Developing an effective approach to assess pavement condition for high friction surface treatment (HFST) installation

Alireza Roshan; Magdy Abdelrahman

doi:10.14311/AP.2024.64.0571

Authors

Alireza Roshan Missouri University of Science and Technology, Department of Civil, Architectural and Environmental Engineering, MO 65409, Rolla, USA
Magdy Abdelrahman Missouri University of Science and Technology, Department of Civil, Architectural and Environmental Engineering, Missouri Asphalt Pavement Association (MAPA) Endowed Professor, MO 65409, Rolla, USA

DOI:

https://doi.org/10.14311/AP.2024.64.0571

Keywords:

high friction surface treatment (HFST), ensemble machine learning models, pavement condition index (PCI), international roughness index (IRI)

Abstract

For more than two decades, High Friction Surfacing Treatments (HFST) have been used worldwide to improve road safety at critical locations, such as sharp curves and intersections. However, the costs associated with HFST installation and the rapid deterioration observed on pavements with poor structural conditions cannot be overlooked. To address these concerns, this research study sought a reliable and accurate method for assessing the suitability of applying HFST to pavements. The main focus was on using machine learning techniques and incorporating International Roughness Index (IRI) and Pavement Condition Index (PCI) data to predict and provide informed recommendations for HFST application. To achieve this, ensemble models were employed, of which the decision tree and extreme gradient boosting showed robust performance, achieving an impressive R-squared value of 0.90, indicating a high level of accuracy in predicting PCI. These models were further assessed for HFST application using the LTPP dataset, with sections classified as suitable and categorised them as good, fair, or poor. The suggestions from these models were particularly reliable in determining the appropriate area for HFST application. The research results clearly demonstrated the efficacy of the ensemble models in accurately predicting PCI and providing informed recommendations for HFST application.

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