Selecting Network-Level Project Sections for Sustainable Pavement Management in Texas

Abstract

Abstract:In recent years, the increasing gap between available funding and preservation needs has influenced district pavement engineers to select and prioritize projects to effectively use funding. However, currently, projects are often selected after an informal assessment, based on local conditions and local district engineers' experience, in the absence of a statewide systematic process. The primary objective of this study is to determine network-level project sections for effective sustainable pavement management using logistic regression analysis. A large volume of inventory data, documented using pavement-management information systems (PMIS), was used to develop the logistic regression (LR) model for selecting candidate sections. The LR model was subsequently validated using a single 50/50 split sample method. The findings of this study will assist the Austin, Texas, USA district to select and evaluate candidate projects. Furthermore, the study will eventually contribute to improved efficiency in project selection and prioritization by reducing not only the amount of time necessary to review the district PMIS data to identify project candidates, but also the potential for human error.

Research Trends in Pavement Management during the First Years of the 21st Century: A Bibliometric Analysis during the 2000–2013 Period

“…At the project level of PMSs, decisions are targeted to define the projects with the best cost-benefit ratio, so the limited budgets are used to construct, maintain, or rehabilitate longer lasting roads, considering all the road-life [157]. Accordingly, the concept of lifecycle cost analysis (LCCA) is highlighted during the project-level analysis.…”

Fatores determinantes de sucesso: uma análise dos projetos eólicos dos leilões de energia de reserva da ANEEL

“…Cada combinação linear corresponde a uma componente principal cujo número total é igual à quantidade das variáveis utilizadas. A primeira componente principal é aquela que representa a maior variância de dados, ao passo que a segunda é perpendicular à primeira e abrange a segunda maior variância, e assim sucessivamente até que se chegue à última componente (Hongyu, Sandanielo, & Oliveira, 2016;Jolliffe & Cadima, 2016).…”

Factors Affecting Maintenance Probability and Resurfacing Thickness Based on the Pavement Management System

“…Logistic regression is an appropriate method to model a binary dependent variable as a function of predictors. It has been applied to model a wide variety of transportation and pavement related data, including traffic accidents, pavement fatigue cracking, pavement patching effect, and selection of preservation projects [22][23][24][25][26]. In this research, the goal of logistic regression is to describe how the predictors influence the probability that M&R will be required.…”