Over the last 15 years or so, research has revealed the great self-healing prospects possessed by asphaltic mixtures. Researchers have proposed novel methods to harness this capability, aiming to prolong the service life of asphalt pavement, particularly in porous asphalt. To date, the most promising of the healing methods is the combined capsule-induction system. This thesis aims to ascertain whether such a system would show positive results in stone mastic asphalt (SMA). Following that, an optimisation of the composition of self-healing SMA was proposed by assessing the mechanical and healing properties via laboratory testing. Finally, an evaluation of sustainability from an environmental perspective was done using Life Cycle Analysis (LCA) methodology.

Results of the healing assessment revealed that each combined healing system was able to recover between 58-63\% of its original fracture strength after 8 healing cycles, while the reference mix (without healing) was only able to regain 10\% fracture strength before failure after 2 cycles.

Inclusion of the combined healing system slightly reduced the strength, stiffness and water sensitivity of the SMA mixture compared to the reference. However, improved rutting resistance was observed in each self-healing case. Within the self-healing mixtures, increasing capsule content reduced asphalt density, stiffness and strength and resulted in an increase in asphalt void content.

The LCA results show that the self-healing system had environmental benefits in some facets such as a 14\% reduction in fossil fuel resource depletion and a 21\% reduction in land use. However, the present total known environmental costs of other impacts are approximately 15\% lower in the reference system based on a cradle to gate, and use phase analysis. Almost half of this total cost was attributed to maintenance activities. It was concluded that a 32\% increase in maintenance efficiency would ensure environmental viability of a self-healing mixture over a reference mixture within the constraints of the analysis conducted.