Self-sensing asphalt composite with carbon microfibers for smart weigh-in-motion

The need for ageing infrastructure monitoring has recently emerged as an urgent priority in many countries. Smart road infrastructure is a technology that could be used to address this issue by enabling on-time decisions such as condition-based maintenance. For example, automatic traffic monitoring could be beneficial by improving fatigue analysis, and maintenance priority planning and management of overloaded vehicles. This can be done through imagebased traffic monitoring, weight measurements by static scales, and weigh-in-motion (WIM) stations. WIM can be used to identify and classify the type, the number, and the weight of vehicles passing over a given road segment without interrupting the traffic flow. The general drawbacks of existing WIM technologies are their high costs, low durability, and complex deployment. This paper proposes a new asphalt-like composite enabling self-sensing road pavements that can serve as a low-cost and durable WIM sensor. The proposed novel material consists of a commercial binder called EVIzero, doped with natural aggregates and carbon microfibers. These microfibers provide electrical conductivity and piezoresistive properties through electrical percolation. Here, both the material preparation for road applications and its electromechanical characterization are examined. Various cylindrical samples fabricated using different percentages of carbon microfibers were produced and investigated in order to evaluate their signal quality and strain sensing capabilities. It is found that the material mix fabricated with 1% carbon microfibers with respect to the binder weight has the best sensing performance due to electrical percolation. A mid-size slab sample is produced using this optimal mix in order to achieve a preliminary demonstration of material’s feasibility for WIM sensing. The results show that a linear relationship between the electrical response of the slab sample and the induced strain is established with an R2 of 93%