MR, rut depth (RD) and CI were measured.
For example, a vehicle wandering in the direction opposite to the dominating/severe rut depth will result in a large rut depth measurement error.
Artificial neural networks are investigated for predicting rut depth on a short ferm basis.
Statistical analysis showed that flatness ratio and elongation ratio had no measurable influence on rut depth.
The aim of this study is to investigate impact of crumb rubber on rut depth reduction.
If the road spacing at the point of minimum cost is less than the road spacing at the maximum allowable rut depth, road spacing is not constrained by rut depth.
The contribution of each pavement structural layer to the total rut depth and the evolution of layer deformation were determined.
A selection of tactical scenarios for each vehicle was used to determine rut depth for a range of vehicle missions.
The model identifies whether the cumulative rut depth caused by the forwarder exceeds the maximum allowable rut depth at the point of or prior to the spacing that minimizes the cost, and if so, the rut depth limits the road spacing.
The results also showed that the reinforced layers achieved maximum rut depth reduction compared to unreinforced section.
