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StroNGER for Resilience in Rome
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[email protected] , [email protected]
--
*Research Associate,
School of Civil and Industrial Engineering, Sapienza Università di RomaVia Eudossiana 18 - 00184 Rome (ITALY)tel. +39-06-44585072
StroNGER S.r.l., Co-founder and DirectorVia Giacomo Peroni 442-444, Tecnopolo Tiburtino, 00131 Rome (ITALY)--
Informal Meeting on RESILIENCE
Rome, 2-3 July 2014
School of Civil and Industrial Engineering
University of Rome La Sapienza
StroNGERStroNGERStructures of the Next Generation – Energy harvesting and Resilience
C. Crosti, S. Arangio, F. Petrini *, K. Gkoumas, F. Bontempi
www.stronger2012.com
What is StroNGER
Francesco Petrini. [email protected]
A spin-off research Company
Founded in November 2012
Operating in the civil and environmental engineering industry
Stro N
GERwww.stronger2012.com
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The research group of structural analysis and design at Sapienza Univ.
StroNGER – who we are
Franco Bontempi, PhDStroNGER srl, Scientific Advisor
Prof. of Structural Analysis and DesignSapienza University of Rome
Expertise:-Fire Safety Engineering
-Forensic Engineering
Expertise:-Structural Safety-Structural Identification
Expertise:-Wind Engineering-Performance Based Design
Chiara Crosti, PhDStroNGER srl, CEO
Francesco Petrini, PhDStroNGER srl, Vice Director
Stefania Arangio, PhDStroNGER srl, Director
Konstantinos Gkoumas, PhDStroNGER srl, Partner
Expertise:-Energy Harvesting-Dependability
Francesco Petrini. Co-founder and Director
Stro N
GERwww.stronger2012.com
Academic research Industry research R&D
University courses Professional courses
Big group Small group
Design consultant activityResearch experience in structural analysis
CONVERSION: StroNG points
StroNGER S.r.l.
a Spin-off Company (Small Medium Enterprise)
that operates in the Civil Engineering industry.
High-profile tools and methodologies that lead to structures that fulfill required
performances under a resilience and sustainability point of view.
StroNGER expertise: •Design and rehabilitation of Civil structures and infrastructures with regard to wind, earthquakes, waves, landslides, fire and explosions. •Disaster resilience assessment. •Advanced numerical modeling of Civil structures and infrastructures. •Forensic engineering.•Sustainability and Energy Harvesting in Civil structures and infrastructures.
StroNGER has been recently awarded by the European Space Agency with the space technology transfer permanent award
StroNGER S.r.l. was founded in 2012 by researchers from the academic world working in the civil engineering field, each one having more than 10 years of experience in the field
www.stronger2012.com [email protected]
Phone: +39 0644585070
Structures of the Next Generation – Energy harvesting and Resilience
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Energy Harvesting
Francesco Petrini. [email protected]
StroNGER forStructures of the Next Generation – Energy harvesting and Resilience
Energy Harvesting (EH) can be defined as the sum of all those processes that allow to capture the freely available energy in the environment and convert it in (electric) energy that can be used or stored.
Harvesting ConversionUse
Storage
Energy harvesting - Overview
Francesco Petrini. Co-founder and Director
Resources
Sun
Water
Wind
Temperature differential
Mechanical vibrations
Acoustic waves
Magnetic fields
…
Extraction systems
Magnetic Induction
Electrostatic
Piezoelectric
Photovoltaic
Thermal Energy
Radiofrequency
Radiant Energy
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Applications for the energy sustainabilityEH in buildings – a premise
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• EH devices are used for powering remote monitoring sensors (e.g. temperature sensors, air quality sensors), also those placed inside heating, ventilation, and air conditioning (HVAC) ducts.
