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

By Kyungwon Park, N. Thomas, K. Lee. 2007

146 citations recorded by the source corpus.

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.

Subfields: Civil and Structural Engineering, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/447d46277556c33bbf2de054101d4524fdb6059b

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Civil and Structural Engineering

1. Sherif M. El-Badawy
2. Amr A. Elhadidy
3. Emad Elbeltagi

In Mechanical Engineering

1. Sherif M. El-Badawy
2. Amr A. Elhadidy
3. Emad Elbeltagi

Citations in the corpus

• A simplified pavement condition index regression model for pavement evaluation (2019, methodology): “Park et al. (2007) established a power relationship between PCI and IRI using data from nine states and provinces in Northern America.”