1. BRI Presentation 6 June 2005

2. This research study is undertaken by the Cooperative Research Centre for Construction Innovation (CRC CI). Research partners: RMIT University Queensland University of Technology (QUT) Organisations Partners: Queensland Department of Main Roads (QDMR) Queensland Department of Public Works (QDMP) Background

3. Objective of Research Study To improve reliability in budget/cost estimates for road asset management (Maintenance and rehabilitation)

4. Department of Main Roads has 34,000km of road network consist various pavement types, soils, traffic, environment Queensland have well developed Asset Management practices –Comprehensive, relevant, quality asset data ARMIS (A Road Management Information System) Database –Investment modelling tools: (SCENARIO) Improve reliability in budget estimates for road asset management Background

5. Background (Cont.)

7. Developed a probability-based method for assessing variability in budget estimates for highway asset management

8. Outline of Presentation Identification of critical parameters Demonstrate a method in assessing variation in budget estimates for road maintenance and rehabilitation

9. Part One Identification of critical parameters

10. Identification of Critical Input Parameters The variability of Input parameters Pavement strength Rut depth Annual equivalent number of axles Initial roughness for the analysis year Pavement thickness Cracking The variability of out parameters Annual change in pavement roughness

11. Identification of Critical Input Parameters ΔRI = Kgp (ΔRIs + ΔRIc + ΔRIr + ΔRIt) + m Kgm RIa ΔRIs = change in roughness due to pavement strength deterioration due to vehicles SNPK b =Modified Structural number YE4=Equivalent standard number of axles AGE3=Pavement age Kgp = calibration factor, Default value = 1.0 ΔRI = total change in roughness ΔRIc = change in roughness due to cracking ΔRIr=change in roughness due to rutting ΔRIt=change in roughness due to pothole (m kgm RIa = ΔRIe) =change in roughness due to climatic condition

12. Identification of Critical Input Parameters COV of Input Parameters Compared with COV of output Variable Note: COV is coefficient of variation (σ/μ)

13. Identification of Critical Input Parameters

19. Critical input parameters Pavement strength Rut depth Annual equivalent number of axles Initial roughness Unit costs

20. Case study Assessment of Variation in Budget Estimates for Road Maintenance and Rehabilitation 92 km Bruce highway Pavement strength Rut depth Annual average daily traffic (AADT) Initial roughness

21. Case study Assessment of Variation in Budget Estimates for Road Maintenance and Rehabilitation