RoadGP v1.0 is a browser-based tool that guides users through a step-by-step process to diagnose road defects. By entering basic road information and selecting any observed issues, the tool suggests likely causes and offers potential repair strategies.

Digital Roads Senior Research Associate in Data Science, Dr Stephen Green, explained;

We're currently developing an automated decision support tool that translates the real-world data obtained from road sections all over the country to their appropriate treatment plan. Our system observes defects and the severity of their condition and provides appropriate treatment plans based on what caused the damage, along with relevant repair strategies. The optimum version of this is then decided based on how long they will last and how much they will cost to implement. This tool will be supported by existing maintenance data, processed with the help of machine learning, and distributed to researchers across the field.

Click here to access RoadGP tool

(Opens in new window)

Key Features

• Interactive input: Enter road length, choose road material, and then select defects from an intuitive interface.
• Automatic analysis: The tool analyses selected defects to identify likely causes and recommended repairs.
• Tabulated results: Summaries include likely causes, suggested repair actions, relative likelihoods, estimated lifespans and cost levels, and references to traditional repair strategies.

How to Use RoadGP

Enter road information – Specify the road length (in meters) and pavement material (by clicking on "Asphalt" or "Concrete"), then click “Continue.”
2. Choose observed defects – Select defects using the search box or the road diagram, and indicate severity and quantity.
3. View results – Click “Continue” to see possible causes and recommended repairs. Optional full tables provide extra detail.

This research is supported by the Digital Roads Prosperity Partnership (DR). DR is supported by the Engineering and Physical Sciences Research Council (EPSRC) grant number [EP/V056441/1], Costain, National Highways, the University of Cambridge, Department for Transport and Didimi.

The authors gratefully acknowledge the collaboration of all academic and industrial project partners. Any opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the institutes mentioned above.