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SPECIAL MOBILITY STRAND On Natural Hazards Risk Management Michael Havbro Faber Banja Luka, Bosnia and Herzegovina , December 13, 2018 Michael Havbro Faber, Department of Civil Engineering, Aalborg University, Denmark The European Commission

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Michael Havbro Faber, Department of Civil Engineering, Aalborg University, Denmark

SPECIAL MOBILITY STRAND

On Natural Hazards Risk Management Michael Havbro Faber Banja Luka, Bosnia and Herzegovina , December 13, 2018

The European Commission support for the production of this publication does not constitute an endorsement of the contents which reflects the views only of the authors, and the Commission cannot be held responsible for any use which may be made of the information contained therein.

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2/70 M. H. Faber, K-FORCE December 13, 2018

On Natural Hazards Risk Management

Michael Havbro Faber Department of Civil Engineering Aalborg University, Denmark K-FORCE Lectures Banja Luka Bosnia and Herzegovina December 13, 2018

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3/70 M. H. Faber, K-FORCE December 13, 2018

Introduction – My Group at Aalborg University

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4/70 M. H. Faber, K-FORCE December 13, 2018

Introduction – Members of my Team

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5/70 M. H. Faber, K-FORCE December 13, 2018

Introduction – Collaboration Partners

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6/70 M. H. Faber, K-FORCE December 13, 2018

The Challenges of Risk Management

Interrelations of sectors and activities in society Infrastructures as part of the built environment play a crusial role for the existence and development of society

Natural resources Development and maintenance of Infrastructure Economy Production Human capital Life safety/health Environment

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7/70 M. H. Faber, K-FORCE December 13, 2018

Pressing boundaries for societal developments: At local and global scales it is increasingly appreciated that societal developments are approaching the limits of the capacities of the ecological systems and the Earth life support system

The Challenges of Risk Management

Planetary boundaries, Steffen et al. 2015[1] Population growth, Wikepedia, UN

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8/70 M. H. Faber, K-FORCE December 13, 2018

Pressing boundaries for societal developments: Significant signs of the back-coupling between civilizations and living conditions for civilization are observable

The Challenges of Risk Management

IPCC homepage CO2 emissions constant at 2000 level Scenario A2 – heterogeneous world Scenario B1 – convergent world

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9/70 M. H. Faber, K-FORCE December 13, 2018

Pressing boundaries for societal developments: Significant signs of the back coupling between civilizations and living conditions for civilization are observable

The Challenges of Risk Management

Wikepedia Anthropocene

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10/70 M. H. Faber, K-FORCE December 13, 2018

The Challenges of Risk Management

Source: EM-DAT - The OFDA/CRED International Disaster Database.

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11/70 M. H. Faber, K-FORCE December 13, 2018

The Challenges of Risk Management

Source: EM-DAT - The OFDA/CRED International Disaster Database.

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12/70 M. H. Faber, K-FORCE December 13, 2018

The Challenges of Risk Management

Source: EM-DAT - The OFDA/CRED International Disaster Database.

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13/70 M. H. Faber, K-FORCE December 13, 2018

The Challenges of Risk Management

Source: EM-DAT - The OFDA/CRED International Disaster Database.

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14/70 M. H. Faber, K-FORCE December 13, 2018

The Challenges of Risk Management

Source: EM-DAT - The OFDA/CRED International Disaster Database.

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15/70 M. H. Faber, K-FORCE December 13, 2018

The Challenges of Risk Management

Source: EM-DAT - The OFDA/CRED International Disaster Database.

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16/70 M. H. Faber, K-FORCE December 13, 2018

The Challenges of Risk Management

Source: EM-DAT - The OFDA/CRED International Disaster Database.

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17/70 M. H. Faber, K-FORCE December 13, 2018

The Challenges of Risk Management

Source: EM-DAT - The OFDA/CRED International Disaster Database.

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18/70 M. H. Faber, K-FORCE December 13, 2018

The Challenges of Risk Management

Infrastructures accommodating 7.5 billion people Cities in the world (+1 million inhabitants) ~ 500 Bridges in the USA ~ 600.000 Global road network > 13 million km Global rail network > 1 million km Airports ~ 50.000 Offshore platforms in the world ~ 6.500 Dams in the world ~ 45.000 Nuclear (civil) reactors in the world ~ 440 …….. ……..

