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.

翻译：沥青混凝土（AC）的耐久性与维护需求在很大程度上受其疲劳寿命影响。传统测定该特性的方法既耗费资源又耗时。本研究采用人工神经网络（ANN）来预测AC的疲劳寿命，重点关注应变水平、沥青含量与空隙率的影响。利用大规模数据集，我们定制了模型以有效处理通常以对数尺度表示的宽范围疲劳寿命数据。采用均方对数误差作为损失函数，以提升对所有疲劳寿命水平的预测精度。通过对多种超参数进行比较分析，我们开发了一个能够捕捉数据中复杂关系的机器学习模型。研究结果表明，较高的沥青含量能显著延长疲劳寿命，而空隙率的影响则更具变异性，其具体效应取决于沥青含量水平。最重要的是，本研究深入探讨了使用ANN进行建模的复杂性，展示了其在处理更大数据集时的潜在应用价值。本研究所开发的代码及所用数据已在GitHub仓库开源，论文中附有链接以供完整获取。