DEVELOPING A PAVEMENT CONDITION ASSESSMENT METHOD FOR UNPAVED ROADS IN UGANDA

Musiime, Richard

DEVELOPING A PAVEMENT CONDITION ASSESSMENT METHOD FOR UNPAVED ROADS IN UGANDA

Musiime, Richard

Authors

Richard Musiime

Contributors

Mr Juan Ferriz-Papi J.A.Ferriz-Papi@salford.ac.uk
Supervisor

Amanda Marshall-Ponting
Supervisor

Abstract

Approximately 95% of Uganda's total road network consists of unpaved surfaces, a situation that is prevalent across most developing countries in the world. This heavy reliance on unpaved roads poses significant challenges, as these roads are highly susceptible to rapid deterioration due to the combined effects of traffic and environmental factors. Consistent monitoring and evaluation of the condition of these unpaved roads is, therefore, crucial to ensure the safety and reliability of the transportation infrastructure. However, the current methods employed for assessing the condition of unpaved roads are often subjective, labor-intensive, and time-consuming, frequently leading to inconsistent evaluations.

This PhD study proposes an enhanced method for assessing the condition of unpaved roads in Uganda. The methodology used in this study consisted of five stages: research formulation, investigation, model development, model validation and recommendations. A questionnaire survey was conducted during the investigation stage, with a 51.4% response rate from road maintenance professionals spread across the country's six regions. The novel Gravel Road Condition Index (GRCI) utilized the Analytic Hierarchy Process (AHP) to convert the subjective questionnaire survey results into objective mathematical data. The AHP method provided a rigorous and quantitative approach for systematically weighting and ranking the nine key distresses affecting the surface conditions of unpaved roads in the country.

The developed Gravel Road Condition Index was validated by applying the method on a case-study gravel road and verifying the results through comparison with the pre-existing condition assessment method in Uganda. The results demonstrated that the GRCI offered a rapid, efficient, and user-friendly procedure for assessing the condition of unpaved roads, underpinned by objective weightings that demonstrated consistency in its evaluations. This PhD study further established a relationship between the novel GRCI and the current gravel loss prediction model for unpaved roads in Uganda. This relationship can be used to improve maintenance planning and efficiently optimize the already scarce funding resources in the country.