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19/70 M. H. Faber, K-FORCE December 13, 2018

The Challenges of Risk Management

Built environment alone Contributes with ~10% of GDP in Europe Responsible for 50% of global energy consumption Concrete responsible for ~8% of global CO2 emissions Responsible for ~90% of global material consumption (weight)

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20/70 M. H. Faber, K-FORCE December 13, 2018

The Challenges of Risk Management

Climate change/sustainability McKinsey and Co Ltd

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21/70 M. H. Faber, K-FORCE December 13, 2018

The Challenges of Risk Management

Questions to be answered in natural hazards risk management How to:

  • prioritize investments on design and management of

interlinked systems (economy, environment, health)?

  • plan and budget for the future (economy, qualities of the

environment, social capacity, health)? How to assess vulnerability, risks, robustness, resilience and sustainability consistently, which are the criteria to apply for decision making? How safe is safe enough robust is robust enough resilient is resilient enough sustainable is sustainable enough

?

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22/70 M. H. Faber, K-FORCE December 13, 2018

Resilience/sustainability – definitions and insights Decision Support Framework Probabilistic systems representation

  • Vulnerability and risks of systems
  • Robustness of systems
  • Resilience of systems
  • Consequences to health and environment
  • Sustainability of systems

Examples Conclusions and outlook

Contents of Presentation

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23/70 M. H. Faber, K-FORCE December 13, 2018

Resilience/sustainability – Definitions and Insights

Resilience (definitions): Pimm (1984) - Resilience….the time it takes till a system which has been subjected to a disturbance returns to its original mode and level of functionality Holling (1996) - Resilience.…the measure of disturbance which can be sustained by a system before it shifts from one equilibrium to another Cutter (2010) - Resilience…. capacity of a community to recover from disturbances by their own means Bruneau (2009) – Resilience…. a quality inherent in the infrastructure and built environment; by means of redundancy, robustness, resourcefulness and rapidity National Academy of Science (NAS, USA) - Resilience….a systems ability to plan for, recover from and adapt to adverse events over time

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24/70 M. H. Faber, K-FORCE December 13, 2018

Resilience/sustainability – Definitions and Insights

Sustainability: Gro Harlin Bruntland report (1987) – Our Common Future “Humanity has the ability to make development sustainable to ensure that it meets the needs of the present without compromising the ability of future generations to meet their own needs”

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25/70 M. H. Faber, K-FORCE December 13, 2018

Resilience/sustainability – Definitions and Insights

Sustainability (environment): Kates et al.(2001) recommends to explore and assess the relation between resilience and sustainability and propose to utilize decision support systems as a means to identify sustainable paths of societal developments Steffen et al. (2015) introduce the concept of Planetary Boundaries as a concept for representing the capacities of the Earth System (Earth Life Support System - ELSS) Hauschild (2015) suggests to utilize quantitative sustainability assessments to assess the aggregate impacts of human activities at global level with respect to the main parameters controlling safe

  • perating conditions (ELSS) for the planetary system.
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26/70 M. H. Faber, K-FORCE December 13, 2018

Resilience/sustainability – Definitions and Insights

Strategies for sustainable and resilient systems

  • Efficiency/optimality
  • Diversity
  • Redundancy
  • Robustness
  • Temporally optimized solutions
  • Planned and smart renewals
  • Options for buying information and changing strategies
  • Additional data collection, monitoring and control
  • Optimal balance between efficiency and resilience
  • Joint consideration of efficiency/sustainability, resilience, safety,

economy and welfare

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27/70 M. H. Faber, K-FORCE December 13, 2018

Decision Support Framework

Hierarchies of societal management

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28/70 M. H. Faber, K-FORCE December 13, 2018

The general framework (traditional)

Decision Support Framework

Exposureevents Direct consequences Indirect consequences Constituentdamagestates System damagestates

