Abstract
Rutting causes permanent deformation of the asphalt mixture under high temperature and wheel loads, leading to reduced pavement service life and safety. Shear resistance of asphalt mixtures is key for the design against rutting. Existing research primarily focuses on macro-level analyses of rutting resistance, but these methods are often time consuming and costly and do not directly inform the optimisation of asphalt mixture design. Therefore, investigating shear strength parameters at the meso-level is crucial for understanding the mechanisms behind rutting and guiding the optimisation of material design.This study begins with conventional porous asphalt mixtures (PAMs), aiming to correlate meso-level shear parameters (cohesion and friction angle) with the material’s meso-level properties, including that of the aggregate skeleton, mastic, and the interface between mastic and aggregate. Triaxial compressive tests were performed on PAMs at different temperatures, considering various gradations, aggregate types, and binder types to obtain key strength parameters. Meso-level tests, including triaxial tests on the aggregate skeleton, Dynamic Shear Rheometer (DSR) tests on the mastic, and Sessile Drop (SD) tests on both the aggregate and mastic, were then conducted to obtain the meso-level properties. Two correlation formulas were developed to relate these factors to shear strength parameters, offering insights into pavement design.
The shear strength properties of encapsulated porous asphalt mixtures are further studied in a similar way. On one hand, it focuses on how capsules influence the shear strength of the aggregate skeleton; on the other hand, it considers the effect of potential leakage of the rejuvenator from the capsules on the mastic rheology and aggregate capsule adhesion. The previous two shear strength parameter prediction equations are further modified to include the effect of the capsules.
Additionally, the study investigated the effect of aggregate morphology on the shear strength parameters by blending PAMs with crushed glass or glass beads. Two indices, the angularity (AR) and fractal dimension (FD), were incorporated into the prediction equations. The results demonstrate the applicability of the prediction equations from conventional PAMs and innovative PAMs.
The findings presented in this thesis contribute to a deeper comprehension of the mesoscopic factors that impact shear strength. The predictive framework that has been developed provides a more rational base for optimising the design of PAM to counteract rutting. Additionally, it sets the stage for future applications across diverse scenarios.
| Date of Award | 13 Jul 2025 |
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| Original language | English |
| Awarding Institution |
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| Supervisor | Juan Wang (Supervisor), Shu Liu (Supervisor) & Garcia-Hernandez Alvaro (Supervisor) |