Assessment and Management of Natural Hazards Michael Faber - PowerPoint PPT Presentation

COST Action C26 Urban Habitat Constructions under Catastrophic Events International Conference 16-18 September 2010, Naples Methodology for Risk Assessment and Management of Natural Hazards Michael Faber Harikrishna Narasimhan Eidgenössische Technische Hochschule Zürich Institute of Structural Engineering Swiss Federal Institute of Technology Zurich Group Risk and Safety

Contents of presentation  Engineering decision making  System representation in risk assessment  Risk indicators and quantification  Decision optimization and life safety  Large scale risk assessment  Conclusion 2

Engineering decision making Hoover Dam, USA Hong Kong Island, China  Development and management of societal infrastructure is a central task for success of society  Decision processes involved in this task are subject to significant uncertainty  Holistic management of risks in such decision processes is an overwhelming challenge 3

Engineering decision making Assessment and control of risks due to:  natural hazards (earthquakes, typhoons, landslides)  malevolence (terrorism)  degradation (corrosion, fatigue)  lack of knowledge (human error, design error) 4

Engineering decision making  Assessment of risk along with evaluation of benefits achieved from decisions can be related to concept of utility in economic decision theory.  This provides a methodical framework and theoretical basis for risk based decision making.  The development and application of such a framework for risk based decision making in the field of engineering is described.  This framework can be used for decision support concerning risk and safety management for multiple hazards at strategic, normative and operational levels. 5

System representation in risk assessment System modelling from an intergenerational perspective System System boundary 6

System representation in risk assessment Logical representation of system interrelationships Exposure events Constituent failure events and direct consequences Follow-up consequences  Constituents of system could be physical components, procedural processes or human activities  Level of detail or scale depends on:  physical characteristics of the considered problem  spatial and temporal characteristics of consequences 7

System representation in risk assessment Generation of consequences Exposures All possible endogenous and exogenous effects with potential to cause consequences  Suddenly occurring events – accidents, explosions, rock-fall  Gradually evolving events – deterioration, climate change  Human errors and malevolence 8

System representation in risk assessment Generation of consequences Consequences Can be broadly classified into:  Loss of life and injuries  Damages to qualities of environment  Economic losses 9

System representation in risk assessment Generation of consequences Consequences  Constituents of system are the first line of defence against exposure events  Direct consequences are associated with damage or failure of individual constituents of the system 10

System representation in risk assessment Generation of consequences Consequences  Indirect consequences are associated with the loss of functionality or failure of the entire system  They result from combination and interaction of events of constituent failures 11

System representation in risk assessment Generation of consequences Consequences  Indirect consequences are differentiated due to:  physical system changes  societal perception of such changes  This differentiation is important in risk communication and management 12

System representation in risk assessment Generation of consequences Vulnerability  The ratio between the risks due to direct consequences and the total value of the considered system  Defined for a specified time frame and by considering all relevant exposures 13

System representation in risk assessment Generation of consequences Robustness  The ratio between the direct risks and the total risks  Defined for a specified time frame and by considering all relevant exposure events and all relevant damage states for the constituents of the system 14

System representation in risk assessment System characterization at different scales 15

Risk indicators and quantification Exposure Exposure Vulnerability Vulnerability Indicators Indicators Direct consequences Direct consequences Robustness Robustness Indirect consequences Indirect consequences Risk indicators  Any observable or measurable characteristic of the system or its constituents that contains information about the risk  For a load bearing structure,  loading of the structure – exposure  strength of components of structure – vulnerability  redundancy, ductility, effectiveness of condition control and maintenance – robustness 16

Risk indicators and quantification Exposure Exposure p EX ( ) k Vulnerability Vulnerability p C EX ( ) c ( C ) ij k D ij p S EX ( ) c ( S c , ( )) C Robustness Robustness l k ID l D Quantification of risk EXP n n CSTA Direct risks R p ( C EX ) c ( C ) ( p EX ) D l k D l k k 1 l 1 n EXP n n CSTA SSTA Indirect risks R c ( S , c ( C )) p S ( C , EX ) ( p C EX ) ( p EX ) ID ID m D l m l k l k k k 1 l 1 m 1 R Index of robustness D I R R R ID D 17

Risk indicators and quantification Exposure Exposure Vulnerability Vulnerability Indicators Indicators Direct consequences Direct consequences Robustness Robustness Indirect consequences Indirect consequences Updating of risks  Bayesian updating  Spatial  Temporal P e C ( ) ( P C ) ij ij P C e ( ) ij P e C ( ) ( P C ) P e C ( )(1 P C ( )) ij ij ij ij 18

Risk indicators and quantification Indicators of risk at different levels for a scenario : Scen cenario ario rep eprese esentat ntation on Phys hysica cal Ind ndicator cators Poten ential al cha characte acterist stics cs conseq con seque uence nces Exposure Expo Exposure Expo xposure xposure sure sure Flood Ship impact Use/functionality Explosion/Fire Location Earthquake Environment Vehicle impact Design life Wind loads Societal importance Traffic loads Deicing salt Water Carbon dioxide Vulne Vulnerability Vulne Vulnerability nerab nerab ability ability Yielding Design codes Direct ect con conseq seque uence nces Rupture Design target reliability Repair costs Cracking Age Temporary loss or reduced Fatigue Materials functionality Wear Quality of workmanship Small number of injuries/fatalities Spalling Condition Minor socio-economic losses Erosion Protective measures Minor damages to environment Corrosion Robustness Robustness Rob Rob obustnes obustnes ustness ustness Loss of functionality Ind ndirect ect con conseq seque uence nces Ductility partial collapse Repair costs Joint characteristics Temporary loss or reduced full collapse Redundancy functionality Segmentation Condition Mid to large number of control/monitoring injuries/fatalities Emergency preparedness Moderate to major socio- economic losses Moderate to major damages to environment 19

Decision optimization and life safety Utility Optimal decision Decision alternative Acceptable decisions Feasible decisions Feasible, acceptable and optimal decisions  All decision alternatives are ranked based on their expected value of utility or benefit  This should include the expected value of all discounted costs and incomes resulting from the different decision alternatives 20

Decision optimization and life safety Risk acceptance and life quality index  It is useful to differentiate between tangible risks and intangible risks – i.e. risks which can be easily expressed in monetary terms and which cannot be  The Life Quality Index (LQI) is a measure that facilitates the development of risk acceptance criteria for intangible risks  It is a social indicator derived to reflect the expected length of “good” life and particularly the enhancement of the quality of life by good health and wealth 21

Decision optimization and life safety Risk acceptance and life quality index 1 r r L g (1 w ) The LQI models the preferences of society with a relationship between:  Gross Domestic Product (GDP) per capita g  Life expectancy at birth l  Proportion of life spent for earning w  Trade-off between resources available for consumption and value of time of healthy life r 22

Decision optimization and life safety Risk acceptance and life quality index  Every investment into life safety should lead to an increase in life-expectancy: g (1 r ) 0 g r  This leads to the important Societal Willingness To Pay (SWTP) criterion: g (1 r ) SWTP g r 23

Decision optimization and life safety Sustainable discounting  In evaluating the benefit and risk of decision alternatives, the time of consequences and investments need to be taken into account – by discounting  Discounting should be based on long term average values, free of taxes and inflation  Private sector discounting – long term investment return  Public sector discounting – long term rate of economic growth 24