Equivalent Axle Load Factor Prediction Based on Artificial Neural Networks

Document Type : Civil Article

Authors

1 Payam Noor University

2 Professor Assistant, Department of Engineering, Payame Noor University, PO BOX 19395-3697 Tehran, IRAN, mrkeymanesh@pnu.ac.ir

3 Ferdowsi University, Mashhad, Iran

4 Civil Department, Payame Noor University, Mashhad, Iran.

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

References

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Journal of Modeling in Engineering
Volume 19, Issue 65
June 2021
Pages 149-160

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History

  • Receive Date: 13 September 2020
  • Revise Date: 01 December 2020
  • Accept Date: 30 January 2021

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