• These sensors are very important for the minimization of energy consumption in large buildings
Image courtesy of enocean-alliance®
http://www.enocean-alliance.org
Francesco Petrini. Co-founder and Director
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Francesco Petrini. Co-founder and Director
a. Steel plate (support)
b. Sensor transmitter module
c. Piezoelectric bender
d. Fin
e. Temperature probe
f. Tip mass
Proposal of space technology transfer for the design, testing, production and commercialization of a self-powered piezoelectric temperature and humidity sensor (PiezoTSensor), for the optimum energy management in building HVAC (Heating, Ventilation and Air Condition) systems.
PiezoTSensor ©
HVAC upper wall
HVAC lower wallHVAC lower wall
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Francesco Petrini. Co-founder and Director
PiezoTSensor ©R
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Air flow
Applications for the energy sustainabilityEnergy Harvesting for monitoring HVACs operating conditions
Currently:
•Power is provided by batteries or EH devices based on thermal or RF methods
•Sensors work intermittently (to consume less power ~ 100µW)
An EH sensor based on piezoelectric material has several advantages being capable to provide up to 10-15 times more power than currently used devices leading to additional applications or
longer operation time.
Image courtesy of enocean-alliance®
http://www.enocean-alliance.org
Francesco Petrini. Co-founder and Director
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Francesco Petrini. Co-founder and Director
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Vibration EH devices
Flow-induced EH devices
Applications for infrastructures
Resilience
Francesco Petrini. [email protected]
StroNGER forStructures of the Next Generation – Energy harvesting and Resilience
Francesco Petrini. Co-founder and Director
RISE – Concept resume
MCEER (Multidisciplinary Center for Earthquake Engineering Research), (2006). “MCEER’s Resilience Framework”.
-- = ordinary node
= critical node in case of emergency---
= principal link (e.g. road)
HOSPITAL
HOUSE AGGRGATE
MALL
SHOPPING CENTEROFFICE
HOUSE AGGRGATE
FIRE DEPARTMENT
NUCLEAR PLANT
HOSPITAL
HOUSE AGGRGATE
MALL
SHOPPING CENTEROFFICE
HOUSE AGGRGATE
FIRE DEPARTMENT
NUCLEARPLANT
= earthquake action
= blast action= fire action
Representation of a large infrastructure as a network of nodes and links
Nodes: relevant premises of the infrastructure Links: local and access roads, pipelines and supply system
Initial losses
Recovery time:• Resourcefulness• Rapidity
Disaster strikes
A
L0
(dQ/dt)0
LOCAL- LEVEL:Contribute of the single premise (e.g. hospital, by considering the interrelations with proximity elements)
NETWORK- LEVEL:- Convolution of the local-level contributes
dLi
Quantitative definition of Resilience (MCEER) R.I.S.E. Multiscale philosophy
Disaster strikes --> Hazard scenario
Francesco Petrini. Co-founder and Director
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RIS
E –
Fram
ew
ork
Load
Network Model for resilience
Multi-hazard Scenarios
Local Level
NetworkLevel
Local resilience indicators Network resilience indicatorsA
SSES
SMEN
T a
nd
MIT
IGAT
ION
(A
na
lysi
s fo
r ea
ch n
od
e a
nd
lin
k)
Scenario output before mitigation
Scenario output after mitigation
ResIStframework for resilience assessment
Structure performanceA
B Recovery
E.g. Repair time
Damage
Action
Damage/Disservice
% of rescued
Action values
IM
A
IM
100 %
People safetyB
Quality
Indicator
Status of nodes and links(no interaction)
A
Quality
Indicator
Interactions effects (quality drop)B
L0i TR
i
Quality (network level)
Combination of local indicators
Indicator
L0 TR
Resilience ∞ 1 /A
C
Local resilience indicators are evaluated for each node and Link and for each scenario
Network resilience indicators are evaluated for each scenario
---- = Output
---- = comment
Qua
lity
L0 = initial lossesTR = recovery time
Infrastructure representation
Hazard Analysis
Protection analysis
Performance analysis
Resilience Assessment
Network Level
1
2 System Recovery functionD
** Picture taken from:
Decò A., Bocchini P., Frangopol D.M.. A probabilistic approach for the prediction of seismic resilience of bridges.
