A Machine Learning based approach to predict road rutting considering uncertainty

Case Studies in Construction Materials, vol. 20, pp. e03186

Abstract

Roads as vital public assets are the backbone for transportation systems and support constant societal development. Recently, data-driven technologies such as digital twins and especially machine learning have shown great potential to maintain the service level of the existing road infrastructure by accurate future condition modelling and optimal maintenance treatment recommendations. However, the pavement community suffers from inadequate data and errors experienced in data collection, which unavoidably limits machine learning performance. In addition, focusing solely on data without considering the underlying physical behaviour remains as a challenge for the practical implementation of machine learning. To this end, this study provides a machine learning based approach to predict road rutting taking into account the machine learning uncertainties. The US Long-Term Pavement Performance public database has been used as the main data source while supplementary synthetic data was added using Finite Element simulations based on physics. The obtained results indicate that adding extra simulation data improved the model’s short-term prediction accuracy by 4.4% and reduced the long-term prediction uncertainty by 6.76%. The approach could potentially mitigate the issue of lack of data and the uncertainties around the data collected, by integrating existing understanding of pavement physical behaviour into the machine learning modelling pipeline.

Authors 6

1. Ke Chen corresponding

   University of Nottingham · University of Birmingham

   Affiliation as printed

   Nottingham Transportation Engineering Centre, Department of Civil Engineering, University of Nottingham, Nottingham NG7 2RD, United Kingdom

   School of Engineering, University of Birmingham, Birmingham B15 2TT, United Kingdom

2. Mehran Eskandari Torbaghan

   University of Birmingham

   Affiliation as printed

   School of Engineering, University of Birmingham, Birmingham B15 2TT, United Kingdom

3. Nick H. Thom

   University of Nottingham

   Affiliation as printed

   Nottingham Transportation Engineering Centre, Department of Civil Engineering, University of Nottingham, Nottingham NG7 2RD, United Kingdom

4. Álvaro García Aachen Institute of Highway Engineering

   RWTH Aachen University

   Affiliation as printed

   Institute of Highway Engineering, RWTH Aachen University, Mies-van-der-Rohe-Str. 1, Aachen 52074, Germany

5. Asaad Faramarzi

   University of Birmingham

   Affiliation as printed

   School of Engineering, University of Birmingham, Birmingham B15 2TT, United Kingdom

6. David Chapman

   University of Birmingham

   Affiliation as printed

   School of Engineering, University of Birmingham, Birmingham B15 2TT, United Kingdom