 2025-08-01 doi: 10.14062/j.issn.0454-5648.20250240

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摘要

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再生骨料混凝土徐变是其结构长期性能评判的一个重要时变指标，但其徐变行为显著大于普通混凝土，严重影响结构长期性能。本文将从再生骨料混凝土徐变行为的关键影响因素、微观作用机理及预测模型3个方面概述再生骨料混凝土材料的徐变特性研究进展。本文系统综述了材料组分、配比设计、环境因素等关键因素作用下的再生骨料混凝土徐变性能，并基于最大相关性分析方法及关键因素耦合作用分析，发现再生骨料残余砂浆含量、吸水率及母体混凝土水灰比是影响徐变的关键材料因素。在微观层面上，从物化作用、水化产物、微观结构等角度，分析概述了导致再生骨料混凝土相较于普通混凝土更为显著徐变的内部机理，主要源于其内部C-S-H凝胶的黏滑效应、双重ITZ的应力集中及骨料-基体间相互作用所致。关于再生骨料混凝土徐变预测，本文系统总结了相关徐变模型，包括经典解析式模型、修订的再生骨料混凝土徐变模型、神经网络模型等，并分析了各个模型优缺点及适用性。最后，讨论当前再生骨料混凝土徐变性能及预测研究的不足及发展趋势，未来可结合多尺度仿真、智能算法与动态数据库，建立再生骨料质量-徐变性能关联体系，发展界面修复技术与寿命预测方法，以推动再生骨料混凝土的高性能化应用。

关键词

再生骨料混凝土 / 徐变行为 / 预测模型 / 机器学习

Abstract

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The rapid urbanization in China significantly increases a demand for concrete, driving the development and application of recycled aggregate concrete (RAC) to reduce natural resource consumption and mitigate land pollution from construction waste. The RAC is produced via crushing, screening, and reusing demolished concrete as partial or full replacements for natural aggregates. However, its creep behavior exhibits higher deformation than conventional concrete due to the attached old mortar on recycled aggregates, which weakens interfacial transition zones (ITZs) and increases porosity. This review represents the influencing factors, mechanisms, and prediction models of RAC creep, aiming to guide its engineering applications.

In this review, the creep characteristics and influencing factors of RAC are summarized and discussed from three aspects (i.e., material composition, mix design and environmental factors). The material composition consists of recycled aggregate quality, binder system and parent concrete. Residual mortar content (20%-40%), water absorption (3%-8%), and microcracks in aggregates are exacerbated by 30%-50%. Some techniques like mechanical shaping (e.g., high-speed self-impact to remove weak mortar) and chemical strengthening (e.g., polymers, CO₂ treatment) can reduced by 26.7%. For a binder system, mineral admixtures (e.g., fly ash, GGBS, silica fume) optimize hydration products and ITZs. Fly ash (20%-30%) reduces a long-term creep by 0.25-0.35 in coefficient, while excessive dosage (>50%) increases it. A lower parent concrete strength (e.g., water-cement ratio >0.6) increases RAC creep by 33%. The carbonation of parent concrete alters pore structures, accelerating moisture migration at the RH of <60%. For a mix design, the water-cement ratio, recycled aggregate replacement ratio and mixing methods are discussed. The existing research indicates that saturated surface-dry or additional water method compensates aggregate absorption mitigating creep. Conventional recycled aggregate replacement methods increase a creep by 6.5%, whereas the "equivalent mortar volume" method reduces it by 23.3% via balancing mortar content. The two-stage mixing (TSMA) and ultrasonic vibration enhance the ITZ density, lowering a creep by 4.7%. The environmental factors on the RAC creep of humidity, temperature, CO₂ curing and carbonation effects are summarized. The RH reduction from 70% to 30% increases a creep by 37.7% due to moisture loss in porous aggregates. Elevated temperature accelerates the ITZ microcrack propagation, especially in freeze-thaw cycles. CO₂ curing densifies ITZs via CaCO₃ formation, reducing creep. Finally, the maximum information coefficient (MIC) analysis is conducted, ranking water-cement ratio (MIC=0.476), aggregate absorption (0.454), and loading age (0.441) as dominant effects, and the cement type (0.193) and specimen size (0.213) are less significant.

