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ANALYSIS AND MITIGATION OF RISKS IN INFRASTRUCTURES | INFRARISK-July 19

Hugo Guimarães

Risk analysis of bridges using a new reliability-based robustness assessment methodology

2Hugo Guimarães / Risk analysis of bridges using a new reliability-based robustness assessment methodology

Agenda

Risk-informed decision making• Motivation and Research Goal

• Metamodeling-based methods for reliability analysis

• Framework for robustness assessment

• Case studies

• Additional Contributions

• Conclusions and Future Developments


Bridge Management Systems

Risk-informed decision making

Transportation Network

Chackkebges

EfficientManagement

DecayProcess

LimitedResources

PublicExpectations

PublicDemands

…Challenges for Owners


Bridge Management Systems

Performanceindicator

NDTTesting

VisualInspection

Monitoringsystem

Conditionrating

…Component Level


Recent Events

… More than 40 bridge failures were reported since 2014!!!

… Majority of them was made by concrete

… Main reasons were:• Faulty design or construction error• Loss of Key elements by:

• Collision• Landslide• Scour

• Poor Maintenance


Recent bridge failures


Recent bridge traffic disruptions


Research Goal

Reliability Analysis

NonlinearFE Analysis

Robustness Assessment


The goal is to contribute new generation of risk-based bridge management systems



Robustness assessment methodology

Performance underextreme sudden events

Performance underservice conditions

Criticality level Risk indicatorTransportation Network

Structural Performance

… System-level performance… Wide spectrum of Key Performance Indicators

Reliability analysis

Consequence analysis


Metamodeling for Reliability Analysis

Model Evaluation

Initial Design of Experiments

Model Selection and Model Fitting

Convergence

DoE

Metamodel Goal

Region of Interest Updating DoE

Model Validation

… More than 40 papers were reviewed! “Metamodeling-based methods for reliability analysis: a comparative review and insights for future developments” will be submitted to Reliability engineering and System Safety


Metamodels

Response Surface Methods

Kriging or Gaussian Process

Artificial Neural Networks (ANN)

Support Vector Machines (SVM)

Polynomial Chaos Expansion (PCE)

… Benchmarking methods is not a straightforward task!Still a reduced list of test examples was selected.


Dynamic Problem with 6 RV

Kriging ANN SVM PCE Spline iRS


3-bay 5-story Building Frame (21 RV)

PCE RSM iRS

Correlation is not efficiently addressed by existing RSM


Developed Method - iRS

… Main features• Optimized DoE• Double weighted regression technique• Stepwise Selection• Region of Interest• Cross-Validation• CI based on bootstrapping


Structural Performance Assessment

Definition Limit State Function

Reliability Analysis

Performance Assessment

𝐺 𝑋 = ⋯ 𝑃& = 𝑃(𝐺 𝑋 < 0) 𝛽 < 𝛽,-./0,

Deflection control Crack controlStress level

Resistance capacityStress level

Nonlinear FE Analysis

iRS method Code-basedRecommendation



Normalized risk indicator which considers consequences of disruption

Sabatino (2015a, 2015b)

Maximum admissible direct losses: % annual budget for maintenance

Maximum admissible indirect losses: % GDP of affected region


Transportation NetworkInsignificantConsiderableSeriousSevere

Very Likely (50-100%)

Likely (30-50%)

Occasional (10-30%)

Unlikely (0-10%)

Negligible - 1Acceptable - 0,8Controllable - 0,6Critical - 0,2Unacceptable - 0

Disastrous

Ruan et al. (2015a)

Utility indifference curves for risk-averse attitude


Case studies… Short-span RC bridge studied by Wong et al. (2005)

1. Several numerical difficulties were encountered using classical RSM (Wong et al. 2005)

2. Typical configuration of highway overpasses

3. Life-cycle performance assessment


Simply supported girder bridge

0

1

2

3

4

5

0 20 40 60 80

Relib

ailityIndex

TimesinceCorrotionInitiation[years]

Onlycorrosion OnlyLiveLoad

BothCorrosionandLiveLoad

-2

-1

0

1

2

3

4

5

0 20 40 60 80

Relib

ailityIndex


Onlycorrosion Failure ofExterior Girder

βsys

βdam

Target reliability based on Ghosn and Yang (2014)NCHRP Report 776

LLXLFXG −= )()(


Simply supported girder bridge

0

1

2

3

4

5

6

0 20 40 60 80

Relib

ailityIndex

ServiceTime[years]

Onlycreep andshrinkage Corrosion (low intensity)

βserv = 1.3EN-1990 (2002)]

JCSS (2001) and ISO2394 (2015)

βserv = 1.5


Tercenas Bridge


Tercenas Bridge

2

3

4

5

6

0 5 10 15 20 25 30

Relib

ailityIndex


CorrosiononlyG1 General Corrosion

βsys

0

1

2

3

4

5

6

0 5 10 15 20 25 30

Relib

ailityIndex


CorrosiononlyG1 Failure Exterior Girder

βdam

The structure presents a satisfactory reserve capacity and redundancy.


Comparing PerformanceSimply supported bridge Tercenas Bridge


Additional Contributions



Conclusions

Risk-informed decision making• Research needs vs Research Goals

• Challenges and solutions in metamodeling-based reliability

• Alternative approach to prioritize maintenance actions in bridges

• Contributions to academical and practical environment


Future Developments and suggestions to research

Risk-informed decision making• Introducing clustering algorithms to deal with multiple failure modes

• Performance metrics at network level can still be improved and include

probabilistic nature (flow-based and topology-based)

• Extend and disseminate developed software

• Include multiobjective optimization in the proposed framework

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