Synergistic Effects of Alkali Activator Dosage on Carbonation Resistance and Microstructural Evolution of Recycled Concrete: Insights from Fractal Analysis and Optimal Threshold Identification
Abstract
1. Introduction
2. Materials and Methods
2.1. Materials
2.2. Mix Proportion
2.3. Carbonation Test Method
2.4. Microstructure Characterization Methods
- The pore structure was assumed to exhibit statistical self-similarity within the analyzed scale range (50 nm to 50 μm).
- Pores smaller than the SEM resolution threshold (50 nm) were not captured in the imaging process.
- Fractal dimension (FD) values derived from 2D images may underestimate the true 3D pore complexity; however, averaging across multiple imaging planes was applied to mitigate this effect.
2.5. Flowchart
3. Results and Discussion
3.1. Carbonation Depth and Compressive Strength
3.2. Comparative SEM Analysis of Microstructural Evolution Pre- and Post-Carbonization
3.3. XRD Analysis of JFRAC
3.4. Effect of Carbonization on the Physical Phase Distribution of Pores
3.5. Fractal Analysis of Pore Structure Evolution in Concrete During Carbonation
3.6. Embodied Carbon Analysis
4. Conclusions
- (1)
- Optimal Alkali Dosage Enhances Carbonation Resistance and Microstructural Integrity: The incorporation of 8% CaO as an alkali activator significantly improves the carbonation resistance of fly ash recycled aggregate concrete (FRAC), achieving a 35% reduction in carbonation depth and a 10.76% increase in compressive strength compared to the control group. This optimal dosage promotes the formation of dense C–S–H/AFt networks, reduces porosity to 22.87%, and optimizes pore fractal complexity (FD > 1.9), effectively suppressing CO2 diffusion. However, excessive alkali activation (12% CaO) induces microcracks and pore interconnectivity, leading to a 7.6% strength reduction and accelerated carbonation.
- (2)
- Fractal–Pore Relationships Reveal Durability Mechanisms: Fractal dimension (FD) analysis establishes a quantitative link between pore complexity and macro-performance. A threshold of FD > 1.9 correlates with low pore connectivity and high durability, validating its role as a critical indicator for carbonation resistance. However, FD sensitivity to image resolution and binarization methods necessitates standardized protocols for future studies.
- (3)
- Environmental and Practical Implications: Despite the embodied carbon of CaO (9.84 kg CO2/m3), its synergy with fly ash (≈120 kg CO2/m3 reduction) and extended service life (15–20 years) ensure a net carbon benefit. This highlights the potential of alkali-activated FRAC in sustainable construction.
5. Study Limitations and Future Directions
5.1. Limitations
- (1)
- The current scope focuses on compressive strength and carbonation resistance. Other durability metrics (e.g., chloride ingress, sulfate attack) remain unexplored.
- (2)
- Thermogravimetric analysis (TGA) was not performed to quantify carbonation products.
- (3)
- The volumetric strain induced by CaO expansion was not directly measured.
5.2. Future Directions
- (1)
- Multidisciplinary Modeling: integrate DeepLabv3+ and EfficientNet-B7 for automated microstructural analysis, enabling the pixel-level quantification of pores, cracks, and ettringite morphology.
- (2)
- Extended Testing: evaluate carbonation behavior beyond 28 days to capture long-term degradation patterns.
- (3)
- Durability Expansion: validate the 8% threshold against chloride penetration, freeze–thaw cycles, and acid exposure.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
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Compressive Strength/MPa | Flexural Strength/MPa | Solidification Time/min | Stabilizing | ||||
---|---|---|---|---|---|---|---|
3 d | 28 d | 3 d | 28 d | Initial Set | Final Set | ||
Standardized value | ≥17.0 | ≥42.5 | ≥4 | ≥6.5 | ≥45 | ≤600 | Conformity |
Measured value | 17.6 | 45.5 | 5.1 | 7.8 | 1545 | 371 | Conformity |
Chemical Composition | SO3 | CaO | SiO2 | Al2O3 | Fe2O3 | MgO |
---|---|---|---|---|---|---|
Quantity contained (%) | 0.8 | 5.6 | 43 | 23 | 2.5 | 0.95 |
Group | Cement (kg/m3) | Recycled Aggregate (kg/m3) | Coarse Aggregate (kg/m3) | Fine Aggregate (kg/m3) | Water (kg/m3) | Fly Ash (kg/m3) | CaO (kg/m3) |
---|---|---|---|---|---|---|---|
J0 | 287 | 338.33 | 789.42 | 607.25 | 221 | 123 | 0 |
J4 | 287 | 338.33 | 789.42 | 607.25 | 221 | 123 | 4.92 |
J8 | 287 | 338.33 | 789.42 | 607.25 | 221 | 123 | 9.84 |
J12 | 287 | 338.33 | 789.42 | 607.25 | 221 | 123 | 14.76 |
Pre-Carbonation | Post-Carbonization | |||
---|---|---|---|---|
Fractal Dimension Estimation | R2 | Fractal Dimension Estimation | R2 | |
J0 | y = 0.16961 − 1.8912x | 0.998 | y = 0.11657 − 1.9296x | 0.999 |
J4 | y = 0.14308 − 1.9041x | 0.999 | y = 0.11349 − 1.9306x | 0.999 |
J8 | y = 0.14077 − 1.9147x | 0.998 | y = 0.09003 − 1.9474x | 0.999 |
J12 | y = 0.16993 − 1.8915x | 0.998 | y = 0.12384 − 1.9257x | 0.998 |
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Huang, Y.; Gong, A.; Jin, Z.; Peng, Y.; Shao, S.; Yong, K. Synergistic Effects of Alkali Activator Dosage on Carbonation Resistance and Microstructural Evolution of Recycled Concrete: Insights from Fractal Analysis and Optimal Threshold Identification. Buildings 2025, 15, 1742. https://doi.org/10.3390/buildings15101742
Huang Y, Gong A, Jin Z, Peng Y, Shao S, Yong K. Synergistic Effects of Alkali Activator Dosage on Carbonation Resistance and Microstructural Evolution of Recycled Concrete: Insights from Fractal Analysis and Optimal Threshold Identification. Buildings. 2025; 15(10):1742. https://doi.org/10.3390/buildings15101742
Chicago/Turabian StyleHuang, Yier, Aimin Gong, Zhuo Jin, Yulin Peng, Shanqing Shao, and Kang Yong. 2025. "Synergistic Effects of Alkali Activator Dosage on Carbonation Resistance and Microstructural Evolution of Recycled Concrete: Insights from Fractal Analysis and Optimal Threshold Identification" Buildings 15, no. 10: 1742. https://doi.org/10.3390/buildings15101742
APA StyleHuang, Y., Gong, A., Jin, Z., Peng, Y., Shao, S., & Yong, K. (2025). Synergistic Effects of Alkali Activator Dosage on Carbonation Resistance and Microstructural Evolution of Recycled Concrete: Insights from Fractal Analysis and Optimal Threshold Identification. Buildings, 15(10), 1742. https://doi.org/10.3390/buildings15101742