Return to the VTRC Home Page
Click here to print the printer friendly version of this page.
Page Title: VTRC Report Detail

The contents of this report reflect the views of the author(s), who is responsible for the facts and the accuracy of the data presented herein. The contents do not necessarily reflect the official views or policies of the Virginia Department of Transportation, the Commonwealth Transportation Board, or the Federal Highway Administration. This report does not constitute a standard, specification, or regulation. Any inclusion of manufacturer names, trade names, or trademarks is for identification purposes only and is not to be considered an endorsement.


Development of Enhanced Pavement Deterioration Curves
Samer W. Katicha, Ph.D., Safak Ercisli, Gerardo W. Flintsch, Ph.D., P.E., James M. Bryce, Ph.D
Brian K. Diefenderfer
Brian K. Diefenderfer
Year: 2016
VTRC No.: 17-R7
Abstract: This report describes the research performed by the Center for Sustainable Transportation Infrastructure (CSTI) at the Virginia Tech Transportation Institute (VTTI) to develop a pavement condition prediction model, using (negative binomial) regression, that takes into account pavement age and pavement structural condition expressed in terms of the Modified Structural Index (MSI). The MSI was found to be a significant input parameter that affects the rate of deterioration of a pavement section with the Akaike Information Criterion (AIC) suggesting that the model that includes the MSI is, at least, 50,000 times more likely to be closer to the true model than the model that does not include the MSI. For a typical pavement at 7 years of age (since the last rehabilitation), the effect of reducing the MSI from 1 to 0.6 results in reducing the critical condition index (CCI) from 79 to 70.

The developed regression model predicts the average CCI of pavement sections for a given age and MSI value. In practice, the actual CCI of specific pavement sections will vary from the model-predicted condition because many (important) factors that affect deterioration are not considered in the model. Therefore an empirical Bayes (EB) method is proposed to better estimate the CCI of a specific pavement section. The EB method combines the recorded CCI of the specific section with the CCI predicted from the model using a weighted average that depends on the variability of individual pavement sections performance and the variability of CCI measurements. This approach resulted in improving the prediction of the future CCI, calculated using leave one out cross validation, by 21.6%.