Speaker
Description
Abstract. After the success of the electric cars, the future will be based on Connected and Autonomous Vehicles (CAVs) that will change the actual driving mode. CAVs will also change the design of road pavements because their transverse position in the lane produces different distresses compared to the actual cars due to CAVs ability to keep their transverse position con-stantly in the lane, eliminating lateral wandering and inducing more damage to pavements than conventional vehicles. It is crucial to evaluate how the wander of CAVs affects pavement performance for the definition of mainte-nance and rehabilitation policies. This paper analyses how the lateral wan-der of CAVs affects pavement performance in terms of fatigue cracking and permanent deformation. These results can be used to establish the values for the wander to minimize those distresses in road pavements. This work was carried out for a pavement with three layers, changing the thickness of the asphalt layer and the stiffness of the subgrade. The wander varied from 0.2 to 0.6 m, applying a normal and a uniform distribution. The variation of the pavement life due to the wander was modeled using Artificial Neural Net-works (ANN) and models were developed for these two distributions and for cracking and permanent deformation distresses. It was concluded that a significant damage reduction occurred as the wander increased, and the probability distribution also shaped the damage profiles. The developed ANN models showed that they are suitable tools for predicting the perfor-mance of pavement subject to any wheel wander.
