Predicting pavement condition index using international roughness index in Washington DC.
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Predicting pavement condition index using international roughness index in Washington DC.

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      A number of pavement condition indices are used to conduct pavement management assessments, two of which are the International Roughness Index (IRI) and Pavement Condition Index (PCI). The IRI is typically measured using specialized equipment that calculates the smoothness and ride quality of the roadway segment based on established computer algorithms, while the PCI is based on subjective rating of the number of pavement distress. The literature suggests that most pavement indices are related, as a result of which several jurisdictions have developed models to predict one index from the other(s). This study used three (3) years of IRI-PCI data obtained from the District Department of Transportation to develop models which could potentially predict PCI from IRI by functional classification and by pavement type. The regression models explored were developed using the ordinary least squares method and were tested on the basis of 5% level of significance. The IRI-PCI models yielded R2 and adjusted R2 values between 0.008 and 0.0730, indicating that the models could only explain up to 7.3% of the variations in the data. In addition, the root mean square errors of the models were all determined to be greater than 1. Even though the results of the ANOVA tests indicated that the coefficients were generally statistically significant, the low R2 values and high RMSEs indicate that the models do not adequately predict PCI from IRI, within the margin of error. A more sophisticated prediction tool, such as artificial neural networks, could be explored to potentially predict PCI from IRI more accurately.
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