Bayesian Regression Methodology for Project Prioritization

Abstract

In highway infrastructure planning, results from pavement evaluation need to be aggregated into a composite or combined measure of quality for project selection at the network level. In the project prioritization process of the Kansas Department of Transportation (KDOT), a pavement structural rating attribute, Pavement Structural Evaluation (PSE), is used. Currently these ratings are done subjectively based on the condition of the pavement indicated by the visual distresses, maintenance history, and engineering judgement because KDOT does not collect any deflection data during network-level distress survey. This paper describes the application of classical and Bayesian regression methodologies for better estimation of PSE values using the results from the Falling Weight Deflectometer (FWD) tests and network-level distress survey for project prioritization purposes.

Keywords:

• Pavement
• Pavement Management System
• Pavement Evaluation
• Bayesian Regression
• Fwd