Predicting pavement performance using distress deterioration curves

Abstract

Highway Authorities in the UK use Surface Condition Assessment for the National Network of Roads (SCANNER) in assessing and managing their road networks. This survey vehicle utilises laser measurements to detect and quantify most of the distress on the road surface, such as rutting, cracking and texture depth. It is however a data intensive and expensive approach since it is conducted annually. This study presents a simple method to predict pavement distress using previous SCANNER measurements. The previous measurements are used to develop Distress Deterioration Master Curves (DDMC) that relate distress deterioration rate with the severity of the distress. These curves can be used to predict future distress severity based on the current state without the need to provide further data such as pavement age or pavement material properties. To demonstrate the application of this method, a significant amount of SCANNER data covering around 400 km of class A roads in Nottinghamshire collected between 2014 and 2020 were analysed, and rutting, crack intensity, and texture depth were modelled in this study. DDMRs of these distress types were built based on data collected between 2014-2018, then 2020 data were used to validate the predictions. The results show that the developed method can be implemented in predicting surface distress of roads using previous measurements, which makes it a valuable addition tool for highway authorities subject to underfunding.

KEYWORDS:

• Pavement performance
• distress prediction
• modelling
• deterioration curves

Acknowledgement

The authors would like to acknowledge Neil Butler, Peter Wells, and Ian Patchett from Via East Midlands for providing the data and for their technical support during the development of this work.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability

The data underlying this article will be made available upon reasonable request from the corresponding author subject to data owner approval.

Additional information

Funding

This work was supported by the Engineering and Physical Sciences Research Council [grant number EP/T01962X/1].