Pistone_VCE_IDRC14

EQvis: A risk management platform for a
5th International Disaster and Risk Conference IDRC 2014
‘Integrative Risk Management - The role of science, technology & practice‘ • 24-28 August 2014 • Davos • Switzerland
www.grforum.org
resilient society
E. Pistone, D. Schäfer, M. Pietsch, H. Wenzel, A. Bosi.
VCE – Vienna Consulting Engineers ZT GmbH, Vienna, Austria.

×
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Risk Framework
Catastrophic events showed that harm depends not only on
hazard, vulnerability and exposure, but also on resilience.
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RISK
QUANTIFICATION
×
Regional
Cluster
Individual
Specific
HAZARD
Regional
Cluster
Individual
Specific
RISK
Regional
Cluster
Individual
Specific
VULNERABILITY
Regional
Cluster
Individual
Specific
EXPOSURE
Regional
Cluster
Individual
Specific
RESILIENCE
Resilience: the capacity of a system, community or society
to withstand loss or damage or to recover from the impact
of an emergency or disaster. The higher the resilience, the
less likely damage may be, and the faster and more
effective recovery is likely to be (Dept. Human Services 2000).

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Resilience and Earthquakes
• Emergency preparedness, response and recovery are concepts
introduced already in the 1970s.
• Monitoring and the use of disparate data from widespread
sensors have changed these pre-conditions dramatically.
• Decision making and scenario elaboration becomes feasible.

Relations of Basic Constituents of Resilience towards
Loss
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Functionality
time
functionality
absorption consequences
adaptation
alternative
recovery curve
restoration
Resilience
index
Linear
Exponential
Trigonometric
In a system, resilience is a function of:
• Absorption: the capacity to dissipate negative effects without
consequences in a pre-event phase.
• Adaptation is the short-term post-event response.
• Restoration is the long-term post-event part of the recovery
process.

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Innovations in Resilience
It is not always possible to re-establish
the original functionality curve due to
economic or social or other reason.
In other case, an improvement of the
original functionality curve can be
pursued.
Mitigation actions can increase
resilience.
EQvis can increase
resilience.

EQvis: Consequence-based Risk Management for
Data  Analysis  Visualization
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Seismic Events
Input Motion Parameter
Social/Economic Impact Limit State
Input error margin
Response error margin
Decision
Support
Damage
Prediction
Fragility
Models
Inventory
Selection
Hazard
Definition
It is based on the open-source-platform MAEviz,
developed by the Mid-America Earthquake Center and
the National Center for Supercomputing Applications.

It is an advanced seismic loss assessment and risk management software
relying on the Consequence-based Risk Management methodology.
It enables policy-makers and decision-makers to develop risk reduction
strategies and implement mitigation actions.
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Overview of the Platform EQvis
Scenario View
Data Catalogue
Main Window
Visually-based
Menu-driven
Generate damage-estimates
Test multiple mitigation strategies
Support modelling efforts
Valued as the best software for
scenario risk assessment (The
world bank, 2014).

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Modularity in EQvis
Modularity is the key for this open platform.
Plug-in based: it can be improved and quickly expanded.

Demonstration in Tolna County, Hungary
Real time earthquake simulation.
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Message of the earthquake,
Data about location,
magnitude and depth.
Creating the Earthquake
Scenario

• Collect information in real-time to update the scenario.
• Coordinate the teams of the Disaster Management Team.
• Risk estimation.
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Scenario Computed
Scenarios finished and
damages computed
Expansion of a toxic cloud near
the chemical plant in Pincehely

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Inpection Teams
Scenarios finished, damages
computed, reports printed
Sending out the Bridge Inspection Team
Sending out the Building Inspection
Team

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Scenario updated with real-time data
Computation is updated with the
information from the Inspection
Teams.
The Bridge Inspection Team ends
measurements at bridge 1 and 2.

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Conclusions
• The study of earthquake resistance was enabled.
• Effective emergency management.
• Integration of GIS-driven map was highly beneficial.
• It can help the work of rescue workers.
Further work includes the inclusion:
• Landslides and other natual hazards.
• Psychological resilience of communities.
• Political decision impact.

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Thank you
for your attention!

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15
RISK MANAGEMENT FRAMEWORK:
Risk Quantification
March 25, 2014 - Technische Universität, Wien
Hazard data
Fragility Manager
Exposure
Risk Quantification
http://gratiszeitung.org/2010/04/19/landkarte-osterreich-map-austria/
http://commons.wikimedia.org/wiki/File:Austria_relief_location_map.jpg

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Risk framework
= ×
RISK
QUANTIFICATION
×
Regional
Cluster
Individual
Specific
HAZARD
Regional
Cluster
Individual
Specific
RISK
Regional
Cluster
Individual
Specific
VULNERABILITY
Regional
Cluster
Individual
Specific
EXPOSURE

Relations of basic constituents of resilience towards
Loss
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functionality
time
functionality
consequences
absorption
adaptation
alternative
recovery curve
restoration
Resilience
index

