Project Participants: Queensland University of Technology (QUT)

2. Project Participants: Queensland University of Technology (QUT) Central Queensland University (CQU) Monash University (MU) University of Wollongong (UOW) Industrial Partners: V/Line Department of Transport Victoria Rio Tinto ARTC & KiwiRail

3. Outline of the Presentation • An overview • Common weaknesses of existing BMS in Australia • Maintenance optimisation process – summary • Framework of the proposed BMS • Classification (or Categorisation) of network of bridges • Prediction of Remaining Service Potential (RSP) • Durability Assessment of Steel Bridges: Failure Due to Corrosion and Cracking • Criticality and Vulnerability Analysis • Synthetics Rating • Maintenance optimisation

4. An Overview There are over 9,480 bridges in the major Australian Rail Networks: • 3,710 in Queensland Rail (including QRN); • 3,230 in ARTC; • 1,200 in RailCorp; • 990 in V/Line; • 350 in TasRailand • 40 in Rio Tinto • Over 30% of these bridges are over 80 years old • Replacement of 3000 bridges nationally at a cost of $4.5 Billion over 20 years

5. Common weaknesses of existing BMS in Australia • Syndromes and symptoms • Bridge classification (or categorisation) is generic • Inspection records are not detail enough for maintenance optimisation at network level • Deterioration models are not in use and remaining service potential cannot be predicted • Maintenance intervention points cannot be identified • Maintenance strategies cannot be compared (eg. Repair work, Strengthening) • Subjective maintenance work based on human judgements • Item vice cost cannot be identified and maintenance cost cannot be optimised

6. Maintenance Optimisation Process - Summary Current conditions of the components from inspection Rating based on structural Criticality and Vulnerability analysis (QUT) Future conditions of the components (UOW and MU) Rate Bridges based on current and future conditions (Synthetic rating) QUT Remaining life + Intervention frequencies UOW+MU+QUT Alternative management strategies CQU MR&R optimisation CQU Work orders

7. Framework of the Proposed BMS Phase 1 Bridge Inventory Data Inspection module Current Condition Assessment Future Condition Assessment (Prediction) Environmental classification Bridge Classification Loading Deterioration modelling Maintenance History QUT UOW+MU Flood, Wind, Earthquake Vehicle collision, Environmental effects Future condition of components Rating based Criticality and Vulnerability Remaining Service Potential (RSP) of components QUT Synthetic rating module Intervention frequencies QUT+ UOW+MU

8. Framework of the proposed BMS (cont) Phase 2 • Analysis period, analysis scenarios and base case Define alternative bridge management strategies (Preventative maintenance, Repair work, Strengthening, Replacement, Do Nothing) Performance review • Estimate costs • Agency & routine maintenance • User, work related, other • Vulnerability cost Modify management strategies MR&R optimization module Project level optimization CQU Minor works or Regular repair Component interaction Maintenance quality or political decisions Network level optimization (Network level criticality) Budget limits Unacceptable Calculate Net Present Value Select preferred strategy Maintenance implementation Prepare work bids and plains Record maintenance history Assignment of projects to work groups

9. Classification (or Categorisation) of network of bridges

10. Prediction of Remaining Service Potential (UOW) • Contributing factors : • Rail-traffic volume (Tonnage ) • Number of tracks, • Material type, • Functional class, • Nature of the defect • Structure type • Environmental categories, etc. • Markov chain based stochastic deterioration models were selected • Regression-based nonlinear optimization techniques were use to estimate the Transition Probability Matrixes (TPM) . • Deterioration curves were developed for classified element groups based on their; • Structural role • Maintenance requirements • Costing or inspection procedures • Environmental category • Traffic volume

11. A typical example for a TPM of a primary beam (Average Performance Index vs Age) (c) Application of Markov approach for approximate service life prediction of single components (a) Network level Analysis Results By using one TPM (b) Network level Analysis By using multiple TPMs

12. Highlights • Expected performance index curves and transition probability derived for entire life of a subcomponent can be used to comparison purpose and network level bridge management decisions. • Markov approach can be used to predict the average remaining service life estimation of individual components after considering non-homogeneity of the deterioration process, by considering separate Transition Probability for different time zones . • Inspection intervals need to be predicted by rating each important element of these components. • Accuracy of the service life estimation is depend on the reliability of the data. Transition Probability matrixes should be updated when new data available in the future.

13. Remaining Service Potential of Steel Bridges (MU): (Failure Due to Corrosion and Cracking) • Material behavior from 7 microns upwards can be represented as: • The engineering assessment of the durability requires a knowledge of both the operational usage and the environment (rate of corrosion). REPOS measured for Three Classes of Trains Monitoring Corrosion on Bridge 44

14. Criticality and Vulnerability Analysis (QUT) The degree of the criticality of the structural elements is identified by weighting factors • Criticality of the elements due to different structural configuration • Criticality of the factors according to the environmental condition • Critical factors: • Live Load • Environment factors such as corrosion and temperature • Extreme events such as Flood, Wind, Earthquake & Collusion The vulnerability may refer to the vulnerability of whole structure or vulnerability of the critical elements of the structure. Scope: Setup of Criticality and Vulnerability Rating Criteria: • The factors related to the Structural Condition are taken into account. • Bridges will be rated based on Synthetic Rating Method.

15. Synthetics Rating (QUT) (3) Vulnerability rating of each bridge • Condition rating • (Inspection+ RSP) (2) Criticality and Vulnerability analysis (4) Synthetic rating of each bridge

16. Maintenance optimisation (CQU)

17. 5 Maintenance optimisation... Priority ranking 7 8 8.0% 60.8% 3.9% 4 8.0% 6 64.7% 3 72.8% 9 23.4% 2 96.2% 10 3.8% 1 100% Risk Priority Number Consequences of Failure Consequences of Failure Probability of Failure Safety Functionality Environment Sustainability Element criticality Network criticality Inspection cost (to reduce the risk) Maintenance/ repair cost Repair priority ranking Bridge element criticality rating Network Criticality

18. Proposed Software Platform

19. Acknowledgement To our Industrial partners including V/Line, Rio Tinto and ARTC for their generous support. V/line– North East corridor