Asset management

Cover of Interim Network Level Functional Road Deterioration Models
Interim Network Level Functional Road Deterioration Models
  • Publication no: AP-T158-10
  • ISBN: 978-1-921709-50-0
  • Published: 30 November 2010

This report documents the development of interim network level road deterioration (RD) models for the roughness, rutting and cracking of sealed granular pavements.

These RD models can be used at the network level for the strategic analysis of the impact of increased traffic loading, climate change, changes to maintenance and rehabilitation strategies and the estimation of the cost of road wear. The observational data for the RD model development were gained from the long term pavement performance (LTPP) and long term pavement performance maintenance (LTPPM) sites, including additional cracking deterioration data from South Australia. The observational data were extended by experimental data from accelerated load testing (ALT) full scale simulations with the Accelerated Loading Facility (ALF) to increase the range of the data for possible changes to maintenance and axle loads.

  • 1. INTRODUCTION
    • 1.1. Background
    • 1.2. Scope of Report
  • 2. SUMMARY OF OBSERVATIONAL AND EXPERIMENTAL DATA FOR MODEL DEVELOPMENT
    • 2.1. Observational Data
      • 2.1.1. Assessment of Variables
    • 2.2. Experimental Data
      • 2.2.1. Limits to Deterioration Prediction
      • 2.2.2. Development of Relative Performance Factors for Changes in Maintenance
      • 2.2.3. Development of Relative Performance Factors for Changes in Axle Loads
  • 3. NETWORK DETERIORATION MODEL DEVELOPMENT
    • 3.1. Rutting Deterioration Model
    • 3.2. Cracking Deterioration Model
      • 3.2.1. Cracking Deterioration Model for Sprayed Sealed Pavements
      • 3.2.2. Cracking Deterioration Model for Asphalt Surfaced Pavements
    • 3.3. Roughness Deterioration Model
      • 3.3.1. Cumulative Roughness Deterioration Model
      • 3.3.2. Incremental Cumulative Roughness Deterioration Model
  • Cumulative rutting model predictions
  • 4. CONCLUSIONS
    • 4.1. Scope
    • 4.2. Findings
      • 4.2.1. RD Model Summary
    • 4.3. Recommendations
  • REFERENCES
  • APPENDIX A DETAILS OF OBSERVATIONAL DATA
  • APPENDIX B DEVELOPMENT OF RELATIVE PERFORMANCE FACTORS FROM ALF DATA