Project Participants:
Queensland University of Technology (QUT)Central Queensland University (CQU)Monash University (MU)University of Wollongong (UOW)
Industrial Partners:
V/LineDepartment of Transport VictoriaRio TintoARTC & KiwiRail
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
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 TasRail and
– 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
An Overview
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
Maintenance Optimisation Process - Summary
Future conditions of the components (UOW and
MU)
Rating based on structural Criticality and Vulnerability
analysis (QUT)
Rate Bridges based on current and future conditions (Synthetic rating)
Remaining life + Intervention frequencies
Current conditions of the components from inspection
Alternative management strategies
MR&R optimisation
Work orders
QUT
UOW+MU+QUT
CQU
CQU
Phase 1Phase 1
Framework of the Proposed BMS
Inspection module
Synthetic rating module
Bridge Inventory Data
Environmental classification
Deterioration modelling
Bridge Classification
Loading
QUT UOW+MU
Future Condition Assessment (Prediction)
Current Condition Assessment
Intervention frequencies
Mai
nten
ance
His
tory
QUT+ UOW+MU
QUT
Future condition of components
Remaining Service Potential (RSP) of components
Rating based Criticality and Vulnerability
Flood, Wind, EarthquakeVehicle collision,
Environmental effects
Phase 2 Phase 2
Framework of the proposed BMS (cont)
Maintenance quality or political decisions
UnacceptableBudget limits
Project level optimization
Network level optimization(Network level criticality)
Component interaction
Analysis period, analysis scenarios and base case
Define alternative bridge management strategies(Preventative maintenance, Repair work, Strengthening, Replacement, Do Nothing)
Calculate Net Present Value
Minor works or Regular repair
Estimate costs· Agency & routine maintenance· User, work related, other· Vulnerability cost
Modify management strategies
MR&R optimization module
Assignment of projects to work groups
Prepare work bids and plains
Select preferred strategy
Record maintenance history
Maintenance implementation
Per
form
ance
rev
iew
CQU
Classification (or Categorisation) of network of bridges
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
(a) Network level Analysis Results By using one TPM
A typical example for a TPM of a primary beam (Average Performance Index vs Age)
(b) Network level Analysis By using multiple TPMs (c) Application of Markov approach for approximate service life prediction of single components
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.
Remaining Service Potential of Steel Bridges (MU): (Failure Due to Corrosion and Cracking)
The engineering assessment of the durability requires a knowledge of both the operational usage and the environment (rate of corrosion).
Monitoring Corrosion on Bridge 44
Material behavior from 7 microns upwards can be represented as:
REPOS measured for Three Classes of Trains
Criticality and Vulnerability Analysis (QUT)
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.
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.
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
Synthetics Rating (QUT)Components Current Future
Foundation 1 2
Abutments 4 5
Back wall 3 4
Wing walls 1 2
Piers 5 5
Columns 4 5
Primary Beams
4 5
Secondary Beams
2 3
Deck 3 4
Joints 2 3
Current Future
58.8 66.08
61.6 72.8
56.28 64.68
47.25 53.1
109.2 109.2
92.4 109.2
84.7 100.1
59 67
75.04 86.24
35.4 40.2
(1) Condition rating (Inspection+ RSP)
(2) Criticality and Vulnerability analysisCurrent condition of the bridge: 679.67
Future condition of the bridge: 775.14
Factor Current Future
Flood 241.3 265.2
Wind 0.6 0.8
Earthquake 1.1 2.2
Collision 0.0 0.0
Environment 532.1 625.2
(3) Vulnerability rating of each bridge
(4) Synthetic rating of each bridge
Maintenance optimisation (CQU)
Managing Risks with Bridge ManagementR
isk
Miti
gatio
nR
isk
Man
agem
ent
Ris
k A
naly
sis
Haz
ards
Inputs: Inventory, Condition monitoring, SHM, Load ratings, Environmental, Economic, Repair knowledge bank, Future requirements.
Deterioration Mechanisms
(Age, chloride, carbonation, corrosion, etc.)
Unexpected events(Floods, fire, derailments,
cyclone, collision, etc.)
Overloading(Train overloading)
Current bridge condition
Deterioration model
Future bridge condition
Frequency of failure
(Flood 1:30years
Fire 1:10 years)
Consequences(injury, economic, etc.)
Failure modes
Multi objective optimisation(risk, costs, reliability, condition, etc.)
Bridge criticalityEconomic and
repair costs
Schedule maintenance
Reduce bridge load capacity Reduce train speedCondition
monitoring
Priority Order
Element Criticality
Defect Severity
Network Criticality
1 4 5 Use
Highest%
first
2 4 43 3 54 3 45 2 56 2 47 1 58 1 49 4 3
10 3 311 2 312 1 3
Maintenance optimisation... Priority ranking
Risk Priority Number
Probability of Failure Consequences of Failure Consequences of Failure
Safety EnvironmentFunctionality Sustainability
Element criticality Network criticality Inspection cost (to reduce the risk)
Maintenance/ repair cost
Bridge element criticality ratingCriticality
RatingDescription
1 Non-structural2 Structural with redundancy3 Protective4 Structural without redundancy
Network Criticality
Repair priority ranking
Proposed Software Platform
AcknowledgementTo our Industrial partners including V/Line, Rio Tinto and ARTC for their generous support.
V/line– North East corridor
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