# Management of Earthquake Risks using Condition Indicators

## Presentation on theme: "Management of Earthquake Risks using Condition Indicators"— Presentation transcript:

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

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

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

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

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

Risk Assessment Framework
Exposure Vulnerability Indicators Risk reduction measures Robustness

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

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

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

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

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.

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

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

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.

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

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

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

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

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

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

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

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

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.

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