Equivalent Axle Load Factor Prediction Based on Artificial Neural Networks

Abstract

Lack of accurate knowledge of pavement behavior under moving loads is the one of the most important disadvantages in calculation of Equivalent Axle Load Factor (EALF) in roads pavement. Among the many researches, the most comprehensive method is based on the AASHTO road test. As the main weakness of this method, the results are limited to the experimented axles, which makes it impossible to determine the EALF for all existing axles, hence reducing the accuracy of the results, causing premature failure, and leading to higher maintenance costs. Today, although numerous software packages are available for EALF calculation, they require various parameters, are time-consuming, and can only simulate one section at a time . On the other hand, artificial neural networks, as an artificial intelligence subcategory, have many advantages such as reduced input data, increased modeling process speed, ability of parallel modeling of several pavements with different conditions, etc. In this paper, after verifying the simulation of flexible pavements in ABAQUS, a model based on Artificial Neural Network (ANN) was presented to calculate EALF using the back-propagation architecture. Finally, from among the reviewed ANN configurations, the network with the 7-13-1 architecture incorporating the sigmoid function was selected as the optimum network.

Keywords

• equivalent axle load factor (ealf)
• artificial neural networks
• finite element
• abaqus
• flexible pavements