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© Fraunhofer EMI
Session on Resilience Methods, Tools and Indicators
I. Häring, B. Scharte, S. Hiermaier
Fraunhofer EMI, Germany
Contact: {haering; scharte; hiermaier}@emi.fraunhofer.de
Towards a novel and applicable
approach for Resilience Engineering
International Disaster and Risk Conference Davos 2016
‘Intergrative Risk Management – Towards Resilient Cities‘
28.8.-1.9.2016, Davos, Switzerland
Session on Resilience Methods, Tools and Indicators
Tuesday, 30.8.2016, 17:30-19:00
© Fraunhofer EMI
Overview
 Need for Resilience-driven Risk control from technical science perspective
 Generic working definitions for (technical) Resilience Engineering
 Need for Technical science driven o perationalization of Resilience Engineering
 Joint Resilience Optimization in all resilience management phases
 Resilience analysis and Development process
 Design of Technical resilience capabilities
 Resilience Engineering methods and application examples
 Semi-quantitative/ process-driven
 Resilience dimensional expansions
 Event propagation: forward and backward
 Socio-technical modelling and simulation
 Experimental-empirical
© Fraunhofer EMI
Need for Resilience-driven Risk control from technical
science perspective
 Ever more complex, interlinked, autonomous, smart, connected, etc., systems are
facing an ever increasing list of natural/ anthropogenic, accidental/ manmade,
malicious/ terroristic (un)exampled threats/ events/ disruptions
 Need of technical science to deliver solutions to challenges generated by itself
 Classical (naively applied) system risk control approaches are too limited
 Need to leverage Societal science/ Humanities dominated Resilience discussion in
the technical science domain
 Need for technical science founded approach to resilience
 Need for technical guidance on resilience generation
© Fraunhofer EMI
Generic working definition for (technical) Resilience
Engineering
 Resilience Engineering means preserving critical functionality, ensuring graceful
degradation and enabling fast recovery of complex systems with the help of
engineered generic capabilities as well as customized technological solutions
when the systems witness problems, unexpected disruptions or unexampled
events.
 Resilience Engineering is a technical science approach to societally acceptable
risk and resilience of (socio) technical systems utilizing and further developing
resilience concepts.
Resilience concepts used in Resilience Engineering include, e.g.
 Improved Resilience management in all phases (e.g. preparation, prevention,
detection, protection, response, recovery);
 Identification, assessment and improvement of system functions relevant for
resilience (e.g. critical functions bounce back fast/ better);
 Identification, design and development of generic technical resilience
capabilities (e.g. sense, model, infer, act and adopt in case of disruptions).
© Fraunhofer EMI
Need for Technical science driven operationalization
of Resilience Engineering
 Need for approaches that can be understood by Engineers, MINT scientists
 Competition of design requirements for systems, e.g. economic, ecologic, user
attraction, control by user, scalability, etc.
 Quantification of resilience gain for absolute and relative comparison of systems
 Identification of implicit technical resilience for avoiding double work
 Avoidance to cover with resilience only issues that are not yet covered in
established technical science approaches
© Fraunhofer EMI
Joint Resilience Optimization in all resilience management
phases
1. Preparation
2. Prevention
3. Protection
4. Response, Absorption
9. Recovery and Adaption
© Fraunhofer EMI
1. Context
analysis
2. System
analysis
3. System
function
identification
4. Disruptions
identification
5. Resilience
analysis6. Resilience
quantification
7. Resilience
evaluation
8. Option selection
9. Option design,
development and
implementation
Overall
requirements
Resilience
assessment and
generation process
Process
requirements
Process steps
requirements
Methods,
techniques,
measures,
tools
Resilience analysis and Development process
© Fraunhofer EMI
Technical Resilience Capabilities for resilient systems
System
Technical
reslience
capabilities,
e.g.
