Version 1
: Received: 9 October 2024 / Approved: 11 October 2024 / Online: 14 October 2024 (03:34:34 CEST)
How to cite:
Asi, I.; Alhadid, Y.; Alhadidi, T. Predicting Marshall Stability and Flow Parameters in Asphalt Pavements Using Explainable Machine-Learning Models. Preprints2024, 2024100854. https://doi.org/10.20944/preprints202410.0854.v1
Asi, I.; Alhadid, Y.; Alhadidi, T. Predicting Marshall Stability and Flow Parameters in Asphalt Pavements Using Explainable Machine-Learning Models. Preprints 2024, 2024100854. https://doi.org/10.20944/preprints202410.0854.v1
Asi, I.; Alhadid, Y.; Alhadidi, T. Predicting Marshall Stability and Flow Parameters in Asphalt Pavements Using Explainable Machine-Learning Models. Preprints2024, 2024100854. https://doi.org/10.20944/preprints202410.0854.v1
APA Style
Asi, I., Alhadid, Y., & Alhadidi, T. (2024). Predicting Marshall Stability and Flow Parameters in Asphalt Pavements Using Explainable Machine-Learning Models. Preprints. https://doi.org/10.20944/preprints202410.0854.v1
Chicago/Turabian Style
Asi, I., Yusra Alhadid and Taqwa Alhadidi. 2024 "Predicting Marshall Stability and Flow Parameters in Asphalt Pavements Using Explainable Machine-Learning Models" Preprints. https://doi.org/10.20944/preprints202410.0854.v1
Abstract
The traditional method for determining the Marshall stability (MS) and Marshall flow (MF) of asphalt pavements is laborious, time consuming, and costly. This study aims to predict these parameters using explainable machine-learning techniques. A comprehensive database comprising 721 hot mix asphalt (HMA) data points was established, including variables such as aggregate percentage, asphalt content, and specific gravity. Models were constructed using the PyCaret Python library, and their performance was assessed using metrics such as the mean absolute error (MAE) and coefficient of determination (R²). The CatBoost regression model outperformed the other models, achieving R² values of 0.835 and 0.845 for MS and MF, respectively. Additionally, Shapley values were used to quantify the variable effects on the predictions. This approach enables the efficient preselection of design variables, reducing the need for extensive laboratory testing and promoting sustainable construction practices.
Engineering, Transportation Science and Technology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
