Authors: John Hickey, Brian Broderick, Terence Ryan

1. Evaluating the use of OpenSees for Lifetime
Seismic Performance Assessment of Steel
Frame Structures
John Hickey
Prof. Brian Broderick
Terence Ryan
Department of Civil, Structural and Environmental Engineering

2. Performance of CBFs
Concentrically Braced Frames (CBFs)
‒ Diagonal bracing members resist lateral load though axial
tension and compression
‒ Energy dissipated through buckling and tensile yielding of
braces
‒ High lateral stiffness
‒ Unlikely to suffer collapse when designed to modern
codes
‒ Can still suffer significant losses
» Due to damage to structural & and particularly non-
structural components
Concentrically Braced Frame Steel Structures

3. Overall Aim
Examine the influence of the behaviour factor (q) on the lifetime seismic
performance of CBFs
‒ q used to account for nonlinear behaviour in
design while avoiding the need for nonlinear
analysis
‒ Influence of q on lifetime costs?
Department of Civil, Structural and Environmental Engineering
Research Goal

4. Performance Assessment
PEER Equation -
‒ 4 Probability Distributions
» Seismic Hazard Assessment – number of different Hazard levels
» Structural Analysis – Peak drift & floor acceleration
» Damage Assessment – Structural & Non-structural Components
» Consequence Estimation – Probable repair costs, downtime, casualties etc.
Department of Civil, Structural and Environmental Engineering
US Geological Survey
http://geohazards.usgs.gov/hazardtool/application.php
OpenSees
(McKenna, 1997)
PACT (FEMA P-58)
(ATC, 2012)
Components of Lifetime Performance Assessment Procedure
Seismic Hazard
Ground
Motion
Records
OpenSees – Time History Analysis
Performance Metrics
Engineering
Demand
Parameters

5. OpenSees in Performance Assessment
Structural analysis for a given seismic hazard
Calculate Engineering Demand Parameters (EDPs)
‒ Peak inter-storey drift
‒ Peak absolute floor acceleration
Question
‒ How accurately do OpenSees models represent CBF response?
Role of OpenSees in Lifetime Performance Assessment

6. BRACED
‒ Shake table test program examining CBF response (Broderick et al., 2015)
‒ Various structural configurations at 50%, 10% and 2% in 50 year
intensities
BRACED Experimental Test Program

7. Model Accuracy
Results from to OpenSees models can be compared to BRACED results
‒ Allowing model accuracy to be evaluated
Approach
‒ Using ‘conventional‘ modelling procedures, what is the level of
uncertainty associated with OpenSees models compared to
experimental results for EDPs of interest?
How do conventionally modelled CBFs compare to experimental result

8. OpenSees Model
Material
‒ Steel02 – Giuffré-Menegotto-Pinto Model
Braces
‒ Uriz et al., 2008
» 2 nonlinearBeamcolumn elements
» Initial camber 0.1% of brace length at midpoint
» 3 Integration points per element
» Fibre Section
‘Conventional’ Modelling Assumptions

9. OpenSees Model
Gusset Plates
‒ Hsiao et al., 2012
» Out of plane nonlinear rotational springs
Beams & Columns
‒ Nonlinear BeamColumn elements
» 3 Integration points per element
» Fibre Section
Analysis
‒ Ground motion = recorded shake table motions
‒ 3% Rayleigh damping at 1st and 3rd mode
‘Conventional’ Modelling Assumptions

10. OpenSees Model
Illustration of OpenSees model geometry

11. OpenSees Model
Illustration of OpenSees model geometry superimposed on test frame

12. OpenSees vs Experimental EDPs
Peak Drift
‒ Underestimated at all intensity
levels
» Typically model predicts about
60% of experimental value
‒ Attributable to:
» Model underestimating
flexibility in connections
Comparison between numerical and experimental results

13. OpenSees vs Experimental EDPs
Peak Acceleration
‒ Overestimated at 50% in 50 year
intensity level
» On average model predicts
135% of experimental value
‒ Good estimates at 10% and 2% in
50 intensity level
» Peak acceleration limited by
yield strength
Comparison between numerical and experimental Results

14. OpenSees vs Experimental EDPs
Summary
‒ OpenSees model underestimates peak elastic and inelastic drift (about 60%)
‒ OpenSees model overestimates peak elastic acceleration (about 135%)
‒ Predicts peak inelastic acceleration reasonably well (±10%)
Question
‒ How do these inaccuracies impact on the performance assessment procedure?
Comparison between numerical and experimental Results

15. Impact on Performance Assessment
Influence of modelling inaccuracies on Performance Assessment Procedure
Case Study Building
‒ Single storey CBF with BRACED
frame as seismic resisting
element
» Performance assessment
using PACT
1. Experimental EDPs
2. Numerical EDPs
‒ Allows us to see how different
EDP values impact on
performance metrics

16. Impact on Performance Assessment
Influence of modelling inaccuracies on Performance Assessment Procedure
Case Study Building
‒ Performance metrics with
Numerical EDPs less than those
with Experimental EDPs

17. Impact on Performance Assessment
Influence of modelling inaccuracies on Performance Assessment Procedure
Dealing with Modelling
Uncertainty in PACT
‒ Factor βm
‒ Increases covariance of EDP
distributions to account for
uncertainty

18. Impact on Performance Assessment
Influence of modelling inaccuracies on Performance Assessment Procedure
Performance Metrics – Including
Modelling Uncertainty
‒ Including factor to account for
uncertainty for numerical EDPs
‒ Using PACT recommended value
of βm
‒ Performance metrics from
numerical and experimental EDPs
are very similar

19. Impact on Performance Assessment
Influence of modelling inaccuracies on Performance Assessment Procedure
Summary
‒ Performance metrics from numerical and experimental EDPs similar when
uncertainty accounted for along with numerical values
‒ Level of modelling uncertainty found agrees with that anticipated in PACT model
‒ Can proceed with performance assessment with some level of confidence

20. Impact of q on Lifetime Performance
Frames Analysed
Frames Analysed
‒ Perimeter CBFs
‒ 2 & 5 storey CBFs
‒ q = 1, 2, 3, 4, 5
‒ 10 storey CBFs
» q = 2, 3, 4
‒ Case study site in Oakland, CA.
‒ Modelled in OpenSees as before
‒ Time history analysis performed using
ground motions records matching the
Conditional Spectrum at the site

21. Impact of q on Lifetime Performance
Sample EDP Results – Peak Inter-storey Drift

22. Impact of q on Lifetime Performance
Sample EDP Results – Peak Floor Acceleration

23. Impact of q on Lifetime Performance
Expected Annual Losses
Sample Results – Expected Annual
Losses
‒ PACT used to calculate performance
metrics using EDPs from OpenSees
‒ Losses in $/m2
‒ Losses represented as financial losses
due to repair and downtime
‒ Estimated losses increase with the
behaviour factor

24. Impact of q on Lifetime Performance
Expected Annual Losses – Percentage of Initial Costs
Sample Results – Expected Financial
Losses – Including Initial Costs
‒ Losses as percentage of initial costs
‒ Initial costs estimated as $250 per ft2
‒ Initial costs increase as behaviour
factor reduces - estimated by cost of
extra weight of steel

25. Conclusion
Summary
• Investigated the level of uncertainty in OpenSees models using experimental results
• Verified that the assumed level of modelling uncertainty in the performance
assessment is in line with this
• Examined the impact of the behaviour factor on lifetime seismic performance for case
study CBFs
• Shown expected losses increase with the behaviour factor
Summary of work discussed