Exposure Condition Functionality

Economy Health Environment Economy Health Environment Hazards/threats

Vulnerability Robustness Resilience

Economy Health Environment

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29/70 M. H. Faber, K-FORCE December 13, 2018

The general framework (new direction)

Decision Support Framework

Exposureevents Direct consequences Indirect consequences System Constituentdamagestates System damagestates

Exposure Condition Functionality

Economy Health Environment Economy Health Environment Economy Health Environment Hazards/threats Economy Health Environment

Vulnerability Robustness Resilience

Utility

P Feasibledecisions Acceptable decisions

Expected value of utility

The general framework (enhanced)

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30/70 M. H. Faber, K-FORCE December 13, 2018

Probabilistic System Representation

Interlinked systems

Infrastructure system Ecological/earth lifesupport system Social system Geo hazard system Anthropological hazardsystem Monitoringand control system Regulatory system

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31/70 M. H. Faber, K-FORCE December 13, 2018

Risk aggregation - portfolio risk modeling

Common model uncertainties Common hazard events Aggregated consequences

Generic risk models

Objects and segments

Probabilistic System Representation

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32/70 M. H. Faber, K-FORCE December 13, 2018

Hazards and disturbances Type 1: “Large scale averaging events”

  • low probability/high consequences

Type 2: “Seepage events”

  • high probability/low consequences

Type 3: “Non-averaging events”

  • low probability/extreme consequences

Type 4: ”Information condition”

  • as for Type 1-3

Exposure events Direct consequences Follow-up consequences Constituent damage states System damage states

Exposure Condition Functionality

Hazards

Vulnerability Robustness

Probabilistic System Representation

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33/70 M. H. Faber, K-FORCE December 13, 2018

Information condition

Probabilistic System Representation

Objectives

  • preferences
  • constraints

Values

  • social
  • political

Stakeholders Decision maker Perceptions Decision analysis

  • knowledge
  • models
  • options

Risk specialists Outcomes

  • ranking
  • implications

State of nature System

  • states
  • consequences

Decisions

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34/70 M. H. Faber, K-FORCE December 13, 2018

Information condition 1. The information is relevant and precise. 2. The information is relevant but imprecise. 3. The information is irrelevant. 4. The information is relevant but incorrect. 5. The flow of information is disrupted or delayed.

Probabilistic System Representation

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35/70 M. H. Faber, K-FORCE December 13, 2018

Direct and indirect consequences

Hazards/threaths Constituent damage states System damage states

Phase 1 Disturbanceeffects Phase 2 Redistribution effects

Damages and failure caused directly by disturbances Damages andfailures during internal redistribution Direct consequences are associatedwith damages and failures of the constituents in phase 1 - marginally Indirect consequences are associated with loss of functionality of the systemcausedby damages and failures in phase 1 and phase 2

Probabilistic System Representation

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36/70 M. H. Faber, K-FORCE December 13, 2018

Vulnerability and risk modelling

Exposure events Direct consequences Follow-upconsequences Constituent damage states System damage states

Exposure Condition Functionality

Hazards

Vulnerability Robustness , ,

( , ( ), ( ), ( ), ( )))

D I D P ID

i p i c i c i c i  S

It is assumed that all relevant scenarios have been identified

1,2,..,

s

i n 

, ,

( ) ( ) ( )

D I D P VS R

c i c i I i c  

Probabilistic System Representation

1

1 ( )

s

n VT VS i R

I I i c

: total replacement costs

R

c

, , 1

( ) ( ) ( )

S

n D I D P ID i

R c i c i c i

  

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37/70 M. H. Faber, K-FORCE December 13, 2018

Robustness modeling

Exposure events Direct consequences Follow-upconsequences Constituent damage states System damage states

Exposure Condition Functionality

Hazards

Vulnerability Robustness , ,

( , ( ), ( ), ( ), ( )))

D I D P ID

i p i c i c i c i  S

It is assumed that all relevant scenarios have been identified

1,2,..,

s

i n 

( ) ( ) ( )

D R T

c i I i c i 

, , ,

( ) ( ) ( ) ( )

D I R D I D P

c i I i c i c i  

, , , ,

( ) ( ) ( ) ( ) ( ) ( )

