The Texas Department of Transportation (TxDOT) is concerned about the widening gap between preservation needs and available funding. Funding levels are not adequate to meet the preservation needs of the roadway network; therefore projects listed in the 4-Year Pavement Management Plan must be ranked to determine which projects should be funded now and which can be postponed until a later year. Currently, each district uses locally developed methods to rank and prioritize projects. These ranking methods have relied on less formal qualitative assessments based on engineers’ subjective judgment. It is important for TxDOT to have a rational 4-Year Pavement Management Plan. The objective of this study is to develop a conceptual framework that describes the development of the 4-Year Pavement Management Plan and a proposed ranking process. It can be largely divided into three steps; (1) Network-Level preliminary project screening process, (2) Project-Level project ranking process, and (3) Economic Analysis. A rational pavement management procedure and a project ranking method that are accepted by districts and the TxDOT administration will maximize efficiency in budget allocations and help improve pavement condition.
As a part of this study, based on the data provided by the Austin District Pavement Engineer, the Network-Level Project Screening (NLPS) tool, including the candidate project selection algorithm and the preliminary project screening matrix, is developed. The NLSP tool has been used by the Austin District Pavement Engineer (DPE) to evaluate the PMIS (Pavement Management Information System) data and to prepare a preliminary list of candidate projects for further evaluation. The automated tool will help TxDOT engineers easily incorporate the developed mathematical algorithm into their daily pavement maintenance management. / text
Identifer | oai:union.ndltd.org:UTEXAS/oai:repositories.lib.utexas.edu:2152/ETD-UT-2011-08-4269 |
Date | 30 September 2011 |
Creators | Hwang, Jea Won |
Source Sets | University of Texas |
Language | English |
Detected Language | English |
Type | thesis |
Format | application/pdf |
Page generated in 0.0017 seconds