Best Presentation Winner at the 8th International Conference on Weigh-In-Motion, which took place in Prague in May 2019. Authors: Lorcan Connolly, Roisin Donnelly, Alan O'Connor, Eugene O'Brien

1. Application of WIM Data for
Probabilistic Bridge Assessment
Roisin Donnelly, Lorcan Connolly, Alan
O’Connor, Eugene OBrien.
Session 6

2. Introduction
The problem: Maintenance of aging infrastructure
– 30% of Europe’s bridges > 100 years old
Problem highlighted by recent failures
Budget constraints often prohibit effective repair /
identification of problems. This is compounded by
codified load models which are not representative
of the bridges to which they are assessed
- Application of WIM data and probabilistic methods
to appropriately assess bridge structures
- Novel Application to Railway WIM – potential for
load modelling across an entire network!
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Lorcan Connolly - Application of WIM data for Assessment of Bridges

3. Case Study: Ebbw River Railway bridge, Wales
• Steel plate girder bridge - constructed 1966
• Two identical single spans of 24.5m
• Only one span needs to be assessed at ULS
• Proximity to WIM site – no junction between
• Two main girders with cross girders between
• Ballasted track on steel deck plate
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Lorcan Connolly - Application of WIM data for Assessment of Bridges
WIM Site

4. Deterministic Assessment to DMRB
• FE Modelling (MIDAS Civil)
• Plate elements for deck plate (omitted from figure)
• Linear elastic beam elements elsewhere
• Loading comprised of Dead load, SDL, Live Load
• Live Load is RU loading from BD 37/01
• Yield stress of 247MPa (specified on drawings)
• Dynamic Amplification Factors for bending:
• Cross Girder DAF = 1.71
• Main Girder DAF = 1.47
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Lorcan Connolly - Application of WIM data for Assessment of Bridges

5. WIM data
• Calibrated a minimum of three times per day!
• Maximum error < ± 3%
• Data for 46 days used in this study.
• Heavy axle loads present in data!
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Lorcan Connolly - Application of WIM data for Assessment of Bridges
• Fibre optic sensors installed
under rail
• Static & dynamic loads recorded
• Averaging across sensors

6. Probabilistic Assessment
Stress check of main girder & cross girder
Load & Resistance variables modelled as random
variables
Account for uncertainty in parameters
Allows calculation of reliability (β) and failure
probability
Model uncertainty of 5% on permanent loading,
9% on resistance (DRD, 2004)
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Lorcan Connolly - Application of WIM data for Assessment of Bridges
Element Property Mean
Standard
Dev.
Cross
Girder
Dead Load 5.6 MPa 0.3 MPa
SDL 9.6 MPa 1.0 MPa
Yield Stress 304 MPa 25 MPa
Main Girder
Dead Load 17.3 MPa 0.9 MPa
SDL 18 MPa 1.8 MPa
Yield Stress 283 MPa 25 MPa
Connection
(CG – MG)
Dead Load 0.2 kN 0.01 kN
SDL 0.4 kN 0.04 kN
Yield Stress 304 MPa 25 MPa
Permanent Load & Resistance Random Variables

7. Live Load Modelling – codified approach
Assume RU loading to be 98% fractile value of
Gumbel distribution
Conservative CoV of 20% → mean value = 0.66
“Medium” uncertainty → CoV of 15%
Dynamic amplification:
DAF = 1 + ε
Use CoV = 100% and mean value which
produces 98% fractile value equal to
deterministic DAF:
εmean (cross girder) = 0.23
εmean (main girder) = 0.04
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Lorcan Connolly - Application of WIM data for Assessment of Bridges
Probabilistic Assessment

8. Live Load Modelling – WIM approach
Load Effects are worse for the cross girder:
We expect the failure probability to be higher
for the cross girder!
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Lorcan Connolly - Application of WIM data for Assessment of Bridges
Probabilistic Assessment
Tail of stress data raised to the power of n for
max yearly distribution of stress

9. Live Load Modelling – WIM approach
Lower uncertainty (10%) based on recorded data
DAF modelled according to Eurocode approach
for real trains:
φ’’ is constant (track irregularities)
φ’’ is modelled from the dynamics recorded
by the WIM system
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Lorcan Connolly - Application of WIM data for Assessment of Bridges
Probabilistic Assessment

10. Results
FORM analysis programmed in MATLAB to
calculate reliability and failure probability for
each element
HL-RF method used (works for linear
performance functions):
G(x) = R – L
Target reliability = 4.2!
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Lorcan Connolly - Application of WIM data for Assessment of Bridges
Probabilistic Assessment

11. Conclusions
• Railway WIM can be used to develop a load model for almost any
structure on a network, with relatively few measurement sites.
• Application of WIM data in a probabilistic assessment framework can
show some elements to be less safe than deterministic codified
approaches.
• Live streaming of data can be combined with these approaches to
monitor reliability over time
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Lorcan Connolly - Application of WIM data for Assessment of Bridges

12. Further work
Consideration of structural
redundancy showing sufficient
safety
Probabilistic consideration of
Fatigue Limit State, with
application of WIM data
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Lorcan Connolly - Application of WIM data for Assessment of Bridges

13. Acknowledgement
The research leading to these conclusions were part of the SAFE-10-T
project, funded by the European Union H2020 programme
(GA number 723254)
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Lorcan Connolly - Application of WIM data for Assessment of Bridges