  Asphalt concrete's (AC) durability and maintenance demands are strongly
influenced by its fatigue life. Traditional methods for determining this
characteristic are both resource-intensive and time-consuming. This study
employs artificial neural networks (ANNs) to predict AC fatigue life, focusing
on the impact of strain level, binder content, and air-void content. Leveraging
a substantial dataset, we tailored our models to effectively handle the wide
range of fatigue life data, typically represented on a logarithmic scale. The
mean square logarithmic error was utilized as the loss function to enhance
prediction accuracy across all levels of fatigue life. Through comparative
analysis of various hyperparameters, we developed a machine-learning model that
captures the complex relationships within the data. Our findings demonstrate
that higher binder content significantly enhances fatigue life, while the
influence of air-void content is more variable, depending on binder levels.
Most importantly, this study provides insights into the intricacies of using
ANNs for modeling, showcasing their potential utility with larger datasets. The
codes developed and the data used in this study are provided as open source on
a GitHub repository, with a link included in the paper for full access.
