Title: Predicting Pavement Condition Index Using International Roughness Index in a Dense Urban Area
Abstract: A number of pavement condition indices are obtained and used to conduct pavement management assessments, two of which are the International Roughness Index (IRI) and Pavement Condition Index (PCI). The IRI is typically obtained using specialized equipment which indicates the smoothness of the roadway segment based on established computer algorithms, while the PCI is based on subjective rating of the number of pavement distresses. The literature suggests that most of these pavement indices are related as a result of which several jurisdictions have developed models to predict one index from the other. This study used 2 years of IRI-PCI data sets to develop models that predict PCI from IRI by functional classification and by pavement type in the District of Columbia. The results of the descriptive statistics, based on the mean IRI and PCI values, suggest that highways have a smoother ride than arterials, followed by collectors and local roads. Similarly, when the data was analyzed by pavement type, the results show that Composite Pavements were smoother than Asphalt Pavements followed by Concrete Pavement. The regression models between the IRI and PCI by functional classification and pavement type were determined to be statistically significant within the margin of error (5% level of significance), with R 2 values between 0.56 and 0.82. The results of the ANOVA tests also showed statistically significant F - statistics (p < 0.05) in addition to statistically significant regression coefficients (from the t-tests, with p < 0.05). The residual plots for all the models also showed randomness about the zero line indicating their viability, in addition to the normal probability plots showing points near a straight line.
Publication Year: 2015
Publication Date: 2015-01-01
Language: en
Type: article
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Cited By Count: 76
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