Sense
Represent
and model
Simulate
and infer
Act
Adopt
© Fraunhofer EMI
Semi-quantitative/ process-driven approaches
Examples
 Assess and optimize chances of successful resilience management phases
 Jointly assess and minimize risks for each emergency/ crisis management phase
 Use management domains (e.g. physical, cyber, organizational, individual) and resilience
capabilities for assessment and optimization
 Genric: Use resilience dimensions and looping hierarchies as appropriate in application
domain
Context
Chance/
risk
identification
Chance/
risk
computation
Chance/
risk
evaluation
Improvement/
Counter
measures
© Fraunhofer EMI
Resilience dimensional expansions
 Example: Resilience can be achieved through
 sufficient protection only or
 fast response and optimized recovery
 Probabilistic approach taking account of statistical dependencies for systematic
resilience dimensional order expansions
 Capable of considering multiple resilience dimensions
 Suitable e.g. for generation of Probability networks (e.g. Bayesian Belief Networks),
guiding data assessments and big data mining designs
 Example for single resilience assessment dimension:
© Fraunhofer EMI 11
Example for inductive resilience trajectory based
assessment of natural/ anthropogenic threats
2
1
4
3
5
© Fraunhofer EMI 12
Resilience quantification by causal/temporal (dynamic)
trajectory/transition computations
Single initial event
Inductive resilience
quantification
© Fraunhofer EMI 13
Example: Causal/temporal propagation trajectories of
single event using engineering and simulative models
1) Christensen, Hjørt (2012)
Structural damage after
the Oslo bombing (2011)1)
Comparative damage assessment of the
Oslo attack using engineering models
Local pressure-time relation
for hazard propagation
SDOF model to derive initial
structural consequences in
terms of maximum deflections
of ersatz models
1
2
3
5
4
Initial structural damage assessment with critical deflections
EU-Projects VITRUV, EDEN, ENCOUNTER
© Fraunhofer EMI 14
Example: Towards modeling, simulation and resilience
analysis of cascading effects in coupled supply grids
EU-Project Snowball, http://www.snowball.leute.server.de/index.php
© Fraunhofer EMI 15
Modelling and simulation approach: Coupling of engineering simulations of grids (electricity,
water) using software agents that model technical interfaces, operators and user behavior.
Visualized using 3D GIS.
Quantities of interest for resilience quantification include
 (Time-dependent/ Future) Areas/Nodes affected in case of single and multiple disruptions
within single grids and taking grid coupling into account
 Dependency of breakdowns (disruptive events) and success of response and recovery
on interface, operator and user behavior
Example: Towards modeling, simulation and reslience
analysis of cascading effects in coupled supply grids
Grid 1
area
Grid 2
area
© Fraunhofer EMI 16
Examples for quantities accessible from system
modeling and simulation for resilience quantification
Non-
Disruptive
event
© Fraunhofer EMI
Summary
 Need for Resilience-driven Risk control from technical science perspective
 Generic working definition for (technical) Resilience Engineering
 Need for Technical science driven operationalization of Resilience Engineering
 Joint Resilience Optimization in all resilience management phases
 Resilience analysis and Development process
 Design of Technical resilience capabilities
 Resilience Engineering methods and application examples
 Semi-quantitative/ process-driven
 Resilience dimensional expansions
 Engineering event propagation: forward and backward
 Socio-technical modelling and simulation
 Experimental-empirical
© Fraunhofer EMI
Conclusions
 Resilience Engineering has been introduced as a multidisciplinary technical
science dominated emerging research domain
 Resilience Engineering leverages the potential of the technical and engineering
sciences for countering threats and disruptive events to today’s (socio-) technical
systems
 Concepts, Approaches and methods approaches where shown that can be applied
in technical sciences
 to assess the resilience of systems
 to determine functions relevant for resilience performance
 to design resilient system
 Challenges include
 to develop improved concepts suitable for technical operationalization
 to develop novel and improved technical approaches and methods
 to translate science innovation in new application tools, e.g. for business
continuity, supply network operators, smart grid designs, (re)insurance, NGOs
and public agencies supporting emergency and disaster response
