Work

A Double‐T model for rutting performance prediction integrating data augmentation and periodic patterns

Year: 2025

Type: article

Abstract: 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.

Source: Computer-Aided Civil and Infrastructure Engineering

DOI: 10.1111/mice.70066

Authors: Xingyi Zhu, Yanan Wu, Chao Wang, Yicong Hu, Luca Rosafalco, Stefano Mariani

Institutions: Tongji University, Politecnico di Milano

Language: English

Topic: Infrastructure Maintenance and Monitoring

Subfield: Civil and Structural Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Industry, innovation and infrastructure, Sustainable cities and communities

Open Access status: hybrid

Estimated APC paid: $5,230