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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.

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Authors

K. Chen

M. Eskandari Torbaghan

Profile image of NICK THOM

Dr NICK THOM NICHOLAS.THOM@NOTTINGHAM.AC.UK
ASSISTANT PROFESSOR

A. Garcia-Hernández

A. Faramarzi

D. Chapman



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.

Citation

Chen, K., Torbaghan, M. E., Thom, N., Garcia-Hernández, A., Faramarzi, A., & Chapman, D. (2024). A Machine Learning based approach to predict road rutting considering uncertainty. Case Studies in Construction Materials, 20, Article e03186. https://doi.org/10.1016/j.cscm.2024.e03186

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

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