Upgradation of pavement deterioration models for urban roads by non-hierarchical clustering

Abstract

Pavements, being a significant component of urban infrastructure, their maintenance and rehabilitation to the desired serviceability level is a challenging problem faced by engineers. The development of a reliable pavement deterioration model is essential to devise proper maintenance policies. This exploratory paper presents the development of network-level pavement performance prediction models for the selected arterial and sub-arterial roads of Tiruchirappalli city, India. Road inventory, traffic volume, maintenance history, pavement condition, and roughness data of the study area are collected periodically for seven years. The Pavement Condition Index (PCI) is determined from the data collected through visual evaluation of the type, severity, and amount of pavement distress. Roughometer is deployed to obtain the International Roughness Index. The parameters which influence pavement deterioration vary widely for different roads within the same network. The pavement sections are assembled into three homogeneous clusters using k-means clustering, which is a nonhierarchical clustering algorithm, so that they can be modeled with better acceptability. Pavement performance prediction models are generated for different clusters using multiple linear regression analysis, and comparison is made with that developed for non-clustered data. The error in prediction is found to be less for clustered models. While the pavement sections in cluster 2, when left unmaintained, deteriorates from a PCI value of 100 to 77 in 5 years, those belonging to cluster 3 are found to deteriorate from 100 to 13. The variation in the deterioration process and the significance of clustering pavement sections for efficient pavement maintenance management is established.