Journal of Transportation Research

Journal of Transportation Research

Machine Learning-Based Prediction of Unconfined Compressive Strength and Feature Importance Analysis of Mixture Design and Construction Parameters in Cement-Treated Base Mixtures Containing Reclaimed Asphalt Pavement

Document Type : Original Article

Author
Assistant Professor, Road, Housing and Urban Development Research Center, Tehran, Iran.
Abstract
In this study, machine learning techniques were employed to predict and analyze the unconfined compressive strength (UCS) of cement-treated base (CTB) mixtures containing 100% reclaimed asphalt pavement (RAP). A comprehensive experimental database comprising 540 UCS specimens with different curing ages was established. The specimens were prepared using three cement contents (4%, 5%, and 6%), three moisture contents (5.5%, 6.5%, and 7.5%), and two compaction methods in accordance with ASTM D1557 and ASTM D558. In addition, a polymer–mineral additive (Nicoflok) was incorporated at 10% of the cement weight to enhance the mechanical performance of the mixtures. The predictive performance of several machine learning algorithms, including Random Forest (RF), Linear Regression (LR), Support Vector Regression (SVR), K-Nearest Neighbors (KNN), Multi-Layer Perceptron (MLP), and Self-Attention-enhanced MLP, was evaluated for UCS prediction. The results demonstrated that the developed machine learning models were capable of predicting UCS with satisfactory accuracy, and the predicted values exhibited good agreement with the experimental measurements. Furthermore, the effects of the mixture design and construction variables on UCS were investigated using three feature importance analysis techniques, namely SHAP, Permutation Importance, and Gradient Boosting Feature Importance. The results consistently identified curing age as the most influential parameter affecting UCS, followed by cement content, while the presence of the additive, moisture content, and compaction method ranked as the third, fourth, and fifth most influential factors, respectively. Unlike many previous studies that primarily focused on UCS prediction, this study also provides a comprehensive interpretation of the relative importance of the input variables using three complementary feature importance techniques. The consistent ranking of the input variables obtained from these methods further confirms the robustness and reliability of the proposed machine learning framework for both prediction and interpretation of UCS behavior in cement-treated RAP mixtures.
Keywords
Subjects

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