Earthquake Engineering and Structural Dynamics, Wiley, DOI: 10.1002/eqe.2282
Recovery analysis
**
3
RISE framework for resilience assessment
Francesco Petrini. [email protected]
Real application of the resilience conceptA strategic infrastructure for water supply (serving about 1,300,000 people)
19Francesco Petrini. Co-founder and Director
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Real application of the resilience conceptA strategic infrastructure for water supply (serving about 1,300,000 people)
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Seismic ActionFrancesco Petrini. Co-founder and Director
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Critical Node
Francesco Petrini. Co-founder and Director
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Energy and water supply infrastructure: representation
WU
WDHY
CBCR
CU
RETAINING WALL UP (WU) RETAINING WALL DOWN (WD) HYDROELECTRIC POWER STATION (HY)
CONDUIT UP (CU) CONDUIT ROSALBA
CONDUIT PAVONCELLI BIS
1
2
34
5
6
7
1 2 3
4 5 6
7
HYDRAULIC JUNCTION
ELECTRICITY
WATER
Infrastructure plan view Individuation of the system/network components Representation of the system
Outputs
Network Model for resilience
Multi-hazard Scenarios
NetworkLevel
Infrastructure representation
Hazard Analysis
1 Load
Network Model for resilience
Multi-hazard Scenarios
Local Level
NetworkLevel
Local resilience indicators Network resilience indicators
ASS
ESS
MEN
T a
nd
MIT
IGAT
ION
(A
nal
ysis
for
ea
ch n
ode
and
link
)
Scenario output before mitigation
Scenario output after mitigation
ResIStframework for resilience assessment
Structure performanceA
B Recovery
E.g. Repair time
Damage
Action
Damage/Disservice
% of rescued
Action values
IM
A
IM
100 %
People safetyB
Quality
Indicator
Status of nodes and links(no interaction)
A
Quality
Indicator
Interactions effects (quality drop)B
L0i TR
i
Quality (network level)
Combination of local indicators
Indicator
L0 TR
Resilience ∞ 1 /A
C
Local resilience indicators are evaluated for each node and Link and for each scenario
Network resilience indicators are evaluated for each scenario
---- = Output
---- = comment
Qu
alit
y
L0 = initial lossesTR = recovery time
Infrastructure representation
Hazard Analysis
Protection analysis
Performance analysis
Resilience Assessment
Network Level
1
2 System Recovery functionD
** Picture taken from:
Decò A., Bocchini P., Frangopol D.M.. A probabilistic approach for the prediction of seismic resilience of bridges.
Earthquake Engineering and Structural Dynamics, Wiley, DOI: 10.1002/eqe.2282
Recovery analysis
**
3
RISE framework for resilience assessment
Francesco Petrini. Co-founder and Director
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System with Elements connected in Parallel
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Damage at Local Level
25
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Damage at Element Level
26
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Damage at Structure Level
Energy and water supply infrastructure: scenarios
FLOW REDUCTION (U)FLOW REDUCTION (R)
ELECTRIC POWER INTERRUPTIONTOTAL FLOW INTERRUPTION (R+U)
Co
nse
qu
en
ce s
cen
ario
sNetwork Model for
resilience
Multi-hazard Scenarios
NetworkLevel
Infrastructure representation
Hazard Analysis
1 Load
Network Model for resilience
Multi-hazard Scenarios
Local Level
NetworkLevel
Local resilience indicators Network resilience indicators
ASS
ESS
MEN
T a
nd
MIT
IGAT
ION
(A
nal
ysis
for
ea
ch n
ode
and
link
)
Scenario output before mitigation
Scenario output after mitigation
ResIStframework for resilience assessment
Structure performanceA
B Recovery
E.g. Repair time
Damage
Action
Damage/Disservice
% of rescued
Action values
IM
A
IM
100 %
People safetyB
Quality
Indicator
Status of nodes and links(no interaction)
A
Quality
Indicator
Interactions effects (quality drop)B
L0i TR
i
Quality (network level)
Combination of local indicators
Indicator
L0 TR
Resilience ∞ 1 /A
C
Local resilience indicators are evaluated for each node and Link and for each scenario
Network resilience indicators are evaluated for each scenario
---- = Output
---- = comment
Qu
alit
y
L0 = initial lossesTR = recovery time
Infrastructure representation
Hazard Analysis
Protection analysis
Performance analysis
Resilience Assessment
Network Level
1
2 System Recovery functionD
** Picture taken from:
Decò A., Bocchini P., Frangopol D.M.. A probabilistic approach for the prediction of seismic resilience of bridges.
