1. Management of Earthquake Risks using Condition Indicators
Institute of Structural Engineering (IBK)
Yahya Y. Bayraktarli
Prof. Dr. Michael Faber

2. The Project Group MERCI
Project started in June 2004.
Interdisciplinary research group. Funded by the Swiss National Fund.
Participating Institutes from Swiss Federal Institute of Technology Zurich
Institute of Structural Engineering
Group Risk and Safety
Prof. Dr. Michael H. Faber
Dr. Jens Ulfkjaer
Yahya Y. Bayraktarli
Group Earthquake Engineering and Structural Dynamics
Prof. Dr. Alessandro Dazio
Ufuk Yazgan
Institute of Construction Engineering and Management
Institute of Geotechnical Engineering
Dr. Jan Laue
Juliane Buchheister
Institute of Geodesy and Photogrammetry

3. Before During After Decision Situations
Optimal allocation of available ressources for risk reduction
- retrofitting
- rebuilding
in regard to possible earthquakes
Damage reduction/Control
Emergency help and rescue
Aftershock hazards
Rehabilitation of infrastructure functionality
Condition assessment and updating of reliability and risks
Optimal allocation of ressources for rebuilding and strengthening

4. Introduction Decision Support Risk assessment Probabilities
Consequences
Structure
Models
Lifeline
Models
CONDITION INDICATORS
Soil
Behaviour
Photo-
grammetry
Socio-econo-
mical Modeling
Structural
Dynamics
Geophysics

5. Uncertainties in the Functional Chain of an Earthquake
characteristics
Festgestein
Sediment-
schichten
Wave
propagation
Freefield
Structural
response
Seismic
Input
Earthquake
Source
Site effects
Site response
Structural response
Damage
Consequences

6. Risk Assessment Framework
Exposure
Vulnerability
Indicators
Risk reduction measures
Robustness

7. Decision Theoretical Framework
The overall theoretical framework is the Bayesian decision theory. Risks will be quantified using Bayesian Probabilistic Networks (BPN).
Causal interrelations of events leading to events of interest are graphically shown in terms of nodes connected by arrows.
A probability structure describing the conditional state probabilities for each node is assigned.
Consequences corresponding to the events in the BPN are assigned.
Variable C B A
Consequ-
ences
Decision

8. Example: Bauwerksklasse
Decision situation, whether to retrofit or not.
columns
30x30 16f16
Jacketed columns
50x50 24f16
stirrups

9. Example: Bayesian Network
Soil Type
Erdbebenstärke
EQ Magnitude
EQ Distance
Spectral
Displacement
Periode
Damage
No. of people
at risk
Structural
Class
Costs
No. of
fatalities
Entscheidung
Actions

10. Example: Modeling of the Seismic Hazard
EQ Magnitude
Gutenberg & Richter (1944) Magnitude recurrence relationship
Actions

11. Example: Modeling of Exposure
Attenuation relations
Mw=5.5
Mw=6.5
Mw=7.0
Mw=7.5
R=10 km
R=20 km
R=40 km
R=80 km … … …
For each spectra 20 time series were simulated.

12. Example: Generation of a Network
Soil Type
EQ Magnitude
EQ Distance
Spectral
Displacement

13. Example: Generation of a Network
Soil Type
EQ Magnitude
EQ Distance
Spectral
Displacement
Periode
Structure
Class

14. Example: FE Calculations
For each time series the maximum interstory drift ratio (MIDR)
is calculated by PreOpenSeesPost.
Details will be given in the presentation of Jens-Peder Ulfkjaer.

15. Example: Fragility Functions
retrofitted Sd Sd
Probability
Probability No No
Slight
Mod.
Total
Heavy
Slight
Mod.
Total
Heavy

16. Example: Generation of a Network
Soil Type
EQ Magnitude
EQ Distance
Spectral
Displacement
Periode
Damage
Structure
Class

17. Example: Modeling of Consequences
Distribution of the No. of people at risk is assumed at the moment by engineering judgement.
No fatalities for the damage classes „No damage“, „slight damage“ und „moderate damage“.
For „heavy damage“ and „totale damage“ a distribution is assumed. 1 2 3 4 5 6 7 8 9 10 11 12
No. of people at risk

18. Example: Generation of a Network
Soil Type
EQ Magnitude
Eq Distance
Spectral
Displacement
Periode
Damage
No. of people
at risk
Structure
Class
Costs
No. of
Fatalities
Actions

19. Example: Updating the Network in the „During“ phase
Soil Type
EQ Magnitude
Eq Distance
Spectral
Displacement
Periode
Photogram.
Measurements
Residual Drift
Ratio
Structure
Class
Damage
No. of people
at risk
Costs
No. of
Fatalities
Actions

20. Bayesian Network for the Soil Input
Type
Earthquake
Magnitude
SPT_CPT
Soil
Subclass
Earthquake
Max. Acc.
Number of
Cycles
Seismic
Demand
Density
Liquefaction
susceptibility
Liquefaction
triggering
Soil
Response
Damage
Groundwater
Level
Hollow
Cylinder

21. Bayesian Network for the Consequences
Structural
Damage
Number of
People
at risk
Earthquake
Time
Consequences
Number of
Fatalities
Probability
of Escape
Number of
Injured People
Age
of People

22. An Example for Extension of the Network
Soil
Type EQ
Magnitude EQ
Distance
SPT_CPT
Soil
Subclass EQ
Max. Acc.
No. of
Cycles
Seismic
Demand
Fundamental
Period
Density
Liquefac.
Suscep.
Liquefac.
Triggering
Soil
Response
Structural
Damage
Structural
class GW
Level
Hollow
Cylinder
No. of
People
at risk EQ
Time
Consequences
Number of
Fatalities
Probability
of Escape
No. of
Injured People
Actions
Age
of People

23. Summary and Conclusions
A systematic approach is suggested by formulating decision problems for three cases in terms of characteristic descriptors (condition indicators), which can be observed and/or measured.
The Bayesian decision theory provides the mathematical framework for the consistent treatment of uncertainties and consequences.
Bayesian Probabilistic Networks are utilized for the consistent consideration of causal dependencies and uncertainties prevailing the identified decision problem.
The modular approach enables the utilisation of different models with varying levels of details.