Applicability of the International Roughness Index as a Predictor of Asphalt Pavement Condition

TLDR

The study investigates the relationship between asphalt pavement distress, measured by the pavement condition index (PCI), and surface roughness, measured by the international roughness index (IRI). Using DataPave to obtain IRI values and MicroPAVER1 to compute PCI from distress data, the authors fitted a transformed linear regression model to predict PCI from IRI across LTPP roadway sections. The analysis confirms that IRI explains roughly 59 % of PCI variation and that a strong statistical relationship exists between the two indices.

Abstract

This note establishes the relationship between the surface distress of an asphalt pavement and its roughness, as conveyed respectively by the pavement condition index (PCI) and the international roughness index (IRI). The DataPave software provides the roughness of varied roadway pavement sections from the North Atlantic region that were investigated under the long term pavement performance (LTPP) study. The MicroPAVER1 software system computes the condition of the same sections using cross-referenced distress data from DataPave. A transformed linear regression model predicts pavement condition given roughness. It confirms the acceptability of the IRI as a, albeit not the sole, predictor variable of the PCI whereby the former accounts for the majority, close to 59%, of the variations in the latter. Further, an analysis of variance confirms the existence of a strong relationship between both variables.