Stress and damage-sensing capabilities of asphalt mixtures incorporating graphene nanoplatelets

The development of innovative sensing technologies is essential for implementing smart road pavement monitoring systems. The design of asphalt-based materials with intrinsic self-sensing capabilities represents a promising solution in that regard. Despite this, this technology is still not mature and further efforts should be made for its development. With this aim, the present paper evaluates the self-sensing response of asphalt mixtures incorporating graphene nanoplatelets (GNPs), and proposes the use of a digital signal processing technique, based on wavelet transform, for the analysis of the electrical signals generated by the mixture. The results showed that the mixtures exhibited both stress and damage sensing functions, albeit some issues related to the dispersion of GNPs should be further investigated. In addition, wavelet transform analysis seems to be able to capture insightful information about the electrical response of the mixture, as well as its structural condition, useful for traffic and pavement health monitoring purposes.