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

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.

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 23300+ 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.