Prediction of the optimum asphalt content using artificial neural networks

Authors

  • Kareem Mohamed Mousa Othman Civil engineering department, University of Toronto, 35 St George St, Toronto, Canada https://orcid.org/0000-0002-5322-0918
  • Hassan Abdelwahab Public works department, Faculty of engineering, Cairo University, Giza, Egypt

DOI:

https://doi.org/10.30544/579

Keywords:

Artificial Neural Networks, Asphalt mix design, Early Stopping Technique, Machine Learning, Marshall Design, Optimum asphalt content

Abstract

The performance of the asphalt mix is significantly influenced by the optimum asphalt content (OAC). The asphalt content is responsible for coating the aggregate surface and filling the voids between the aggregate particles. Thus, the aggregate gradation has a significant influence on the required asphalt content. The Marshall design process is the most common method used for estimating the OAC, and this process is called the asphalt mix design. However, this method is time consuming, labor intensive, and its results are subjected to variations. Thus, this paper employs the artificial neural network (ANN) to estimate the OAC from the aggregate gradation for the two most common gradations used in asphalt mixes in Egypt (3D, 4C). Results show that the proposed ANN can predict the OAC with a coefficient of correlation of 0.98 and an average error of 0.026%. As a result, a new approach for the Marshall test can be adopted using results of the proposed ANN, and only three specimens, instead of fifteen, are prepared and tested for estimating the remaining parameters. This approach saves the time, effort, and resources required for estimating the OAC. Additionally, the ANN was validated with previously developed models, and the ANN shows promising results.

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Mardhall test ANN

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Published

2021-04-28

How to Cite

Othman, Kareem Mohamed Mousa, and Hassan Abdelwahab. 2021. “Prediction of the Optimum Asphalt Content Using Artificial Neural Networks”. Metallurgical and Materials Engineering 27 (2):227-42. https://doi.org/10.30544/579.

Issue

Vol. 27 No. 2 (2021)

Section

Research

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