Microstructural Analysis and Rheological Modeling of Asphalt Mixtures Containing Recycled Asphalt Materials
Abstract
:1. Introduction
2. Literature Review
3. Objective and Research Approach
4. Materials and Testing
Mix | Recycled Material | VMA (%) | VFA (%) | VAir Voids (%) | VMastic (%) | |||
---|---|---|---|---|---|---|---|---|
ID | Description | RAP (% weight) | TOSS (% weight) | MWSS (% weight) | ||||
1 | PG 58-28 Control | 0 | 0 | 0 | 15.9 | 76.6 | 3.7 | 12.2 |
2 | 15% RAP | 15 | 0 | 0 | 15.2 | 72.9 | 4.1 | 11.1 |
3 | 25% RAP | 25 | 0 | 0 | 15.3 | 73.0 | 4.1 | 11.2 |
4 | 30% RAP | 30 | 0 | 0 | 15.0 | 75.4 | 3.7 | 11.3 |
5 | 15% RAP 5% MWSS | 15 | 0 | 5 | 15.6 | 75.0 | 3.9 | 11.7 |
6 | 15% RAP 5% TOSS | 15 | 5 | 0 | 15.9 | 77.2 | 3.6 | 12.3 |
7 | 25% RAP 5% TOSS | 25 | 5 | 0 | 15.4 | 73.9 | 4.0 | 11.4 |
8 | 25% RAP 5% MWSS | 25 | 0 | 5 | 14.8 | 72.5 | 4.1 | 10.7 |
Sieve size (mm) | RAP Passing (%) | MWSS Passing (%) | TOSS Passing (%) |
---|---|---|---|
19.000 | 100.0 | 100.0 | 100.0 |
12.500 | 94.0 | 100.0 | 100.0 |
9.500 | 87.0 | 100.0 | 100.0 |
4.750 | 69.0 | 100.0 | 98.0 |
2.360 | 55.0 | 99.0 | 97.0 |
1.180 | 44.0 | 85.0 | 81.0 |
0.600 | 32.0 | 65.0 | 61.0 |
0.300 | 18.0 | 49.0 | 53.0 |
0.150 | 10.0 | 35.0 | 40.0 |
0.075 | 6.6 | 24.1 | 30.9 |
5. Microstructural Analysis
5.1. Two- and Three-Point Correlations: Theoretical Background
5.2. Digital Image Processing (DIP) Analysis Overview
- First, the volume fraction of the actual air voids content and of the Voids in the Mineral Aggregate (VMA, %) were obtained for each mixture using the experimental data as:
- 2.
- 3.
- Based on the experimental data of Table 1, two threshold values, one for the air voids volume fraction, T1, and one for the mastic volume fraction, T2, were calculated from the converted gray scale images of asphalt mixture slices. The values of T1 and T2 were computed by assuming that the air voids phase takes the darkest pixel range in the gray scale images (Figure 1, Step b).
- 4.
- All the asphalt mixture slices were cut into 240 asphalt mixture BBR beams (8 mixtures × 5 slices × 6 replicates) [46] (Figure 1, Step b). The four largest sides of the BBR mixture beams were then scanned and converted into gray scale images [62]. Therefore, a total of 960 images of asphalt mixture beams were used for DIP analysis (Figure Step c). Finally, based on the threshold values, T1 and T2, previously obtained, three-phase images of asphalt mixture beams were generated; meanwhile the DIP values of VMAd (total VMA,%), Vagg_d (volume fraction of aggregate,%), Vma_d (volume fraction of asphalt mastic,%) and Vair_d (volume fraction of air voids,%) were computed.
Mixture | VMAd, % | Vair_d, % | Vmastic_d, % | VMA* | Vair* | Vmastic* | |||
---|---|---|---|---|---|---|---|---|---|
Ave | CoV | Ave | CoV | Ave | CoV | % | % | % | |
1 | 15.55 | 3.26 | 4.25 | 10.12 | 11.30 | 7.32 | −0.35 | 0.55 | −0.88 |
2 | 14.56 | 3.62 | 4.36 | 4.19 | 10.20 | 4.18 | −0.64 | 0.26 | −0.88 |
3 | 14.94 | 5.03 | 4.71 | 6.13 | 10.22 | 6.94 | −0.36 | 0.61 | −0.94 |
4 | 14.77 | 4.73 | 4.31 | 4.39 | 10.46 | 6.06 | −0.23 | 0.61 | 3.65 |
5 | 14.25 | 6.77 | 4.31 | 5.87 | 9.94 | 10.42 | −1.35 | 0.41 | −1.76 |
6 | 15.08 | 3.70 | 4.11 | 4.46 | 10.97 | 5.55 | −0.82 | 0.51 | −1.31 |
7 | 14.42 | 3.44 | 4.64 | 4.17 | 9.79 | 5.77 | −0.98 | 0.64 | −1.59 |
8 | 14.08 | 2.91 | 4.67 | 6.53 | 9.40 | 6.46 | −0.72 | 0.57 | −1.33 |
5.3. Numerical Solutions of 2- and 3-Point Correlation Functions
5.4. Autocorrelation Length
ID | Material | 2-Point correlation function (mm) | 3-Point correlation function (mm) | ||||
---|---|---|---|---|---|---|---|
Aggregates | Mastic | Air voids | Aggregates | Mastic | Air voids | ||
1 | PG 58-28 Control | 3.97 | 0.59 | 1.02 | 2.33 | 0.40 | 0.88 |
2 | 15% RAP | 3.97 | 0.64 | 1.48 | 2.34 | 0.48 | 1.37 |
3 | 25% RAP | 4.06 | 0.64 | 1.44 | 2.42 | 0.48 | 1.53 |
4 | 30% RAP | 4.09 | 0.66 | 1.51 | 2.45 | 0.49 | 1.61 |
5 | 15% RAP 5% MWSS | 1.57 | 0.25 | 0.72 | 1.37 | 0.22 | 0.73 |