D I D P R D I D P ID

c i c i I i c i c i c i    

Probabilistic System Representation

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38/70 M. H. Faber, K-FORCE December 13, 2018

Probabilistic resilience modeling

Service provision Time Time of disturbance event Time to recover Total service loss Capacity

Probabilistic System Representation

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39/70 M. H. Faber, K-FORCE December 13, 2018

Probabilistic resilience modeling

Service provision Time Time of disturbance event Time to recover Total service loss Capacity

Robustness Probabilistic System Representation

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40/70 M. H. Faber, K-FORCE December 13, 2018

Probabilistic resilience modeling

Service provision Time Time of disturbance event Time to recover Total service loss Capacity

Robustness Preparedness, adaptive capasity

Faber M. Risk Informed Structural Systems Integrity Management: A Decision Analytical

  • Perspective. ASME. International Conference on

Offshore Mechanics and Arctic Engineering, Volume 9: Offshore Geotechnics; Torgeir Moan Honoring Symposium ():V009T12A040. doi:10.1115/OMAE2017-62715.

Probabilistic System Representation

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41/70 M. H. Faber, K-FORCE December 13, 2018

Resilience modeling

Faber M.H., Qin J., Miraglia S. and Thöns S. (2017). On the Probabilistic Characterization of Robustness and Resilience”, Procedia Engineering 198 ( 2017 ) 1070 – 1083.

 

 

 

( ( ) ( ) 0, ( ) ( ) ) ( ) lim

f t

P R S t R t t S t t f t t   

 

         

  • Probabilistic System Representation

Benefit Time Realization of benefit generation Capacity accumulated Realization of capacity accumulated Event of resilience failure Disturbance events

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42/70 M. H. Faber, K-FORCE December 13, 2018

Consequences to health, environment and economy Impacts to health and safety are addressed through the relative utility function comprised by the Life Quality Index (LQI) (Nathwani et al, 1997) Impacts to the environment are addressed through:

  • Quantitative Life Cycle Analysis (substances/energy)

(Hauschild, 2015) Impacts to the economy are addressed through:

  • Monetary benefits (production functions)
  • Monetary losses (production functions)

Probabilistic System Representation

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43/70 M. H. Faber, K-FORCE December 13, 2018

Sustainability modeling Global Planetary Boundaries provide a means for allocating capacities to different societal activities

Global capacities Local /national and sector wise allocation of capacities

  • Built environment
  • Energy production and distribution
  • Food production
  • Transportation
  • .....
  • ....
  • ...
  • ..

Probabilistic System Representation

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44/70 M. H. Faber, K-FORCE December 13, 2018

Probabilistic System Representation

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Sustainability modeling

For given sector, geographical area or project sustainability

failure is expressed in terms of exceedance of Planetary Boundaries

Ultimate capacity Loading process Time Loading, capacity (Planetary Boundaries)

 

 

 

( ( ) ( ) 0, ( ) ( ) ) ( ) lim

f t

P R S t R t t S t t f t t   

 

         

  • Probabilistic System Representation
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Overall framework

Probabilistic System Representation

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47/70 M. H. Faber, K-FORCE December 13, 2018

Example Illustrations

Application of modeling concept

Exposure Vulnerability Indirect consequences Direct consequences Robustness Exposure Vulnerability Indirect consequences Direct consequences Robustness

Earthquake risk management

  • Rock-Fall
  • Typhoons
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Exposure Modeling

Exposure analysis in regard to rock-fall

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49/70 M. H. Faber, K-FORCE December 13, 2018

Exposure Modeling

Exposure analysis in regard to rock-fall

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Exposure Modeling

Exposure analysis in regard to rock-fall

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Exposure Modeling

Exposure analysis in regard to rock-fall

Detachment modeling Fall modeling

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52/70 M. H. Faber, K-FORCE December 13, 2018

Typhoon Exposure Modeling

Representing the Event of Typhoons

Knowledge modeling updating conditioning Transition model Occurrence model Wind field model Surface friction model Vulnerability model Typhoon model Data Information

