Machine learning approaches for predicting Cracking Tolerance Index (CTIndex) of asphalt concrete containing reclaimed asphalt pavement.

ABSTRACT: One of the various sorts of damage to asphalt concrete is cracking. Repeated loads, the deterioration or aging of material combinations, or structural factors can contribute to the development of cracks. Asphalt concrete's crack resistance is represented by the CT index. 107 CT Index data samples from the University of Transport Technology's lab are measured. These data samples are used to establish a database from which a Machine Learning (ML) model for predicting the CT Index of asphalt concrete can be built. For creating the highest performing machine learning model, three well-known machine learning methods are introduced: Random Forest (RF), K-Nearest Neighbors (KNN), and Multivariable Adaptive Regression Spines (MARS). Monte Carlo simulation is used to verify the accuracy of the ML m

SUBMITTER: Nguyen LN 

PROVIDER: S-EPMC10602248 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature