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Pavement management system review : final report, October 2009.
  • Published Date:
    2009-10-01
  • Language:
    English
Filetype[PDF-4.57 MB]


Details:
  • Publication/ Report Number:
    930-598
  • Resource Type:
  • Geographical Coverage:
  • OCLC Number:
    658060935
  • Edition:
    Research report; 2003-2008.
  • NTL Classification:
    NTL-HIGHWAY/ROAD TRANSPORTATION-Pavement Management and Performance ; NTL-HIGHWAY/ROAD TRANSPORTATION-HIGHWAY/ROAD TRANSPORTATION ;
  • Format:
  • Abstract:
    The University of Alabama (UA) researchers worked with Alabama Department of Transportation (ALDOT) managers to investigate various mechanisms to provide quality control of data collection and interpretation for pavements, and to target the data results at system decisions. Toward this end, protocols and statistical methods were created and implemented.

    A review of the work steps to develop an automated pavement condition collection process is described along with the collection criteria based on AASHTO PP 44-01, Quantifying Cracks in Asphalt Pavement Surface, used by the ALDOT. During the course of this project multiple vendors, rating approaches and technologies were used. Specifically the resolution of the automated pavement image capture systems (i.e., cameras) improved from 3 mm to 1mm. Likewise the size and resolution of display monitors increased. At the same time, the underlying algorithms for automated interpretation of video data ranged from rule-based to statistical pattern recognition, intensity was added to strobe lighting, and laser lighting was introduced. Overall the trend is improved technology.

    However, the statistical analysis performed on field data found that interpretation reliability among the state quality control staff, the automated interpretation systems and human interpreted images vary considerably. Variability was introduced throughout the collection process beginning with establishing pavement baseline measures, to image collection, to rater interpretation of the images. The field work also identified the need for repeated quality control in calibrating sensors and an overall assurance program.

    In summary the ALDOT-UA team recognizes the improvements in image acquisition and interpretation technologies over the course of the four year project, and concludes that automated collection of pavement distress is becoming better, especially where guided by expert human review and enhancement. However, field tests were not conclusive that the current state of practice allows consistent capture and interpretation of 1/25th inch to 1/8th inch pavement cracks desired for high order modeling of pavement deterioration.

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