An efficient conditional GAN-based framework for high-resolution prediction of tyre-pavement contact stresses – a contribution towards a digital twin of the road system

International Journal of Pavement Engineering, vol. 25

Abstract

The high-resolution prediction of tyre-pavement contact stresses is crucial for advancing pavement design and vehicle safety. A significant knowledge gap exists in accurately and quickly predicting tyre-pavement contact stresses, especially under varying conditions. Addressing this challenge, this study is part of the concerted effort within the project CRC/TRR 339, contributing to the series ‘A Contribution towards a Digital Twin of the Road System’. It aims to bridge this gap by introducing a novel Multimodal Conditional Deep Convolutional Generative Adversarial Network (MC-DCGAN) model for forecasting tyre-pavement contact stresses, offering an efficient and accurate method. The incorporation of a multimodal conditional module within the GAN-based framework allows for controllable outputs tailored to specific tyre types, inflation pressures, and loads. Leveraging the DCGAN architecture significantly enhances prediction accuracy. Experimental validation confirms the method’s high accuracy, strong generalisation capabilities, and a substantial reduction in computational time – nearly two orders of magnitude lower compared to traditional Finite Element simulations. This research not only fills a critical void in the high-precision prediction of tyre-pavement contact stresses, but also holds significant implications for optimising pavement design and enhancing road traffic safety, marking a significant step towards realising a digital twin of the road system.

Authors 5

1. Pengfei Liu corresponding Aachen Institute of Highway Engineering

   RWTH Aachen University

   Affiliation as printed

   Institute of Highway Engineering, RWTH Aachen University, Aachen, Germany

2. Hancheng Zhang corresponding Aachen Institute of Highway Engineering

   Southeast University · RWTH Aachen University

   Affiliation as printed

   Institute of Highway Engineering, RWTH Aachen University, Aachen, Germany

   Intelligent Transportation System Research Center, Southeast University, Nanjing, People’s Republic of China

   Intelligent Transportation System Research Center, Southeast University, Nanjing, People's Republic of China

3. Yuanyuan Hu Aachen Institute of Highway Engineering

   RWTH Aachen University

   Affiliation as printed

   Institute of Highway Engineering, RWTH Aachen University, Aachen, Germany

4. Jinchao Guan Aachen Institute of Highway Engineering

   RWTH Aachen University

   Affiliation as printed

   Institute of Highway Engineering, RWTH Aachen University, Aachen, Germany

5. Ventseslav Yordanov Aachen Institute for Automotive Engineering

   RWTH Aachen University

   Affiliation as printed

   Institute for Automotive Engineering, RWTH Aachen University, Aachen, Germany