Earthquake Engineering and Structural Dynamics, Wiley, DOI: 10.1002/eqe.2282
Recovery analysis
**
3
RISE framework for resilience assessment
Francesco Petrini. Co-founder and Director
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WU FAIL
HY FAIL?
CU FAIL?
Y
WU + WD +HY+ CU
TOTAL FLOW
TOTAL FLOW
TOTAL FLOW
NO R + E
CRFAIL?
WU
WU + WD
WU + WD + HY
WD FAIL?
N
N
N
Y
Y
N
N
N
N
CRFAIL?
CRFAIL?
CRFAIL?
NO R
NO R
NO U + E
NO U+ E + R
N
N
N
N
Y
Y
Y
Y
Fau
lt-T
ree
an
alys
is
Cri
tica
l se
rie
s o
f co
mp
on
en
ts
WU
WDHY
CBCR
CU
Energy and water supply infrastructure: scenarios
Network Model for resilience
Multi-hazard Scenarios
NetworkLevel
Infrastructure representation
Hazard Analysis
1 Load
Network Model for resilience
Multi-hazard Scenarios
Local Level
NetworkLevel
Local resilience indicators Network resilience indicators
ASS
ESS
MEN
T a
nd
MIT
IGAT
ION
(A
nal
ysis
for
ea
ch n
ode
and
link
)
Scenario output before mitigation
Scenario output after mitigation
ResIStframework for resilience assessment
Structure performanceA
B Recovery
E.g. Repair time
Damage
Action
Damage/Disservice
% of rescued
Action values
IM
A
IM
100 %
People safetyB
Quality
Indicator
Status of nodes and links(no interaction)
A
Quality
Indicator
Interactions effects (quality drop)B
L0i TR
i
Quality (network level)
Combination of local indicators
Indicator
L0 TR
Resilience ∞ 1 /A
C
Local resilience indicators are evaluated for each node and Link and for each scenario
Network resilience indicators are evaluated for each scenario
---- = Output
---- = comment
Qu
alit
y
L0 = initial lossesTR = recovery time
Infrastructure representation
Hazard Analysis
Protection analysis
Performance analysis
Resilience Assessment
Network Level
1
2 System Recovery functionD
** Picture taken from:
Decò A., Bocchini P., Frangopol D.M.. A probabilistic approach for the prediction of seismic resilience of bridges.
Earthquake Engineering and Structural Dynamics, Wiley, DOI: 10.1002/eqe.2282
Recovery analysis
**
3
RISE framework for resilience assessment
Francesco Petrini. Co-founder and Director
Re
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Load
Network Model for resilience
Multi-hazard Scenarios
Local Level
NetworkLevel
Local resilience indicators Network resilience indicators
ASS
ESS
MEN
T a
nd
MIT
IGAT
ION
(A
nal
ysis
for
ea
ch n
ode
and
link
)
Scenario output before mitigation
Scenario output after mitigation
ResIStframework for resilience assessment
Structure performanceA
B Recovery
E.g. Repair time
Damage
Action
Damage/Disservice
% of rescued
Action values
IM
A
IM
100 %
People safetyB
Quality
Indicator
Status of nodes and links(no interaction)
A
Quality
Indicator
Interactions effects (quality drop)B
L0i TR
i
Quality (network level)
Combination of local indicators
Indicator
L0 TR
Resilience ∞ 1 /A
C
Local resilience indicators are evaluated for each node and Link and for each scenario
Network resilience indicators are evaluated for each scenario
---- = Output
---- = comment
Qu
alit
y
L0 = initial lossesTR = recovery time
Infrastructure representation
Hazard Analysis
Protection analysis
Performance analysis
Resilience Assessment
Network Level
1
2 System Recovery functionD
** Picture taken from:
Decò A., Bocchini P., Frangopol D.M.. A probabilistic approach for the prediction of seismic resilience of bridges.