10

O

20

O

30

O

40

O

50

O

120

O

130

O

140

O

150

O

160

O

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53/70 M. H. Faber, K-FORCE December 13, 2018

Typhoon Exposure Modeling

Representing the Event of Typhoons

Translation speed Latitude Longitude Translation direction Cental pressure Current time step Translation speed Translation direction Cental pressure : incremental change in 6 hours Transition model Cental pressure Distance from center Wind speed Wind field model

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54/70 M. H. Faber, K-FORCE December 13, 2018

Typhoon Exposure Modeling

Representing the Event of Typhoons

10

O

20

O

30

O

40

O

50

O

120

O

130

O

140

O

150

O

160

O

1 0.5 Wind speed (km/h) 1 0.5 1 0.5 Tokyo Time 1 Time 2 Time 3 Probability Probability Probability

  • 2

2

  • 4

4

  • 6

6

  • 8

8

  • 1

1

  • 1

2 1 2

  • 1

4 1 4

  • 1

6 1 6

  • 1

8 1 8

  • Time 1

Time 2 Time 3

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Management of Risks due to Earthquakes

Large scale earthquake risk management

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Management of Risks due to Earthquakes

Risk assessment for large portfolios

Risk Management Risk Management

On-site observations On-site observations Official/insurance data Official/insurance data Airplane observations Airplane observations Satellite Observations Satellite Observations

Real World

Exposure Exposure Vulnerability Vulnerability Robustness Robustness Indicators Indicators Exposure Exposure Vulnerability Vulnerability Robustness Robustness Indicators Indicators

Models of real world

Risk reduction measures Risk reduction measures Risk reduction measures Risk reduction measures

Actions GIS Interface Platform GIS Interface Platform

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57/70 M. H. Faber, K-FORCE December 13, 2018

Management of Risks due to Earthquakes

Large scale earthquake risk management

Risk Management Risk Management

On-site observations On-site observations Official/insurance data Official/insurance data Airplane observations Airplane observations Satellite Observations Satellite Observations

Real World

Exposure Exposure Vulnerability Vulnerability Robustness Robustness Indicators Indicators Exposure Exposure Vulnerability Vulnerability Robustness Robustness Indicators Indicators

Models of real world

Risk reduction measures Risk reduction measures Risk reduction measures Risk reduction measures

Actions GIS Interface Platform GIS Interface Platform

Damage Period Soil profile Clay content Liquid limit Soil response Liquef. suscept. SD PGA EQ R EQ M Liquef. triggering Story area Struct. class No of fatalities No of people at risk Costs Indicators related to vulnerability Indicators related to robustness Indicators related to exposure Model uncertainty
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Recent Developments in Systems Modeling

Large scale earthquake risk management

Damage Period Soil profile Fines content Liquid limit Soil response Liquefact. trigger. Liquefact. suscept. SD PGA Soil Type EQ R EQ M
  • No. people
at risk Direct Costs Actions Story area Structure class Eps_SD Eps_PGA Indirect Costs

Recurrence Attenuation Mmax Zonation Source

Occupancy class Age of people Business interrupt EQ Time
  • No. of
fatalities
  • Prob. Of
escape
  • No. of
injuries

Image sharpness Image scale Accuracy plane Accuracy height Resolution Pixel size Extraction mode

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Recent Developments in Systems Modeling

Large scale earthquake risk management

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60/70 M. H. Faber, K-FORCE December 13, 2018

Recent Developments in Systems Modeling

Large scale earthquake risk management

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61/70 M. H. Faber, K-FORCE December 13, 2018

Recent Developments in Systems Modeling

Large scale earthquake risk management

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Recent Developments in Systems Modeling

Exposure Vulnerability Robustness

Ductility capacity Irregularity Damage No of fatalities
  • Prob. of
escape EQ Time Business interruption
  • No. of
injuries Age of People Period No of stories Soil subclass Density Damping Structural system SPT CPT Ductility demand Soil response Liquefact. triggering Liquefact. suscept. Planimetry measure Planimetry measure Planimetry measure Seismic demand EQ duration PGA Soil Type Fault type Aerial photos Flight height Rupture length Average slip Hangingwall Resolution Terrestrial photos EQ distance EQ magnitude Directivity Design code Max. displ. Lab test HCA GW level Base shear capacity Costruction quality Structure class Residual displ. No of people at risk Occupancy class Costs Actions Altimetry measure

