Over the years, different methodologies, e.g., encapsulated rejuvenators [1], have been investigated to improve road service life and reduce maintenance requirements [2]. However, these capsules can affect the mechanical behaviour of asphalt mixtures and assessing their accurate effects through experimental methods is challenging, especially when verifying the isolated effect without considering rejuvenator release. But can numerical models predict their impact on the mechanical response of asphalt mixtures? This study adopts the discrete-based VirtualPM3DLab programme [3] to evaluate the effect of capsules on the stiffness behaviour and tensile strength of asphalt mixtures. Asphalt mixtures incorporating different capsule ratios (0.30, 0.75, and 1.20 wt.%) are subjected to tension-compression and monotonic tensile tests on notched specimens. The results show that the effect on the stiffness modulus progressively increases as the capsule amount grows, reaching levels between 4.3% and 12.4%, while the phase angle is not significantly affected. The capsule modulus shows no significant effect on the stiffness behaviour of asphalt mixtures during dynamic simulations, suggesting that the design parameters of these elements have only a minor influence. Finally, the capsule effect on the strength has a reduced impact on the peak stress, with a reduction of around 12% for the highest capsule content. At the same time, the post-peak response follows a similar softening tendency regardless of the specimen. Most damaged contacts are located between the notch tips, which include aggregate-mastic and mastic-mastic contacts. All contacts that involve capsules cracked during simulations are localised in the notch tip region, showing that simulations considering the rejuvenator effect would possibly influence the recovery of damaged contacts. These findings suggest that the capsules within the studied contents do not have a meaningful impact on the mechanical behaviour of asphalt mixtures. These contents can be safely adopted without compromising their properties. REFERENCES [1] Câmara, G., Micaelo, R., and Azevedo, N.M., 2023. 10.1007/s40571-023-00574-1. [2] Kargari, A., Arabani, M., and Mirabdolazimi, S.M., 2022. 10.1016/j.conbuildmat.2021.125901. [3] Câmara, G., 2024. Computational tool for self-healing asphalt mixture modeling, NOVA School of Science and Technology | NOVA University Lisbon, Portugal.