Earthquake Engineering and Structural Dynamics, Wiley, DOI: 10.1002/eqe.2282
Recovery analysis
**
3
RISE framework for resilience assessment
Load
Local Level
ASS
ESSM
ENT
an
d M
ITIG
ATI
ON
(A
na
lysi
s fo
r ea
ch n
od
e a
nd
lin
k)
Structure performanceA
B Recovery
E.g. Repair time
Damage
Action
Damage/Disservice
% of rescued
Action values
IM
A
IM
100 %
People safetyB
Protection analysis
Performance analysis
2
Critical series of components: retaining walls
WU
WDHY
CBCR
CU
(0,0) (92,0)
(92,29)(0,29)
(0,54)
(0,62) (28.5,62)
(53,56)
(63,45)
(92,32)
(92,34)
Critical series of components
FE model
Interactions on seismic fragility
Load
Network Model for resilience
Multi-hazard Scenarios
Local Level
NetworkLevel
Local resilience indicators Network resilience indicators
ASS
ESS
MEN
T a
nd
MIT
IGAT
ION
(A
nal
ysis
for
ea
ch n
ode
and
link
)
Scenario output before mitigation
Scenario output after mitigation
ResIStframework for resilience assessment
Structure performanceA
B Recovery
E.g. Repair time
Damage
Action
Damage/Disservice
% of rescued
Action values
IM
A
IM
100 %
People safetyB
Quality
Indicator
Status of nodes and links(no interaction)
A
Quality
Indicator
Interactions effects (quality drop)B
L0i TR
i
Quality (network level)
Combination of local indicators
Indicator
L0 TR
Resilience ∞ 1 /A
C
Local resilience indicators are evaluated for each node and Link and for each scenario
Network resilience indicators are evaluated for each scenario
---- = Output
---- = comment
Qu
alit
y
L0 = initial lossesTR = recovery time
Infrastructure representation
Hazard Analysis
Protection analysis
Performance analysis
Resilience Assessment
Network Level
1
2 System Recovery functionD
** Picture taken from:
Decò A., Bocchini P., Frangopol D.M.. A probabilistic approach for the prediction of seismic resilience of bridges.
Earthquake Engineering and Structural Dynamics, Wiley, DOI: 10.1002/eqe.2282
Recovery analysis
**
3
RISE framework for resilience assessment
Local resilience indicators
Quality
Indicator
Status of nodes and links(no interaction)
A
Quality
Indicator
Interactions effects (quality drop)B
L0i TR
i
Local resilience indicators are evaluated for each node and Link and for each scenario
IM (g)
P(E
DP
|IM
)
WUWU WDWD++
Francesco Petrini. Co-founder and Director
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(0,0) (92,0)
(92,29)(0,29)
(0,54)
(0,62) (28.5,62)
(53,56)
(63,45)
(92,32)
(92,34)
WUWU
WDWD
Francesco Petrini. [email protected]
ONGOING: Real application of the resilience conceptStructural analysis of sea port defence structures for durability and robustness
ONGOING: Real application of the resilience conceptStructural analysis of sea port defence structures for durability and robustness
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ONGOING: Real application of the resilience conceptStructural analysis of sea port defence structures for durability and robustness
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“…. to provide, through innovation, advanced products and services for a sustainable and safe world.”
StroNGER – VisionR
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www.stronger2012.com