Large scale earthquake risk management Liquifaction

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63/70 M. H. Faber, K-FORCE December 13, 2018

Condition indicators for liquefaction susceptibility

  • f silty and sandy soils

Recent Developments in Systems Modeling

Large scale earthquake risk management

Damage Soil Profile Fines Content SPT CPT Ductility demand Soil response Liquefact. triggering Liquefact. suscept. Seismic demand PGA Soil Type Lab test HCA Liquid Limit

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Recent Developments in Systems Modeling

Large scale earthquake risk management Vulnerability in regard to liquifaction Locations of buildings and soil measurements

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Recent Developments in Systems Modeling

Large scale earthquake risk management Vulnerability in regard to liquifaction

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Recent Developments in Systems Modeling

Large scale earthquake risk management

Mean and coefficient of variation of conditional Standard Penetration Test (SPT) blowcounts (N1)60 simulations (N1)60 is the SPT blow count normalized to an overburden pressure of approximately 100 kPa and a hammer energy ratio of 60%.

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Recent Developments in Systems Modeling

Large scale earthquake risk management Probability of liquefaction at the study site, given a M=7.5 earthquake causing a PGA of 0.3g

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68/70 M. H. Faber, K-FORCE December 13, 2018

Recent Developments in Systems Modeling

Large scale earthquake risk management Distribution of damage for a M=7.5 earthquake

Damage State

Fully Operational Life Safety Near Collapse Collapse Operational

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69/70 M. H. Faber, K-FORCE December 13, 2018

Recent Developments in Systems Modeling

Large scale earthquake risk management Total risks for a M=7.5 earthquake

0 – 200’000 200’000 – 400’000 400’000 – 600’000 600’000 – 800’000

Total Risk [$]

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70/70 M. H. Faber, K-FORCE December 13, 2018

Management of Risks due to Earthquakes

Risk assessment for large portfolios

EQ_M Model uncertainty Soil profile Story area Struct. class Story area Struct. class Soil profile

Costs portfolio

EQ_M Model uncertainty

Building2

Soil profile Story area Struct. class Story area Struct. class Soil profile Costs EQ_M Model uncertainty

Building264

Soil profile Story area Struct. class Story area Struct. class Soil profile Costs EQ_M Model uncertainty

Building1

Costs

…

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71/70 M. H. Faber, K-FORCE December 13, 2018

Management of Risks due to Earthquakes

Risk assessment for large portfolios

E[Costs]=25 Mio USD E[Costs]=25 Mio USD 10 40 … 700

Portfolio Loss [in Mio USD]

10 40 … 700

Portfolio Loss [in Mio USD]

Without dependency With dependency

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72/70 M. H. Faber, K-FORCE December 13, 2018

Management of Risks due to Earthquakes

Risk assessment for large portfolios

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73/70 M. H. Faber, K-FORCE December 13, 2018

Concluding Remarks

  • Modern risk assessment frameworks and tools greatly

enhance risk management

  • Utilize generic risk modeling
  • Facilitate updating of risks through indicators
  • Can be applied for individually and jointly acting

hazards

  • Can be coupled with any (set) of models available

which link exposure events to effects of climatic change

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74/70 M. H. Faber, K-FORCE December 13, 2018

Concluding Remarks

  • We still need to improve modelling and best practices

in risk management of natural hazards to establish the right focus on how to:

  • reduce risks
  • increase resilience
  • achieve sustainability
  • Efforts must be directed on standardization of:
  • modeling approaches
  • assessment criteria
  • Industry 4.0 must be utilized to facilitate:
  • open platforms for sharing models/data/tools
  • real-time observations/monitoring/advise
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75/70 M. H. Faber, K-FORCE December 13, 2018

K-FORCE Lectures Banja Luka, Bosnia Herzegovina December 13, 2018

Thanks for your attention    

mfn@civil.aau.dk www.r3sbe.civil.aau.dk