Predicting the fatigue life of asphalt concrete using neural networks

TL;DR

This paper demonstrates that neural networks can effectively predict asphalt concrete fatigue life, revealing key factors like binder content and air-voids, and offers an open-source tool for improved durability assessment.

Contribution

The study introduces a neural network model tailored for predicting asphalt fatigue life, utilizing a large dataset and logarithmic error metrics for enhanced accuracy, which is a novel application in this context.

Findings

Higher binder content improves fatigue life.

Air-void content's effect varies with binder levels.

Neural networks can model complex relationships in asphalt data.

Abstract

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.