A Machine Learning based approach to predict road rutting considering uncertainty

Chen, K.; Torbaghan, M. Eskandari; Thom, N.; Garcia-Hernández, A.; Faramarzi, A.; Chapman, D.

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.

• Journal Article Type: Article
• Acceptance Date: Apr 19, 2024
• Online Publication Date: Apr 23, 2024
• Publication Date: 2024-07
• Deposit Date: Jul 8, 2024
• Publicly Available Date: Jul 8, 2024
• Journal: Case Studies in Construction Materials
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 20
• Article Number: e03186
• DOI: https://doi.org/10.1016/j.cscm.2024.e03186
• Public URL: https://nottingham-repository.worktribe.com/output/34317653
• Publisher URL: https://www.sciencedirect.com/science/article/pii/S2214509524003371?via%3Dihub