Abstract
The primary objective of this study is to investigate the effect of in-place air voids (AV), asphalt content (AC), bulk-specific gravity (BSG), and maximum specific gravity of asphalt (Gmm) on the fatigue cracking of asphalt mixtures using the Long-Term Pavement Performance (LTPP) database. This study includes 13 sections from different locations covering different mix designs, pavement age between 20-30 years, and two climate zones across the United States. All data were derived from LTPP database. A multiple linear regression, random forest (RF) and support vector machine (SVM) methods between the selected explanatory properties and the fatigue cracking were used for the investigation. A multiple significant linear regression model was developed, and it confirmed the significant relationships between the AC, AV, Gmm, percent of aggregate Pass.No.200, BSG and the fatigue cracking. RF and SVM methods were established using the same data. They validated significant properties and the accuracy of the model.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
