With over 200 delegates from all over the world including policy makers, industry leaders as well as academia, attendees learned how the collaboration aims to “create the most accurate predictions of road degradation possible”.
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 potential to enable the needed approach for road condition predictions and proactive asset management.
The project, which has been supported by National Highways and has received funding from U21 and UKCRIC, looks at the design, development, implementation, and test for a pavement digital twin. The project also explores the potential benefits of digital twins in monitoring health, modelling pavement deterioration, and selecting maintenance methods based on cost-benefit analysis. The developing model of the pavement includes information related to traffic, climate conditions, pavement degradation, and past maintenance activities.
The data is obtained and processed using sensors, big data analytics, machine learning, and hybrid artificial intelligence-numerical simulation techniques. This will enable a real-time reflection and better understanding of the characteristics of the pavement, which could potentially enhance pavement maintenance strategy decision-making with optimum selection of maintenance type and timing.
Further information about the project including slides from the webinar can be found on the International Road Federation website.