 to reach out to and integrate resilience engineering approaches with the risk
science community

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Towards a Novel and Applicable Approach for Resilience Engineering, Ivo HAERING

  • 1. © Fraunhofer EMI Session on Resilience Methods, Tools and Indicators I. Häring, B. Scharte, S. Hiermaier Fraunhofer EMI, Germany Contact: {haering; scharte; hiermaier}@emi.fraunhofer.de Towards a novel and applicable approach for Resilience Engineering International Disaster and Risk Conference Davos 2016 ‘Intergrative Risk Management – Towards Resilient Cities‘ 28.8.-1.9.2016, Davos, Switzerland Session on Resilience Methods, Tools and Indicators Tuesday, 30.8.2016, 17:30-19:00
  • 2. © Fraunhofer EMI Overview  Need for Resilience-driven Risk control from technical science perspective  Generic working definitions for (technical) Resilience Engineering  Need for Technical science driven o perationalization of Resilience Engineering  Joint Resilience Optimization in all resilience management phases  Resilience analysis and Development process  Design of Technical resilience capabilities  Resilience Engineering methods and application examples  Semi-quantitative/ process-driven  Resilience dimensional expansions  Event propagation: forward and backward  Socio-technical modelling and simulation  Experimental-empirical
  • 3. © Fraunhofer EMI Need for Resilience-driven Risk control from technical science perspective  Ever more complex, interlinked, autonomous, smart, connected, etc., systems are facing an ever increasing list of natural/ anthropogenic, accidental/ manmade, malicious/ terroristic (un)exampled threats/ events/ disruptions  Need of technical science to deliver solutions to challenges generated by itself  Classical (naively applied) system risk control approaches are too limited  Need to leverage Societal science/ Humanities dominated Resilience discussion in the technical science domain  Need for technical science founded approach to resilience  Need for technical guidance on resilience generation
  • 4. © Fraunhofer EMI Generic working definition for (technical) Resilience Engineering  Resilience Engineering means preserving critical functionality, ensuring graceful degradation and enabling fast recovery of complex systems with the help of engineered generic capabilities as well as customized technological solutions when the systems witness problems, unexpected disruptions or unexampled events.  Resilience Engineering is a technical science approach to societally acceptable risk and resilience of (socio) technical systems utilizing and further developing resilience concepts. Resilience concepts used in Resilience Engineering include, e.g.  Improved Resilience management in all phases (e.g. preparation, prevention, detection, protection, response, recovery);  Identification, assessment and improvement of system functions relevant for resilience (e.g. critical functions bounce back fast/ better);  Identification, design and development of generic technical resilience capabilities (e.g. sense, model, infer, act and adopt in case of disruptions).
  • 5. © Fraunhofer EMI Need for Technical science driven operationalization of Resilience Engineering  Need for approaches that can be understood by Engineers, MINT scientists  Competition of design requirements for systems, e.g. economic, ecologic, user attraction, control by user, scalability, etc.  Quantification of resilience gain for absolute and relative comparison of systems  Identification of implicit technical resilience for avoiding double work  Avoidance to cover with resilience only issues that are not yet covered in established technical science approaches
  • 6. © Fraunhofer EMI Joint Resilience Optimization in all resilience management phases 1. Preparation 2. Prevention 3. Protection 4. Response, Absorption 9. Recovery and Adaption
  • 7. © Fraunhofer EMI 1. Context analysis 2. System analysis 3. System function identification 4. Disruptions identification 5. Resilience analysis6. Resilience quantification 7. Resilience evaluation 8. Option selection 9. Option design, development and implementation Overall requirements Resilience assessment and generation process Process requirements Process steps requirements Methods, techniques, measures, tools Resilience analysis and Development process
  • 8. © Fraunhofer EMI Technical Resilience Capabilities for resilient systems System Technical reslience capabilities, e.g. Sense Represent and model Simulate and infer Act Adopt
