Abstract:
The longevity and safety of asphalt pavements which form the foundation of
our transportation infrastructure, are directly impacted by their performance.
Pavement performance has traditionally been measured using the Marshall Mix
Design method which is a time and resource-intensive laboratory procedure.
Machine learning algorithms (MLA) are increasingly popular today and are being
utilized in various fields. Their performance varies so evaluating different MLAs
and comparing them is important. The potential of various machine learning (ML)
algorithms to predict Marshall Stability (MS) and Marshall Flow (MF) is
investigated in this work. We collected the data from 11 published literature
encompassing 732 data points to train and evaluate ML models. Eight key input
parameters were considered for modeling. We used three feature importance
analysis techniques (Random Forest, Permutation Importance, and Lasso
Regression) to determine which parameters were the most significant. Linear
regression (LR), Decision Tree (DT), Random Forest (RF), Support Vector
Machines (SVM), Gradient Boosting Machines (GBM), and Artificial Neural
Network (ANN) are the six MLAs that were assessed. Robust statistical measures
such as MSE, MAE, R² and RMSE were employed to evaluate each model’s
performance. Our results exhibited that the RF algorithm top performed for both MS
and MF parameters prediction followed by ANN and DT. The predicted and actual
values showed a strong correlation evidenced by its high R² and lowest values in
other error metrics indicating good performance. This highlights the significance of
selecting the optimal machine-learning algorithm for a particular predictive task.
