Use of Extreme Value Distributions in Describing the Overloaded Axle Load Data from Pavements

Analysis of overloaded axles has received scant attention during the pavement design process. Such overloaded axles are well known to cause extensive damage to the pavement, though the number of repetitions of such axles is considerably small. This investigation proposes using extreme value distributions to describe the overloaded axles from the axle load data collected from the two National Highways in India. A detailed statistical analysis is carried out under different cases. In the first case, a mixed-normal distribution function is used to describe the entire axle load spectra without explicitly considering the overloading. Different threshold values are chosen for the second and the third cases to consider an appropriate extreme value distribution for analyzing the overloaded data. The right tail region of the axle load data related to overloading is described using Pareto, Burr, and Gamma distributions, while the rest of the data is analyzed using a mixed-normal distribution. The design traffic is estimated for all three cases by calculating the load spectra factor, which uses the moment statistics of the axle load distribution. A significant increase (20%–50%) in the design traffic is observed for the second and third cases.