Date of Completion

8-24-2012

Embargo Period

8-30-2012

Open Access

Open Access

Abstract

This thesis presents the analysis of asphalt pavement cracking data on the network level in Connecticut. Pavement performance indicators including longitudinal and transverse cracking as well as roughness are investigated with respect to numerous distinct factors that can be grouped into three categories, i.e. climatic-, pavement structure-, and traffic-related. High-quality climatic data was obtained from the national weather stations in Connecticut. Maintenance and construction data was used to determine the pavement age and structure amongst several other factors. Traffic data was acquired from the state records and accumulated traffic loading was estimated for all segments based on their age. High definition pavement images collected by the Automatic Road Analyzer (ARAN) van in 2010 were used to quantify the longitudinal and transverse cracking with respect to their location within the pavement surface. The data was analysed in three different methods. First, a Monte Carlo approach in which all segments are sampled one hundred times for randomized 500 foot sections was done. From this, qualitative and quantitative trends were observed to determine the significance each factor had on the three different types of pavement performance indicators. Second, an artificial neural network was constructed for each of the different performance indicators to see how well the different factors predicted the outcome. Lastly, pavement management analysis was done to understand the budget implications of this dataset under four different management scenarios.

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