Quality Assurance Data Analysis as a Leading Indicator for Infrastructure Condition Performance Management [tech brief]

Federal Highway Administration2022PDFBriefRao Research and Consulting, LLCUnited States. Federal Highway Administration. Office of Research, Development, and TechnologyTurner-Fairbank Highway Research CenterCorrigan, MatthewUnited States. Federal Highway Administration. Office of Research, Development, and TechnologyUnited StatesQuality assuranceConstructionPavement management systemsPerformance measurementData analysisMAP-21Construction dataLeading indicatorData integrationPerformance predictionFHWA-HRT-22-050DTFH6115C00041The Federal Highway Administration (FHWA) and the transportation community have a longstanding goal to improve the performance and extend the life of transportation infrastructure. The Moving Ahead for Progress in the 21st Century Act emphasizes risk-based and performance-based requirements to plan and program the most efficient use of Federal transportation funds. FHWA issued a draft notice of proposed rulemaking in 2015 and a final ruling in 2017 to establish pavement performance measures, targets, and reporting.(1) The ruling defines targets using the performance metrics cracking (percent), rutting (inch), and international roughness index (IRI) (inches/mile) for flexible pavements. The ruling further defines cracking (percent), faulting (inch), and IRI for rigid pavements.(2) State highway agencies (SHAs) establish performance targets and measure progress to assess whether they are meeting their targets; they also recognize that routinely collected condition assessment data are a lagging performance indicator. As advanced pavement design methods, sophisticated construction technologies, and digital data collection become the norm, quality assurance (QA) and construction data serve as leading indicators of pavement performance. Those indicators provide the basis for developing performance-related specifications (PRS) for QA and evaluate the impact of deviations from specifications during construction on long-term performance. Construction-stage data offer an opportunity to enhance an SHA's pavement management system (PMS). This project evaluated whether QA data and other as-built construction data from four SHA databases have a strong correlation to pavement performance. The project explored different data integration and statistical procedures required to improve performance prediction at both project and network levels. The goal was to develop practical recommendations and best practices to include these data within the pavement management decision-making framework. These data may provide a foundation to evolve, expand, and improve pavement testing and data processing techniques. As SHAs begin to adopt the framework, they will facilitate and encourage the development of pavement testing and data processing methodologies within each SHA. Case studies demonstrate how using different types of construction and QA data can improve PMS performance forecasting and validate them as leading indicators of performance. This research yields recommendations and best practices to include SHA QA and construction data within the pavement management decision-making framework.136