Skip to main navigation Skip to search Skip to main content

Texture depth prediction using distress deterioration curves

  • University of Nottingham

Research output: Unpublished contribution to conferenceUnpublished Conference Paperpeer-review

215 Downloads (Pure)

Abstract

Road Surface Texture Depth (STD) is a critical aspect of a road surface. A typical STD range is between 2.0-0.8 mm. Roads with STD less than the lower threshold are more prone to traffic accidents due to aquaplaning or drop in skid resistance. Roads with STD more than the upper threshold are prone to fretting and pothole formation. In this study, a simple method to predict STD has been developed. The method utilises previous STD measurements collected by the Surface Condition Assessment for the National Network of Roads (SCANNER) method to quantify STD deterioration rate, which is the amount of increase or decrease in STD over time. The deterioration rates are then converted into Texture Deterioration Master Curves (TDMCs) which can be used in predicting STD. To demonstrate the application of this method, SCANNER data covering around 400 km of class A roads in Nottinghamshire collected between 2014 and 2018 were analysed and used to build TDMCs. STD data in 2020 were then predicted and compared to the measured STD data for validation. The results show that the developed method is simple and reliable, which makes it a valuable management tool for highway authorities enabling them predicting the STD on their road networks and assessing the risks of high or low STD on the condition and operation of their road networks.

Conference

ConferenceThe 21st LJMU International Conference on Highways and Airport, Asphalt Technology and Infrastructure
Country/TerritoryUnited Kingdom
CityLiverpool
Period28/02/2229/02/24
Internet address

Bibliographical note

This is a copy of a paper presented at the 21st LJMU InternationalHighways and Airport Pavement Engineering, Asphalt Technology, and Infrastructure Conference 2024.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

Fingerprint

Dive into the research topics of 'Texture depth prediction using distress deterioration curves'. Together they form a unique fingerprint.

Cite this