Utilizing Machine Learning to Minimize Sample Height Errors in Marshall Asphalt Mixture Design Method

Muhammed Yasin Codur; Halis Bahadir Kasil; Emre Kuskapan

doi:10.48084/etasr.9664

Authors

Muhammed Yasin Codur College of Engineering and Technology, American University of the Middle East, Kuwait
Halis Bahadir Kasil Department of Research and Development, Republic of Turkey-General Directorate of Highways, Erzurum, Turkiye
Emre Kuskapan Department of Civil Engineering, Faculty of Engineering and Architecture, Erzurum Technical University, Erzurum, Turkiye

Volume: 15 | Issue: 4 | Pages: 24511-24515 | August 2025 | https://doi.org/10.48084/etasr.9664

Received: 21 November 2024 | Revised: 12 December 2024, 27 January 2025, and 2 March 2025 | Accepted: 6 March 2025 | Online: 12 June 2025

Corresponding author: Muhammed Yasin Codur

Abstract

In this study, a Machine Learning (ML) method was utilized to predict whether the sample heights of the briquettes prepared during the hot mix asphalt design step (Marshall method) will be within the tolerances specified in ASTM D 6927 standard. Factors affecting the sample height were analyzed using a multilayer perceptron algorithm. In the analysis process, the sample heights of the road layers consisting of base, binder, wearing, and SMA wearing layers were estimated with an accuracy of about 90% or more, demonstrating the high accuracy of the model. As a result, there is high possibility of utilizing ML to prepare asphalt specimens within the required height range.

Keywords:

specimen height, optimum bitumen ratio, machine learning, pavement design

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