A Double‐T model for rutting performance prediction integrating data augmentation and periodic patterns
Xingyi Zhu, Yanan Wu, Chao Wang, Yang Hu, Luca Rosafalco, Stefano Mariani · Computer-Aided Civil and Infrastructure Engineering · 2025
Accurate rutting prediction is crucial for traffic safety and road maintenance, enabling timely interventions and cost-effective strategies. Such prediction remains challenging, especially with limited data across road segments. As traditional methods struggle in the case of data scarcity and complexity, in this study, a Double-T model is developed by merging TimeGAN and TimesNet. TimeGAN is used to augment the dataset from 2925 to 7578 records, while TimesNet is applied to capture multi-scale periodic features. The model has achieved determination coefficients (R2) of 0.939, 0.935, and 0.893 for the training, validation, and testing subsets of the dataset. Increasing the prediction intervals has led to a decline in the model performance. Comparative experiments demonstrate the superior performance of the Double-T model over conventional regression models, pinpointing that key factors influencing rutting include pavement age, traffic load, pavement thickness and temperature.