Features Importance and Their Impacts on the Properties of Asphalt Mixture Modified with Plastic Waste: A Machine Learning Modeling Approach

Abstract

Plastic asphalt mixtures (PAM) have attracted extensive attention lately; however, their application in the field has not been so common because of the lack of clear understanding of the behavior of the asphalt mix after modification. In the interest of closing this gap, a modeling tool able to estimate the plastic effect on asphalt mixtures properties is needed. Nevertheless, the suggestion of a generalized model is complex due to the numerous variables involved. To facilitate this process, the present article aims to expand the current knowledge about PAM modeling by providing a clearer understanding of what variables have the highest impact on PAM properties. To do so, data from previous articles have been gathered and machine learning and shapley additive explanation values have been applied. PAM properties assessed were air voids, Marshall stability, Marshall flow, indirect tensile strength, and tensile strength ratio. Overall, the features with the highest impact are plastic type and content (35%), aggregates gradation (35%), aggregates absorption (9%), bitumen content (8%), mixing technique (4%), and bitumen penetration (3%). The final proposed models extend the application of previous machine learning models and feature importance understanding, and, in the field, they can serve as an initial estimate of the plastic effect on different asphalt mixture types. It is suggested that future articles intending to model PAM should consider these critical features during model formulation, and articles evaluating new types of PAM should clearly report these properties, for they might be the basis of these generalized future models.