Abstract:
Road maintenance is a crucial process for pavement management systems. South African
local roads managed by lower road authorities (municipality, etc) are in critical condition,
and their management is not at optimum level which is evident from their poor condition.
The aim of this paper is to provide a Machine-Learning algorithm to assist road authorities
to provide optimal maintenance strategies. The objective of the study was to determine the
most effective condition index for management of flexible pavements. This is achieved by
conducting descriptive and inferential statistical analysis of two case studies (Low volume
roads and High-volume roads). Statistical analysis indicated that the visual condition index
(VCI) has inconsistencies compared to the deduct point surface condition index (CISURF)
and deduct point pavement condition index (CIPAVE) found in TMH 22. Four machine
learning models were created which included the Gradient Boosting Classifier, Random
Forest Classifier, Support Vector Machine Classifier, and Decision Tree Classifier. Of the
four models explored, the model with the greatest potential for deployment was the
Gradient Boosting Classifier (GBC) model. The GBC model had an accuracy of 74 %, 85
% and 93 % in relation to the VCI, CISURF & CIPAVE respectively. The CISURF and CIPAVE was
identified as the most effective index for use in flexible pavements.