This review also discusses the microscale creep mechanisms. The RAC's creep stems from viscous flow, dual ITZs, moisture diffusion and elastic modulus mismatch according to the results of the existing research. The viscous flow is caused by the stress-induced water migration in C-S-H gels of old mortar. More types of ITZ exist in RAC micro-structures, and weak zones between old/new mortar and aggregates promote microcracking (The SEM images reveal 2-3 times higher porosity in ITZs). A high water absorption (3%-10%) of recycled aggregate creates internal humidity gradients, also coupling shrinkage and creep. Recycled aggregates present a lower stiffness (i.e., 20%-40% reduction compared to natural aggregates), which transfers stress to the matrix, amplifying deformation.

Summary and prospects

The predicted models consisting of empirical models, classical models, code-oriented models, theoretical aging models and machine learning algorithms prediction are summarized. The conventional prediction models, such as ACI 209 and CEB-FIP MC90, rely on regression-based equations, but struggle to account for the complexity of recycled aggregate concrete (RAC). The theoretical models like B3/B4 incorporate aging theory, but still require a recalibration for RAC, such as introducing residual mortar coefficients. These models have limitations, including narrow parameter coverage (3-5 factors), reliance on linear assumptions, and poor long-term prediction accuracy (R²~0.7). In contrast, machine learning (ML) models (e.g., XGBoost, CNN, LGBM) achieve significantly a higher performance, with R² > 0.95, compared to 0.6-0.8 for the conventional models. The XGBoost demonstrates the optimum accuracy (i.e., R²=0.989, RMSE=0.117). The ML key advantages include handling nonlinear interactions among 10+ parameters (e.g., the SHAP analysis) and improving stability through CNN's residual encoder. However, some challenges remain, such as high data dependency (requiring 23 300+ samples), overfitting risks, and limited interpretability, due to their "black-box" nature.

This review highlights the complexities of creep in RAC and proposes key strategies for optimization and innovation. Material optimization can be achieved via controlling residual mortar (<20%), incorporating supplementary cementitious materials (SCMs) like 30% GGBS, and adopting CO₂ curing to mitigate creep. Multiscale modeling, combining molecular dynamics with FEM, can simulate interfacial transition zone (ITZ) effects. Machine learning (ML) advancements should focus on expanding databases to include load history, environmental cycles, and admixtures, while hybrid models (e.g., physics-informed neural networks) can enhance both accuracy and interpretability. Sustainable solutions like bio-chemical ITZ self-healing and digital twins for lifecycle prediction offer promising future directions. Overall, integrating ML-enhanced, data-driven approaches enable safer, large-scale RAC applications, supporting greener construction practices.

Key words

recycled concrete / creep behavior / predictive model / machine learning

引用本文

高畅, 蒋志鹏, 罗楚钰, 汤金辉, 胡张莉, 王磊, 刘加平. 再生骨料混凝土徐变行为的关键影响参数及预测模型研究进展. 硅酸盐学报, 2026 , 54 (5) : 1685 -1698 . DOI: 10.14062/j.issn.0454-5648.20250240

Chang GAO, Zhipeng JIANG, Chuyu LUO, Jinhui TANG, Zhangli HU, Lei WANG, Jiaping LIU. Research Progress on Key Influencing Parameters and Prediction Models of Creep Behavior of Recycled Concrete[J]. Journal of the Chinese Ceramic Society, 2026 , 54 (5) : 1685 -1698 . DOI: 10.14062/j.issn.0454-5648.20250240

基金

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国家杰出青年科学基金项目(52425802)
国家自然科学基金重大项目(52293432)
国家自然科学基金(52308233)
高性能土木工程材料国家重点实验室开放课题(2023CEM002)
高速铁路建造技术国家工程实验室开放课题(HSR202101)

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2026年第54卷第5期

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文章信息

doi: 10.14062/j.issn.0454-5648.20250240

接收时间：2025-03-31
首发时间：2026-09-20
出版时间：2025-08-01

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出版历史

收稿日期：2025-03-31
修回日期：2025-05-06

基金

国家杰出青年科学基金项目(52425802)

国家自然科学基金重大项目(52293432)

国家自然科学基金(52308233)

高性能土木工程材料国家重点实验室开放课题(2023CEM002)

高速铁路建造技术国家工程实验室开放课题(HSR202101)

作者信息

^1．长沙理工大学土木工程学院，长沙 410114

^2．东南大学材料科学与工程学院，南京 211189

通讯作者:

王磊（1979—），男，博士，教授。

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https://castjournals.cast.org.cn/joweb/gsyxb/CN/10.14062/j.issn.0454-5648.20250240

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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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