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）的耐久性和维护需求受其疲劳寿命的显著影响。传统确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（ANNs）预测AC疲劳寿命，重点关注应变水平、沥青含量和空隙率的影响。基于大规模数据集，我们针对通常以对数尺度表示的宽范围疲劳寿命数据优化了模型。采用均方对数误差作为损失函数，以提升各疲劳寿命区间的预测精度。通过对比分析多种超参数，我们开发了能够捕捉数据中复杂关系的机器学习模型。研究结果表明，较高沥青含量显著延长疲劳寿命，而空隙率的影响则随沥青含量的变化呈现较大波动。最重要的是，本研究揭示了使用ANNs进行建模的复杂性，展示了其在大规模数据集上的应用潜力。本研究开发的代码及使用的数据已作为开源资源上传至GitHub仓库，论文中附有完整访问链接。