Evaluation of soft computing techniques for predicting Marshall Stability of waste plastic-reinforced asphalt concrete

ABSTRACT

Pavement engineering has long prioritized enhancing the quality and durability of asphalt concrete, a foundational material in road construction. This study employs advanced soft computing techniques to predict the Marshall Stability (MS) of asphalt concrete reinforced with waste plastic. Techniques such as Artificial Neural Network (ANN), Random Forest (RF), Random Tree (RT), Support Vector Machine (SVM), and Bagging RT are utilized. Evaluation of model effectiveness is conducted using seven statistical metrics: coefficient of correlation (CC), root mean square error (RMSE), mean absolute error (MAE), relative absolute error (RAE), root relative squared error (RRSE), mean absolute percentage error (MAPE), scatter index (SI), and comprehensive measure (COM). Among the applied models, the Bagging RT model emerges as the top performer, exhibiting superior performance across multiple metrics. Specifically, the Bagging RT model achieves impressive CC values of 0.921 and 0.834, indicating strong correlations between predicted and actual MS values. Additionally, it demonstrates low error metrics, with RMSE values of 1.632 and 2.869, and MAE values of 1.207 and 2.081, respectively. A sensitivity analysis conducted for the Bagging RT-based model underscores the significant influence of aggregate size on MS prediction, highlighting the model’s capability to elucidate critical factors shaping material stability.

KEYWORDS:

• Artificial Neural Network
• Random Forest
• Random Tree
• Support Vector Machine
• Marshall Stability

Acknowledgments

The authors gratefully acknowledge the School of Core Engineering, Shoolini University Solan for providing the necessary facilities related to the study.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Author contributions

Conceptualization: [Bhupender Kumar]; Methodology: [Bhupender Kumar, Navsal Kumar]; Formal analysis and investigation: [Bhupender Kumar]; Writing – original draft preparation: [Bhupender Kumar, Navsal Kumar]; Writing – review and editing: [Bhupender Kumar]; Resources: [Bhupender Kumar, Vikas Mehta]; Supervision: [Navsal Kumar, Vikas Mehta].

Data availability statement

Some data or models used during the study are available from the corresponding author by request.

Supplementary material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/24705314.2024.2371038.