Prediction of Marshall design parameters of asphalt mixtures via machine learning algorithms based on literature data

Previous studies have achieved accurate predictions for Marshall design parameters (MDPs), but their limited data and input variables might restrict generalization. In this study, machine learning (ML) was used to predict MDPs with more generalised models. To achieve this, a dataset was collected from six different papers. Inputs were material properties and their ratios in the mixture, while target features were six MDPs used in mixture design. Four ML algorithms were used including linear regression, polynomial regression, k nearest neighbour (KNN) and support vector regression (SVR). Also, the cross-validation (CV) method was used to detect the generalisation capability of the models. Accuracy of the SVR was the highest, however, in nested CV its performance was highly reduced. Therefore, KNN was recommended due to its second highest performance. The results demonstrated that prediction of MDPs from only material properties is possible and promising to use in mixture design.Abbreviations: ANN: artificial neural network; BC: bitumen content; BP: bitumen penetration (1/10 mm); CV: cross-validation; DEM: discrete element method; GA: genetic algorithm; Gmb: Bulk specific gravity of mixture; Gmm: Maximum specific gravity of mixture; Gsb: bulk specific gravity of aggregate; KNN: k nearest neighbour; LA: Los Angeles abrasion; LR: linear regression; MARS: multivariate adaptive regression spline; MDP: Marshall design parameter; MF: Marshall flow; MQ: Marshall quotient (kN/mm); MS: Marshall stability; NMAS: nominal maximum aggregate size; NoB: number of blows; PI: penetration index; PR: polynomial regression; R2: coefficient of determination; SP: softening point (°C); SVR: support vector regression; UPVT: ultrasonic pulse velocity–time; Va: air voids percentage; VFA: voids filled with asphalt; VMA: voids in mineral aggregate; WA: water absorption.