EVALUATION AND VALIDATION OF PAVEMENT DISTRESS AND PERFORMANCE MODELS FOR A PAVEMENT MANAGEMENT SYSTEM USING AUTOMATED ASPHALT CONDITION DATA
1 online resource (184 pages) : PDF
University of North Carolina at Charlotte
Since 2011, the North Carolina Department of Transportation (NCDOT) has implemented an automated data collection process for their Pavement Management System (PMS). This study was conducted to update NCDOT’s PMS with the availability of new automated network data from 2014. Distress indices were developed using NCDOT’s Maximum Allowable Extent (MAE) method and a composite performance index was developed using the Analytic Hierarchy Process (AHP). To predict calculated distress and performance indices over time, non-linear sigmoidal models were developed. These models, developed exclusively with 2014 data were then visually compared to previously developed models. A visual comparison indicated that models developed in this study varied significantly compared to models developed using automated data from 2012 and 2013. Models developed in this study present better performance ratings in years 0 to 10 compared to previously developed models. From years 11 to 20, models developed in this research present higher deterioration rates compared to previous research. This indicates a more responsive PMS where timely maintenance strategies should be implemented to help eliminate excessive deterioration of roadways. For statistical conclusions, confidence intervals were developed for the b variable which controls the horizontal shift of a sigmoidal regression curve. Overall, most previously research b variables fell outside the upper bound of the interval. This statistically confirms the results of the visual comparison between models.
ASPHALT DISTRESSASSET MANAGEMENTPAVEMENT DISTRESS MODELSPAVEMENT MANAGEMENT SYSTEMPAVEMENT PREDICTION MODELS
Construction & Facilities Mgmt
Nicholas, ThomasHildreth, John
Thesis (M.S.)--University of North Carolina at Charlotte, 2015.
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