PREDICTING ROUGHNESS PROGRESSION OF ASPHALT OVERLAYS: JOINT C-SHRP/ALBERTA BAYESIAN APPLICATION

A Bayesian regression model was developed to predict the roughness progression of asphalt overlays (first rehabilitation cycle) placed on existing asphalt concrete pavements with granular base courses. The model will be applicable in central Alberta. Field data used in the analysis consisted of 311 records from the Alberta pavement management system. In accordance with Bayesian statistical theory, the "prior knowledge" of the modelling system was provided by five pavement engineers from the agency. Their expert judgment was encoded and linked to form the input prior. Combining the field data with the input prior resulted in a model which expresses the riding comfort index as a function of the RCI of the original pavement (RCI perf), a soil factor, overlay thickness, age, initial RCI, and cumulative traffic loading. The model can be used in Alberta's PMS although some changes in the PMS program would be needed to facilitate its integration into the software. The authors believe that the Bayesian modelling tool should be further investigated by Alberta Transportation & Utilities, particularly when historical data is limited. Furthermore, the methodology is not limited to pavement engineering problems. Other areas where Bayes can potentially prove useful include traffic engineering, location studies and geotechnical engineering applications. (A)

  • Availability:
  • Corporate Authors:

    Canadian Strategic Highway Research Program

    2323 St Laurent Boulevard
    Ottawa, Ontario K1G 4J8,   Canada 
  • Authors:
    • KURLANDA, M H
    • KAJNER, L
  • Publication Date: 1995-10

Language

  • English

Subject/Index Terms

Filing Info

  • Accession Number: 00726431
  • Record Type: Publication
  • Source Agency: Transportation Association of Canada
  • ISBN: 1-55187-032-0
  • Files: ITRD
  • Created Date: Oct 28 1996 12:00AM