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The Texas DOT (TXDOT) is concerned about the widening gap between pavement preservation needs and available funding. Thus, the TXDOT Austin district pavement engineer (DPE) has investigated methods to strategically allocate available pavement funding to potential projects that improve the overall performance of the district and Texas highway systems. The primary objective of the study presented in this paper is to develop a network-level project-screening and ranking method that supports the Austin district 4-year pavement-management plan development. The writers developed candidate-project selection and ranking algorithms that evaluated pavement conditions of each project candidate using data contained in the Pavement Management Information System (PMIS) database, incorporated insights from Austin district pavement experts, and implemented the developed method and supporting algorithm. This process previously required weeks to complete, but now requires about 10 min, including data preparation and running the analysis algorithm, which enables the Austin DPE to devote more time and resources to conducting field visits, performing project-level evaluations, and testing candidate projects. The case study results show that the proposed method assisted the DPE in evaluating and prioritizing projects and allocating funds to the right projects at the right time. [ABSTRACT FROM AUTHOR]

Recently, a structural condition index (SCI) was developed for the Texas Department of Transportation as a screening tool to discriminate between pavements that need structural reinforcement from those that do not. For the successful implementation of the SCI concept at the network level, one of the critical issues is to determine the minimum falling weight deflectometer (FWD) testing frequency. The paper discusses how to determine the required FWD testing frequency, or the required FWD test spacing needed for implementing the SCI at the network level. In this study, the risk-based method was employed, where the variance of the SCI at the network level was assessed by randomly selecting a small sample of the pavement sections that were representative of the whole network. Due to the limited number of such sections and the lack of knowledge of underlying distribution of the SCI variances at the network level, the bootstrap method was used to obtain the mean and the confidence intervals on the mean of the SCI variances at the network level. [ABSTRACT FROM AUTHOR]