Figure 1-2: Relations of basic constituents of resilience towards functionality
Figure 1-2: Relations of basic constituents of resilience towards functionality
Disasters affect all sectors, disciplines and system performances. Therefore a strong trans
disciplinary approach is taken reflected in the selection of the consortium. It comprises a large
coverage in sectors, regions and practices. This will result in collaboration in disciplines as shown
in the figure below.
Disasters affect all sectors, disciplines and system performances. Therefore a strong trans
disciplinary approach is taken reflected in the selection of the consortium. It comprises a large
coverage in sectors, regions and practices. This will result in collaboration in disciplines as shown
in the figure below.
Let us look at the functionality of a system in time. We assume that functionality can be described
by a performance function 퐹( 푡) which when normalized can start with unit value or 퐹 . Over
time the performance of the system can slightly deteriorate depending on outer factors and
maintenance to its value 퐹 at the time 푡 of event 푁. In case of a disastrous event there is a
sudden drop in performance. The extent of this setback, that is to say the consequences 퐶, depends
on the on the strength of the source (or hazard 퐻) and the vulnerability 푉 of the system to the source
퐶 = 푓( 퐻, 푉) . At this point we can say that there is an inverse relation between consequences and
absorption 퐴푏 such as 퐶 = 1⁄퐴푏. After the event recovery 푅푐 happens based on the characteristics
of adaptation 퐴푑 and restoration 푅푒 so that 푅푐 = 푓( 퐴푑, 푅푒) . During the time of recovery until the
system reaches its pre-event functionality (or higher), losses are generated. The sum of these losses
over the recovery time yields the total loss defined as
Let us look at the functionality of a system in time. We assume that functionality can be described
by a performance function 퐹( 푡) which when normalized can start with unit value or 퐹 . Over
time the performance of the system can slightly deteriorate depending on outer factors and
maintenance to its value 퐹 at the time 푡 of event 푁. In case of a disastrous event there is a
sudden drop in performance. The extent of this setback, that is to say the consequences 퐶, depends
on the on the strength of the source (or hazard 퐻) and the vulnerability 푉 of the system to the source
퐶 = 푓( 퐻, 푉) . At this point we can say that there is an inverse relation between consequences and
absorption 퐴푏 such as 퐶 = 1⁄퐴푏. After the event recovery 푅푐 happens based on the characteristics
of adaptation 퐴푑 and restoration 푅푒 so that 푅푐 = 푓( 퐴푑, 푅푒) . During the time of recovery until the
system reaches its pre-event functionality (or higher), losses are generated. The sum of these losses
over the recovery time yields the total loss defined as
퐿표푠푠 = 1 −
퐹( 퐶, 푅푐, 푡)
퐿표푠푠 = 1 −
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Resilience
• Let us look at the functionality of a system in time. We assume that functionality can be
described by a performance function F(t) which when normalized can start with unit
value or F max. Over time the performance of the system can slightly deteriorate
depending on outer factors and maintenance to its value F NE at the time Tne of event
N. In case of a disastrous event there is a sudden drop in performance. The extent of
this setback, that is to say the consequences , depends on the on the strength of the
source (or hazard ) and the vulnerability of the system to the source or hazard, the
vulnerability so C=f(H,V).
• At this point we can say that there is an inverse relation between consequences and
absorption Ab such as C=1 ⁄Ab . After the event recovery Rc happens based on the
characteristics of adaptation Ad and restoration Re so that Rc=f(Ad, Re). During the
time of recovery until the system reaches its pre-event functionality (or higher), losses
are generated. The sum of these losses over the recovery time yields the total loss
defined as:
• To quantify resilience we look at the savings of the system during the time of recovery,
i.e. losses not generated. A resilience descriptor is defined as
•
• Finally to building resilience is to minimize risk. To reach a clear link between these two
indicators we propose a risk definition in the form Risk=P(Loss)
POLDE 8/153
퐹
푑푡
To quantify resilience we look at the savings of the system during the time of recovery, i.e. losses
not generated. A resilience descriptor is defined as
푅푄 =
퐹
푑푡
Finally to building resilience is to minimize risk. To reach a clear link between these two indicators
we propose a risk definition in the form
푅푖푠푘 = 푃( 퐿표푠푠)
where 푃 denotes probability.
time
POLDE 8/153
퐹
푑푡
To quantify resilience we look at the savings of the system during the time of recovery, i.e. losses
not generated. A resilience descriptor is defined as
푅푄 =
퐹
푑푡
Finally to building resilience is to minimize risk. To reach a clear link between these two indicators
we propose a risk definition in the form
푅푖푠푘 = 푃( 퐿표푠푠)
where 푃 denotes probability.
time
absorption
Resilience
index

EQvis: Consequence-Based Risk Management for
www.grforum.org
Seismic Events
Scenario Manager
Data Catalog
Tables
2D & 3D Views
Charts and Reports
Data  Analysis  Visualization

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Demonstration in Hungary
Real time simulation:
An earthquake hits the region of Tolna county in Hungary. The team of
the disaster management in Tolna has to react on this situation and
take the necessary actions.
Goals
• Use the EQvis – Management tool to coordinate the teams of the
Disaster Management Team in Hungary
• Collect all information available for updating the scenario
• Constant scenario updating as soon as information is available
• Provide new information to the teams
• Risk estimation

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Satellite Images
Satellite Images helped
identifying the cloud and
updating the scenario
The satellite images were
just a few hours old

Added value for the Post 2015 Framework for
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Disaster Risk Reduction
• How did your work support the implementation of the Hyogo
Framework for Action:
– …..
– ……
– ……
• From your perspective what are the main gaps, needs and
further steps to be addressed in the Post 2015 Framework for
Disaster Risk Reduction in
– Research:
– Education & Training:
– Implementation & Practice:
– Policy:

Pistone_VCE_IDRC14

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