A simplified pavement condition index regression model for pavement evaluation

Abstract

ABSTRACT International Roughness Index (IRI) and Pavement Condition Index (PCI) are among other pavement condition indices used to assess pavement surface condition. The literature suggests that most of the pavement indices are related as a result of which several models have been developed to predict one index from the other. This study uses the Long-Term Pavement Performance (LTPP) database to develop a simplified regression model that links PCI with IRI. Measured pavement distresses from 1448 LTPP sections from the Specific Pavement Studies (SPS) and General Pavement Studies (GPS) representing 12744 data points were utilised for the PCI estimation. A total of 1208 sections with 10868 data points were used for model development while 240 sections with 1876 data points were used for the model validation. A sigmoid function is found to best express the relationship between PCI and IRI with a coefficient of determination (R2) of 0.995. The bias in the predicted IRI values is significantly very low. The model validation using a different dataset also yielded highly accurate predictions (R2 = 0.992). Finally, a pavement condition rating based on IRI is proposed. This system yields rating equivalent to the widely used PCI rating method which is based on the pavement condition.