6 | 15% RAP 5% TOSS | 2.75 | 0.42 | 0.76 | 2.10 | 0.32 | 0.78 |
7 | 25% RAP 5% TOSS | 2.84 | 0.47 | 1.14 | 2.18 | 0.40 | 0.97 |
8 | 25% RAP 5% MWSS | 2.03 | 0.30 | 0.76 | 1.69 | 0.28 | 0.76 |
6. Rheological Analysis
6.1. Analogical and Semi Empirical Models
6.2. Back-Calculation
ID | Material | δ | k | h | E∞_binder (MPa) | E∞_mix (MPa) | Log (τbinder) | Log (τmix) | α |
---|---|---|---|---|---|---|---|---|---|
1 | PG 58-28 Control | 6.67 | 0.28 | 0.71 | 2979 | 29,895 | −0.770 | 2.390 | 3.16 |
2 | 15% RAP | 6.45 | 0.25 | 0.58 | 2982 | 29,921 | −0.824 | 3.646 | 4.47 |
3 | 25% RAP | 6.02 | 0.30 | 0.57 | 2985 | 29,926 | −0.658 | 3.492 | 4.15 |
4 | 30% RAP | 6.83 | 0.25 | 0.62 | 2980 | 29,899 | −0.824 | 3.726 | 4.55 |
5 | 15% RAP 5% MWSS | 6.84 | 0.27 | 0.64 | 2988 | 29,931 | −0.509 | 3.591 | 4.10 |
6 | 15% RAP 5% TOSS | 6.85 | 0.25 | 0.66 | 2986 | 29,932 | −0.456 | 4.274 | 4.73 |
7 | 25% RAP 5% TOSS | 6.04 | 0.28 | 0.59 | 2989 | 29,926 | −0.409 | 4.171 | 4.58 |
8 | 25% RAP 5% MWSS | 6.11 | 0.28 | 0.59 | 2991 | 29,962 | −0.495 | 3.965 | 4.46 |
6.3. Comparison with Experimental Data
7. Summary and Conclusions
- Limited variations were observed in the 2- and 3-point correlation functions of the asphalt mixtures; however, the auto correlation length of aggregates, mastic and air voids is significantly influenced by the recycled material used. Nevertheless, while adding reclaimed asphalt pavement increases the auto correlation length, the use of recycled asphalt shingles shorten it, most likely due to the fine particles which are included in the recycled shingles.
- Smaller auto correlation length was observed for manufactured waste scrap shingles compared to tear-off scrap shingles; this indicates that larger representative volume elements are associated with the use of aged shingles.
- Hirsch predictions provide significant overestimations of the creep stiffness of original short-term aged binder and of the seven extracted asphalt binders.
- The creep stiffness of the original binder after short term aging is matched very closely by the ENTPE transformation, while the asphalt binders stiffness curves back-calculated from recycled mixtures data do not match the creep stiffness curves of extracted binders.
- It is hypothesized that blending between new and aged and oxidized binders occurred only partially due to insufficient heat transfer and limited binder contact during mixing and/or to the distribution of the binder film thickness within asphalt mixtures. Therefore, the mixture creep properties were affected by all new binders and only a portion of the old binder. On the other hand, extraction and recovery process resulted in a complete blending which implies reduced relaxation capabilities since all aged binders contributed to the properties of the blend.
- The findings of the present research indicate that the low temperature stiffness properties of asphalt mixture are only partially influenced by the spatial distribution of its components, while the blending process of asphalt binder appears to play a fundamental role when recycled asphalt materials are added to the mixture.
Acknowledgments
Author Contributions
Conflicts of Interest
References
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Falchetto, A.C.; Moon, K.H.; Wistuba, M.P. Microstructural Analysis and Rheological Modeling of Asphalt Mixtures Containing Recycled Asphalt Materials. Materials 2014, 7, 6254-6280. https://doi.org/10.3390/ma7096254
Falchetto AC, Moon KH, Wistuba MP. Microstructural Analysis and Rheological Modeling of Asphalt Mixtures Containing Recycled Asphalt Materials. Materials. 2014; 7(9):6254-6280. https://doi.org/10.3390/ma7096254
Chicago/Turabian StyleFalchetto, Augusto Cannone, Ki Hoon Moon, and Michael P. Wistuba. 2014. "Microstructural Analysis and Rheological Modeling of Asphalt Mixtures Containing Recycled Asphalt Materials" Materials 7, no. 9: 6254-6280. https://doi.org/10.3390/ma7096254