  • 9. © Fraunhofer EMI Semi-quantitative/ process-driven approaches Examples  Assess and optimize chances of successful resilience management phases  Jointly assess and minimize risks for each emergency/ crisis management phase  Use management domains (e.g. physical, cyber, organizational, individual) and resilience capabilities for assessment and optimization  Genric: Use resilience dimensions and looping hierarchies as appropriate in application domain Context Chance/ risk identification Chance/ risk computation Chance/ risk evaluation Improvement/ Counter measures
  • 10. © Fraunhofer EMI Resilience dimensional expansions  Example: Resilience can be achieved through  sufficient protection only or  fast response and optimized recovery  Probabilistic approach taking account of statistical dependencies for systematic resilience dimensional order expansions  Capable of considering multiple resilience dimensions  Suitable e.g. for generation of Probability networks (e.g. Bayesian Belief Networks), guiding data assessments and big data mining designs  Example for single resilience assessment dimension:
  • 11. © Fraunhofer EMI 11 Example for inductive resilience trajectory based assessment of natural/ anthropogenic threats 2 1 4 3 5
  • 12. © Fraunhofer EMI 12 Resilience quantification by causal/temporal (dynamic) trajectory/transition computations Single initial event Inductive resilience quantification
  • 13. © Fraunhofer EMI 13 Example: Causal/temporal propagation trajectories of single event using engineering and simulative models 1) Christensen, Hjørt (2012) Structural damage after the Oslo bombing (2011)1) Comparative damage assessment of the Oslo attack using engineering models Local pressure-time relation for hazard propagation SDOF model to derive initial structural consequences in terms of maximum deflections of ersatz models 1 2 3 5 4 Initial structural damage assessment with critical deflections EU-Projects VITRUV, EDEN, ENCOUNTER
  • 14. © Fraunhofer EMI 14 Example: Towards modeling, simulation and resilience analysis of cascading effects in coupled supply grids EU-Project Snowball, http://www.snowball.leute.server.de/index.php
  • 15. © Fraunhofer EMI 15 Modelling and simulation approach: Coupling of engineering simulations of grids (electricity, water) using software agents that model technical interfaces, operators and user behavior. Visualized using 3D GIS. Quantities of interest for resilience quantification include  (Time-dependent/ Future) Areas/Nodes affected in case of single and multiple disruptions within single grids and taking grid coupling into account  Dependency of breakdowns (disruptive events) and success of response and recovery on interface, operator and user behavior Example: Towards modeling, simulation and reslience analysis of cascading effects in coupled supply grids Grid 1 area Grid 2 area
  • 16. © Fraunhofer EMI 16 Examples for quantities accessible from system modeling and simulation for resilience quantification Non- Disruptive event
  • 17. © Fraunhofer EMI Summary  Need for Resilience-driven Risk control from technical science perspective  Generic working definition for (technical) Resilience Engineering  Need for Technical science driven operationalization of Resilience Engineering  Joint Resilience Optimization in all resilience management phases  Resilience analysis and Development process  Design of Technical resilience capabilities  Resilience Engineering methods and application examples  Semi-quantitative/ process-driven  Resilience dimensional expansions  Engineering event propagation: forward and backward  Socio-technical modelling and simulation  Experimental-empirical
  • 18. © Fraunhofer EMI Conclusions  Resilience Engineering has been introduced as a multidisciplinary technical science dominated emerging research domain  Resilience Engineering leverages the potential of the technical and engineering sciences for countering threats and disruptive events to today’s (socio-) technical systems  Concepts, Approaches and methods approaches where shown that can be applied in technical sciences  to assess the resilience of systems  to determine functions relevant for resilience performance  to design resilient system  Challenges include  to develop improved concepts suitable for technical operationalization  to develop novel and improved technical approaches and methods  to translate science innovation in new application tools, e.g. for business continuity, supply network operators, smart grid designs, (re)insurance, NGOs and public agencies supporting emergency and disaster response  to reach out to and integrate resilience engineering approaches with the risk science community