Practical numerical tool for marshall stability prediction based on machine learning: an application for asphalt concrete containing basalt fiber

Abstract

Marshall stability (MS) is used to evaluate the resistance to settlement, deformation and displacement of asphalt concrete. However, these experiments are complex, expensive and time-consuming. Therefore, it is important to develop an alternative method to quickly determine these parameters. This paper presents a comprehensive investigation into applying machine learning techniques for predicting the MS of basalt fiber asphalt concrete. The study leverages the Gradient Boosting algorithm to establish predictive models. A database containing 128 samples is employed as the foundation for model construction. Additionally, SHAP analysis is employed to reveal the underlying variables influencing the predictive outcomes. To extend the practicality of the findings, a Graphical User Interface (GUI) is developed to facilitate easy access to the predictive tool for material engineers. The results show that the content aggregate 4.75mm is the most influential variable, followed by the content aggregate 2.36mm, the content of fiber, the content of binder, and the content aggregate 9.5mm in descending order of impact.