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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.
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.
At head of title: National Cooperative Highway Research Program.
TRB's National Cooperative Highway Research Program (NCHRP) Synthesis 531 documents agency practices, challenges, and successes in conducting automated pavement condition surveys. The report also includes three case examples that provide additional information on agency practices for conducting automated pavement surveys. Pavement condition data is a critical component for pavement management systems in state departments of transportation (DOTs). The data is used to establish budget needs, support asset management, select projects for maintenance and preservation, and more. Data collection technology has advanced rapidly over the last decade and many DOTs now use automated data collection systems.
The Ohio Department of Transportation (ODOT) has been collecting 3D digital data on their pavement network since 2014. This data contains a variety of information derived from 3d laser scans of the pavement. While ODOT has been using the data to meet federal HPMS reporting requirements of pavement condition, the agency wished to leverage this wealth of data to aid their pavement management system and transition from a manual pavement condition survey to an automated one. This research aims to provide ODOT a means to interpret the data and use it to make the same decisions as the existing pavement management system. Topics include analysis and development of a new rating methodology for automated distress detection and classification as well as deterioration models and decision trees for the new rating methodology. The rating system was developed using comparisons with existing manual ratings and automated data collected from 2014 through 2018. Additionally, the report covers how to implement this methodology and how it impacts pavement management decisions.
TRB's Airport Cooperative Research Program (ACRP) Report 39: Recommended Guidelines for the Collection and Use of Geospatially Referenced Data for Airfield Pavement Management offers recommended guidelines for the collection and use of geospatially referenced data for airfield pavement management. The guidelines provide a data schema, data collection methods, data quality requirements, and other relevant information required for developing specifications and standards for integrating geospatial data into pavement management systems. Appendixes A through C to ACRP Report 39 are available online. Titles of the appendixes are as follows: Appendix A: Survey Questionnaire; Appendix B: Questionnaire Responses; Appendix C: Pavement Management Systems Software Data Elements.