Evaluation of Benefit Weight Factors and Decision Trees for Automated Distress Data Models

Details

• Creators:
  Chen, Don ; Hildreth, John C ; Nicholas II, Thomas ; James, Steven L
• Corporate Creators:
  University of North Carolina at Charlotte. Dept. of Engineering Technology and Construction Management
• Corporate Contributors:
  North Carolina. Dept. of Transportation. Research and Development Unit ; United States. Department of Transportation. Federal Highway Administration
• Subject/TRT Terms:
  • Automatic data collection systems
  • Benefit cost analysis
  • Decision trees
  • Evaluation and assessment
  • Pavement distress
  • Pavement maintenance
  • Pavement management systems
  • Pavement performance
  • Performance Model
  • Distress Model
  More +
• Publication/ Report Number:
  FHWA/NC/2015-01
• Resource Type:
  Tech Report
• Geographical Coverage:
  United States
• Edition:
  Final Report 8/16/2014 – 12/31/2017
• Corporate Publisher:
  North Carolina. Dept. of Transportation
• Abstract:
  A Pavement Management System (PMS) becomes an effective decision-making tool when its performance models are accurate, and its trigger points (values) and benefit weight factors are precisely calibrated. Since 2011, NCDOT began collecting pavement distress data using automated methods. These data were make available to this study. New distress and performance models were developed to take advantage of the increased data quality and quantity. These new models present different deterioration rates, benefit curves, and decision trees. Therefore, new trigger points and benefit weight factors were determined to reflect the new changes. The findings of this study can assist the NCDOT Pavement Management Unit (PMU) to make effective maintenance and rehabilitation decisions.
• Format:
  PDF
• Funding:
  8/16/2014 – 12/31/2017
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/62259/dot_62259_DS1.pdf
• File Type:
  [PDF - 11.09 MB]
• Collection(s):
  US Transportation Collection
• Main Document Checksum:
  urn:sha256:133890f56b722d380b672e058ac10482edb2e09a911a483b0aa9616f8239da8b