A model for equivalent axle load factors

Abstract

Most design methods for road pavements require the design traffic, based on the transformation of the traffic spectrum, to be calculated into a number of equivalent passages of a standard axle using the equivalent axle load factors (EALFs). In general, these factors only consider the type of axle (i.e. single, tandem or tridem), but they do not consider the type of wheel on the axles, i.e. single or dual wheel. The type of wheel has an important influence on the calculation of the design traffic. The existing design methods assume that the EALFs are valid for all pavement structures and do not consider the thickness and stiffness of the pavement layers. This paper presents the results of the development of a model for the calculation of the EALFs considering the type of axle, the type of wheel and the constitution of the pavement. The model was developed based on the tensile strain at the bottom of the asphalt layer that is responsible for bottom-up cracking in asphalt pavement, which is the most widely considered distress mode for flexible road pavements. The work developed in this study also presents the influence of the type of wheel (single and dual) on pavement performance. The results of this work allowed the conclusion that the EALFs for single wheels are approximately 10 times greater than those for a dual wheel. This work also proposes average values for the EALFs. An artificial neural network was developed to calculate the EALFs.

Influence of Temperature and Moisture Content on Pavement Bearing Capacity with Improved Subgrade

“…The bearing capacity of pavement is defined as a characteristic of its structural state, related to the capacity to support the expected traffic. The evaluation of this capacity can be made through parameters such as residual life, required thickness for reinforcement, stiffness modulus and deflection [2].…”

Revealing the impact of losses on flexible pavement due to vehicle overloading

“…The investigation was carried out on Indonesia's national highways, and Table 1 presents the prescribed upper limit for the load capacity of each truck category. The data on a load of heavy vehicles was converted into the equivalent standard axle load (ESAL) using the model developed by [22,23], which will be combined to obtain the Truck Factor for each vehicle and then calculate how this damage affects the flexible pavement using equation (1):…”

Artificial Neural Networks for Flexible Pavement

“…Amorim et al [7] reported a multi-layer perceptron neural network (MLPNN) with two concealed layers for determining the ratio of the influence of various configurations of axle loads. Since the existing mechanical-empirical equation is very complicated for the design equivalent single axle load (ESAL) evaluation, an ANN was developed for the computation of EALFs.…”