Date of Award:


Document Type:


Degree Name:

Master of Science (MS)


Civil and Environmental Engineering

Committee Chair(s)

Kevin Heaslip


Kevin Heaslip


Ziqi Song


Ryan Dupont


The research presented in this thesis investigates the effect the data collection process has on the results of the economic analysis in pavement management systems. The incorporation of pavement management systems into software packages has enabled local governments to easily implement and maintain an asset management plan. However a general standard has yet to be set, enabling local governments to select from several methods of data collection.

In this research, two pavement management system software packages with different data collection methods are analyzed on the common estimated recommended M&R cost provided by their respective economic analysis. The Transportation Asset Management Software (TAMS) software package developed by the Utah LTAP Center at Utah State University consists of a data collection process composed of nine asphalt pavement distress observations. The Micro PAVERTM software package developed by the Army Corps of Engineers consists of a data collection process composed of 20 asphalt pavement distress observations.

A Latin-hypercube sample set was input into each software package, as well as actual local government pavement condition data for the City of Smithfield, Utah and the City of Tremonton, Utah. This resulted in six total data sets for analysis, three entered and analyzed in TAMS and three entered and analyzed in Micro PAVERTM. These sample sets were then statistically modeled to determine the effect each distress variable had on the response produced by the economic analysis of estimated recommended M&R costs.

Due to the different methodologies of pavement condition data collection, two different statistical approaches were utilized during the sensitivity analysis. The TAMS data sets consisted of a general linear regression model, while the Micro PAVERTM data sets consisted of an analysis of covariance model. It was determined that each data set had varying results in terms of sensitive pavement distresses; however the common sensitive distress in all of the data sets was that of alligator cracking/fatigue. This research also investigates the possibility of utilizing statistically produced models as a direct cost estimator given pavement condition data.