International Journal of Pavement Engineering · 2019 · 140 citations · 14 references
EngineeringCivil EngineeringCondition AssessmentPavement EngineeringTransportationPavement DesignPavement DistressesPavement EvaluationPavement Performance AnalysisPavement RoughnessPavement Surface CharacteristicsCondition RatingInternational Roughness IndexPavement Condition IndexPavement Performance EvaluationPavement Performance ModelingPavement Performance PredictionPrediction Model DevelopmentSurface Condition Assessment
International Roughness Index (IRI) and Pavement Condition Index (PCI) are key pavement condition indices, and literature shows they are related, prompting models to predict one from the other. The study develops a simplified regression model linking PCI with IRI using the LTPP database and proposes an IRI‑based pavement condition rating. Using 1,208 LTPP sections (10,868 data points) from SPS and GPS for training and 240 sections (1,876 data points) for validation, the model was built from measured pavement distresses. A sigmoid regression achieved R² of 0.995, with negligible bias and a validation R² of 0.992, producing an IRI‑based rating equivalent to the PCI method.
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.
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