Date of Award
Doctor of Philosophy
Carlos M. Chang
Pavements are an important part of the highway transportation infrastructure, accounting for the largest share of the overall investment. A tremendous amount of time and money is spent each year on the construction of new pavements, as well as on the maintenance and rehabilitation of existing pavements.
Transportation agencies use pavement management systems (PMS) for their maintenance and rehabilitation planning, programming, and budgeting. PMS are used to make decisions regarding when maintenance and rehabilitation should be applied. The systems also select what type of treatment should be applied for each pavement section in the network with clear estimations of the cost for different scenarios. To support these decisions, it is important to have reliable data on pavement conditions and accurate performance models for predicting pavement condition. The data on pavement condition typically comes from regular annual field surveys resulting in distress, condition, and ride scores. PMS datasets are often incomplete (for some locations and some years) because they could not be rated, measured, collected, saved, and managed correctly. The PMS missing data reduce the predictive power of the pavement performance models.
Model-free and model-based replacement techniques for estimating missing data points have been designed and successfully used in other application areas like statistics, economics, marketing, medicine, psychometrics, political science, etc. It is therefore reasonable to apply these methods to the PMS databases. Statistical techniques are assembled and used in a robust approach to systematically analyze the effect of applying these techniques to rebuild missing performance data.
The dissertation is planned as follows. Chapter one is the introduction. Chapter two provides a comprehensive overview of the pavement performance measures in PMS. Chapter three is an extensive literature review of the statistical techniques to handle missing data. Chapter four is a comprehensive description of a systematic statistical approach to populate missing performance data. In addition, several statistical techniques and methods for handling missing data in PMS are discussed.
Chapter five is a technical paper entitled "A Systematic Statistical Approach to Populate Missing Performance Data in Pavement Management Systems," submitted to the Journal of Infrastructure Systems. It includes a case study, Continuous Reinforced Concrete Pavement (CRCP) sections were selected to test the statistical systematic approach from Pavement Management Information System (PMIS) maintains by the Texas Department of Transportation (TxDOT). A major impact was observed in the results of predicting the distress scores due to applying the developed approach. Finally chapter six summarizes the obtained conclusions and recommendations to extend the future works related to this study.
Received from ProQuest
Mazin M. Al-Zou'bi
Al-Zou'bi, Mazin M., "A Systematic Approach To Manage Missing Data In Pavement Management Systems" (2013). Open Access Theses & Dissertations. 1777.