Machine Learning-Based Prediction Performance Comparison of Marshall Stability and Flow in Asphalt Mixtures

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 (MLAs) are increasingly popular today and are being utilized in various fields. Their performances vary; therefore, 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) was investigated in this work. We collected data from published studies in the 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 (SVMs), Gradient Boosting Machines (GBMs), and Artificial Neural Networks (ANNs) were 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 indicate that the RF algorithm had the best performance for both MS and MF parameter prediction, followed by ANN and DT. The predicted and actual values showed a strong correlation, which was evidenced by the high R2 and the lowest values in other error metrics, indicating good performance. This highlights the significance of selecting an optimal machine learning algorithm for a particular predictive task.