In recent years, state highway agencies have come to understand the need for high quality pavement condition data at both the project and network levels. At the same time, agencies also realize that they have become too dependent on contractors to ensure the quality of the delivered data without any means to independently assure the quality of these data. This research study therefore aims to investigate the inherent variability of the automated data collection processes and proposes guidelines for an automated data collection quality management program in Indiana. In particular, pavement roughness data (in terms of IRI) and pavement surface distress data (in terms of PCR and individual pavement surface distress ratings) are considered in this study. Quality control protocols adopted by the contractor are reviewed and compared against industry standards. A complete quality control plan is recommended to be adopted for all phases of the data collection cycle: preproject phase, data collection phase, and post-processing phase. Quality assurance of pavement condition data can be viewed in terms of (i) completeness of the delivered data for pavement management; (ii) accuracy, precision and reliability of pavement roughness data; and (iii) accuracy, precision and reliability of individual distress ratings and an aggregate pavement condition rating. An innovative two-stage approach is developed in this study to evaluate delivered data for integrity and completeness. Different techniques and performance measures that can be used to evaluate pavement roughness and pavement surface distress data quality are investigated. Causes for loss in IRI and PCR accuracy and precision are identified and statistical models are developed to relate project- and network-level IRIs and PCRs. Quality assurance procedures are then developed to allow highway agencies improve their pavement condition data collection practices and enhance applications in the pavement management systems.

"This new ASTM publication presents the latest information on the practical and developmental aspects of pavement surface evaluation procedures and technologies, including their reliability and relevancy. Seven peer-reviewed papers cover: * Pavement surface characteristics measurement procedures and equipment * Approaches to enhance the reliability and accuracy of pavement surface evaluation systems * Approaches to harmonization between different measurement devices for specific pavement surface condition indicators * Assessment of current pavement condition indicators and their relevancy level for use in asset management * Assessment of factors influencing the interaction of tire/pavement surface characteristics * Assessment of automated distress survey systems * Evaluation of new/promising technologies for pavement condition surveys."--Publisher's website.

This study was in two parts. The first part established and demonstrated a framework for pavement data integration. This is critical for fulfilling QC/QA needs of INDOT¿s pavement management system, because the precision of the physical location references is a prerequisite for the reliable collection and interpretation of pavement data. Such consistency is often jeopardized because the data are collected at different years, and are affected by changes in the vendor, inventory, or referencing system or reference points. This study therefore developed a ¿lining-up¿ methodology to address this issue. The applicability of the developed methodology was demonstrated using 2012-2014 data from Indiana¿s highway network. The results showed that the errors in the unlined up data are significant as they mischaracterize the true pavement condition. This could lead to the reporting of unreliable information of road network condition to the decision makers, ultimately leading to inappropriate condition assessments and prescriptions. Benefits of the methodology reverberate throughout the management functions and processes associated with highway pavements in Indiana, including pavement performance modeling, optimal timing of maintenance, rehabilitation, and reconstruction (MRR), and assessment of the effectiveness of MRR treatments and schedules.The second part of the study developed correlations for the different types of pavement distresses using machine learning algorithms. That way, the severity of any one type of distress can be estimated based on known severity of other distresses at that location. The 2012-2014 data were from I-70, US-41, and US-52, and the distress types considered are cracking, rutting, faulting, and roughness. Models were developed to relate surface roughness (IRI) to pavement cracks, and between the different crack types, with resulting degrees of confidence that varied across the different crack types and road functional classes. In addition, for each functional class and for each crack type, models were built to relate crack depth to crack width. The concept can be applied to other distress types, such as spalling, bleeding, raveling, depression, shoving, stripping, potholes, and joint distresses, when appropriate data are available.

Automated collection of pavement data allows agencies to collect data on pavement health, including cracking, rutting, faulting, and roughness, at highway speeds. This provides important information for better pavement decision-making. The TRB National Cooperative Highway Research Program's NCHRP Synthesis 589: Automated Data Collection and Quality Management for Pavement Condition Reporting documents the experiences, challenges, and state-of-the-practice solutions used by state departments of transportation that are in the midst of transition or that have transitioned to automated and semiautomated processes for collecting pavement data. It also summarizes the data for state and federal reporting requirements, such as Transportation Asset Management Plans and MAP-21.