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- Creators: Ford, Emily Lucile
- Creators: Civil, Environmental and Sustainable Engineering Programs
- Creators: Kaloush, Kamil
- Status: Published
This study investigates the mastic level structure of asphalt concrete containing RAP materials. Locally sourced RAP material was screened and sieved to separate the coated fines (passing #200) from the remaining sizes. These binder coated fines were mixed with virgin filler at proportions commensurate with 0%, 10%, 30%, 50% and 100% RAP dosage levels. Mastics were prepared with these blended fillers and a PG 64-22 binder at a filler content of 27% by volume. Rheological experiments were conducted on the resulting composites as well as the constituents, virgin binder, solvent extracted RAP binder. The results from the dynamic modulus experiments showed an expected increase in stiffness with increase in dosage levels. These results were used to model the hypothesized structure of the composite. The study presented discusses the different micromechanical models employed, their applicability and suitability to correctly predict the blended mastic composite. The percentage of blending between virgin and RAP binder estimated using Herve and Zaoui model decreased with increase in RAP content.
ML algorithms for classification of cementitious phases are found to require only the intensities of Ca, Si, and Al as inputs to generate accurate predictions for more homogeneous cement pastes. When applied to more complex UHP systems, the overlapping chemical intensities in the three dominant phases – Ultra High Stiffness (UHS), unreacted cementitious replacements, and clinker – led to ML models misidentifying these three phases. Similarly, a reduced amount of data available on the hard and stiff UHS phases prevents accurate ML regression predictions of the microstructural phase stiffness using only chemical information. The use of generic virtual two-phase microstructures coupled with finite element analysis is also adopted to train MLs to predict composite mechanical properties. This approach applied to three different representations of composite materials produces accurate predictions, thus providing an avenue for image-based microstructural characterization of multi-phase composites such UHP binders. This thesis provides insights into the microstructure of the complex, heterogeneous UHP binders and the utilization of big-data methods such as ML to predict their properties. These results are expected to provide means for rational, first-principles design of UHP mixtures.