Identifying the most suitable machine learning approach for a road digital twin: a systematic literature review

Road infrastructure systems have been suffering from ineffective maintenance strategies, exaggerated by budget restrictions. A more holistic road asset management approach enhanced by data-informed decision making through effective condition assessment, distress detection, future condition predictions can significantly enhance maintenance planning, prolonging asset life. Recent technology innovations such as Digital Twins have great potentials to enable the needed approach for road condition predictions and a proactive asset management. To this end, machine learning techniques have also demonstrated convincing capabilities in solving engineering problems. However, none of them has been considered specifically within digital twins context. There is therefore a need to review and identify appropriate approaches for the usage of machine learning techniques within road digital twins. This paper provides a systematic literature review of machine learning algorithms used for road condition predictions and discusses findings within the road digital twin framework. The results show that existing machine learning approaches are to some extent, suitable and mature to stipulate successful road digital twin development. Moreover, the review whilst identifying gaps in the literature, indicates several considerations and recommendations required on the journey to road digital twins, and suggests multiple future research directions based on the review summaries of machine learning capabilities.