摘要: |
The research team will assess current automated and semi-automated data collection practices and QC/QA practices in TxDOT. Researchers will evaluate the accuracy, precision, and reliability of the pavement distress data collection methods used in the network level for TxDOT’s PMIS data collection. The team will then develop an optimized sampling method for auditing the automated pavement condition data to reliably and efficiently locate the pavements with potential data quality problems, develop sound QA statistics based on both individual ratings/indexes and Scores and provide recommendations on the threshold values to detect potential data quality issue during data collection season. Ultimately, TSUSM researchers will establish QA guidelines, procedures, or specifications for automated and semi-automated pavement condition surveys that could be used by TxDOT to improve data quality management practices for contracting pavement condition data collection. The revisit and corrections of these potential inaccuracies in the distress data collection practice will effectively improve the accuracy and precision of the automated methods and quality and reliability of the PMIS data. The implementation of the research may help identify vulnerable condition data items, pavement classes, and pavement sections during the annual pavement data collection. The implementation of the research may help reduce the equipment cost and man hours spent in TxDOT used to verify the automated and semi-automated data. |