Modelling subjective condition data of asphalt surfaced urban pavements

Pavement surfacing condition data of major highways in Melbourne/ Australia are collected using visual inspection (subjective) surveys. The surveys are conducted every two to three years by experienced personnel following detailed guidelines. The subjective condition data collected for asphalt surfacing include cracking, stone loss, texture loss, deformation and patching. The condition ratings of these five distresses are then combined into a single measure referred to as Surface Inspection Rating (SIR). Deterioration models of SIR are developed using regression analysis and Markov Chains (MC). These models are to be used in identifying and planning resurfacing maintenance priorities at network level. A comparison between the output models of the two approaches is presented and assessed in terms of predictions reasonableness and accuracy. The results indicate that both models predict the same rate of deterioration over time but MC model using weighted average of probabilities predicts higher SIR values at all ages than the regression model.

Language

  • English

Media Info

  • Pagination: 10p
  • Monograph Title: Transport Research Arena (TRA) 2014 Proceedings

Subject/Index Terms

Filing Info

  • Accession Number: 01534618
  • Record Type: Publication
  • Source Agency: ARRB
  • Files: VTI, TRIS, ATRI
  • Created Date: Aug 14 2014 10:16AM