Quality Assurance Data Analysis as a Leading Indicator for Infrastructure Condition Performance Management

Quality assurance (QA) and other construction data contain information about quality of materials and construction that can potentially serve as leading indicators of pavement performance. This project evaluated approaches to integrate QA and construction data into an agency's pavement management system (PMS) for improving performance forecasting models. It also evaluated the ability to further combine site-specific soil, climate, and traffic information to enhance forecasting. Phase I of this study reviewed State department of transportation practices in collecting, storing, and accessing construction data, as well as their interest in relating these data to performance. Data from select agencies, along with other national databases, were then used for analyses and modeling in phase I and phase Ⅱ. Data were obtained from traditional QA tests, innovative QA methods, and construction process control technologies. The performance metrics align with the Moving Ahead for Progress in the 21st Century performance targets—cracking, rutting, and smoothness for asphalt pavements, and cracking, faulting, and smoothness for concrete pavements. Results of this study suggest that traditional QA data elements are statistically correlated to performance and show promise for use in PMS forecasting. Data from innovative QA test methods and construction technologies demonstrate direct or indirect correlation to performance. However, for use in PMS, agencies need to establish systematic procedures for data collection, storage, and access. Further, considerable effort will be required to develop procedures for data conversion, processing, and integration at a project or network level. These efforts may be unique to each agency because QA and PMS databases generate different formats and use different referencing systems. This report provides a framework and broad guidelines for agencies to integrate QA and construction data as leading indicators of performance into their PMS.