Development of a novel ensemble learning model for predicting asphalt volumetric properties using experimental data for pavement performance assessment.

ABSTRACT: The study is conducted across the board from laboratory experiment to the practical use of a cutting-edge machine learning algorithm, aiming to predict the behavior of asphalt mixtures, the focus being on three aspects: Aggregate Void Percentage (AVP), Percentage of Voids Filled with Bitumen (PVFB), and Percentage of Voids in the Marshall Sample (PVMS). Approximately 200 road surface samples from Ardabil city in Iran were taken and characterized based on 11 influential features, including Optimum Bitumen Percentage (OBP), Specific Gravity of Aggregates (SGA), Asphalt and Ambient Temperatures (Aste, AmTe), Fracture Resistance, Softness, and Density. The work was complemented by employing two regression algorithms, namely XGBoost and LightGBM, on the experimental data to forecast volumetric

SUBMITTER: Khiavi AJ 

PROVIDER: S-EPMC12630654 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature