ORCID Identifier(s)

0000-0002-0396-1945

Graduation Semester and Year

2020

Language

English

Document Type

Dissertation

Degree Name

Doctor of Philosophy in Civil Engineering

Department

Civil Engineering

First Advisor

Stefan Dr. Romanoschi

Abstract

Accurate prediction of pavement deterioration is vital for an efficient and cost-effective allocation of available budgets for keeping an agency’s road networks operating at a desirable level. Currently, most cities in the Dallas-Fort Worth Metroplex area are using the software PAVERTM and the associated performance models to predict future conditions as they do not have available reliable prediction models. However, the problem with this type of modeling is that the models are not calibrated to local conditions. The Pavement Deterioration Prediction models that have been developed in this research will help any pavement management agencies within DFW Metroplex area to identify and predict the future pavement performance for any planning period. The models were developed based on the available data collected by the city's pavement management department for the DFW Metroplex area. In this research, a family modeling approach has been used as this method reduces the number of independent variables in performance modeling to a single variable (age in this research) by enabling the development of models in each pavement family. Separate models are also developed for areas with expansive and non-expansive subgrade soil. A total of eleven models are developed for the areas non-expansive subgrade soil area and nine models for the areas with expansive subgrade soil. Deterministic models that are developed are applicable to cities with available historical data on PCI or IRI. The developed probabilistic models are applicable to cities with a current pavement condition data, but no less than the last two consecutive years.

Keywords

Deterministic models, Probabilistic models, Family modeling

Disciplines

Civil and Environmental Engineering | Civil Engineering | Engineering

Comments

Degree granted by The University of Texas at Arlington

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