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StroNGER for Resilience in Rome

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Presentation at the Resilience in Rome Meeting, July 2-3, 2014

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StroNGER for Resilience in Rome

  1. 1. francesco.petrini@uniroma1.it , francesco.petrini@stronger2012.com -- *Research Associate, School of Civil and Industrial Engineering, Sapienza Università di Roma Via Eudossiana 18 - 00184 Rome (ITALY) tel. +39-06-44585072 StroNGER S.r.l., Co-founder and Director Via 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 StroNGERStroNGER Structures of the Next Generation – Energy harvesting and Resilience C. Crosti, S. Arangio, F. Petrini *, K. Gkoumas, F. Bontempi www.stronger2012.com
  2. 2. What is StroNGER Francesco Petrini. francesco.petrini@ustronger2012.com
  3. 3. A spin-off research Company Founded in November 2012 Operating in the civil and environmental engineering industry
  4. 4. Str o N GER www.stronger2012.com FromNovember2012 The research group of structural analysis and design at Sapienza Univ.
  5. 5. StroNGER – who we are Franco Bontempi, PhD StroNGER srl, Scientific Advisor Prof. of Structural Analysis and Design Sapienza University of Rome Expertise: -Fire Safety Engineering -Forensic Engineering Expertise: -Structural Safety -Structural Identification Expertise: -Wind Engineering -Performance Based Design Chiara Crosti, PhD StroNGER srl, CEO Francesco Petrini, PhD StroNGER srl, Vice Director Stefania Arangio, PhD StroNGER srl, Director Konstantinos Gkoumas, PhD StroNGER srl, Partner Expertise: -Energy Harvesting -Dependability Francesco Petrini. Co-founder and Director 5
  6. 6. Str o N GER www.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
  7. 7. 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 info@stronger2012.com Phone: +39 0644585070 Structures of the Next Generation – Energy harvesting and Resilience ResilienceWorkshop.RomeJuly02-032014
  8. 8. Energy Harvesting Francesco Petrini. francesco.petrini@ustronger2012.com StroNGER for Structures of the Next Generation – Energy harvesting and Resilience
  9. 9. 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 Conversion Use 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 9 ResilienceWorkshop.RomeJuly02-032014
  10. 10. Applications for the energy sustainability EH in buildings – a premise 10 • 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 10 ResilienceWorkshop.RomeJuly02-032014
  11. 11. Francesco Petrini. Co-founder and Director 11 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 wall HVAC lower wall ResilienceWorkshop.RomeJuly02-032014
  12. 12. Francesco Petrini. Co-founder and Director 12 PiezoTSensor © ResilienceWorkshop.RomeJuly02-032014 Air flow
  13. 13. Applications for the energy sustainability Energy 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 13 ResilienceWorkshop.RomeJuly02-032014
  14. 14. Francesco Petrini. Co-founder and Director 14 ResilienceWorkshop.RomeJuly02-032014 Vibration EH devices Flow-induced EH devices Applications for infrastructures
  15. 15. Resilience Francesco Petrini. francesco.petrini@ustronger2012.com StroNGER for Structures of the Next Generation – Energy harvesting and Resilience
  16. 16. Francesco Petrini. Co-founder and Director 16 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 NUCLEAR PLANT = 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 Disasterstrikes A L0 (dQ/dt)0 LOCAL- LEVEL: Contributeof 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
  17. 17. Francesco Petrini. Co-founder and Director 17 ResilienceWorkshop.RomeJuly02-032014 RISE–Framework Load Network Model for resilience Multi-hazard Scenarios Local Level Network Level Local resilience indicators Network resilience indicators ASSESSMENTandMITIGATION (Analysisforeachnodeandlink) Scenario output before mitigation Scenario output after mitigation ResISt framework 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 L0 i 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 Quality L0 = initial losses TR = 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
  18. 18. Francesco Petrini. francesco.petrini@ustronger2012.com Real application of the resilience concept A strategic infrastructure for water supply (serving about 1,300,000 people)
  19. 19. 19 Francesco Petrini. Co-founder and Director ResilienceWorkshop.RomeJuly02-032014 Real application of the resilience concept A strategic infrastructure for water supply (serving about 1,300,000 people)
  20. 20. 20 Seismic Action Francesco Petrini. Co-founder and Director ResilienceWorkshop.RomeJuly02-032014
  21. 21. 21 Critical Node Francesco Petrini. Co-founder and Director ResilienceWorkshop.RomeJuly02-032014
  22. 22. Energy and water supply infrastructure: representation WU WD HY CBCR CU RETAINING WALL UP (WU) RETAINING WALL DOWN (WD) HYDROELECTRIC POWER STATION (HY) CONDUIT UP (CU) CONDUIT ROSALBA CONDUIT PAVONCELLI BIS 1 2 3 4 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 Network Level Infrastructure representation Hazard Analysis 1 Load Network Model for resilience Multi-hazard Scenarios Local Level Network Level Local resilience indicators Network resilience indicators ASSESSMENTandMITIGATION (Analysisforeachnodeandlink) Scenario output before mitigation Scenario output after mitigation ResISt framework 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 L0 i 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 Quality L0 = initial losses TR = 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 ResilienceWorkshop.RomeJuly02-032014
  23. 23. 23 System with Elements connected in Parallel www.francobontempi.org Str o N GER
  24. 24. 24 www.francobontempi.org Str o N GER Damage at Local Level
  25. 25. 25 www.francobontempi.org Str o N GER Damage at Element Level
  26. 26. 26 www.francobontempi.org Str o N GER Damage at Structure Level
  27. 27. Energy and water supply infrastructure: scenarios FLOW REDUCTION (U)FLOW REDUCTION (R) ELECTRIC POWER INTERRUPTIONTOTAL FLOW INTERRUPTION (R+U) Consequencescenarios Network Model for resilience Multi-hazard Scenarios Network Level Infrastructure representation Hazard Analysis 1 Load Network Model for resilience Multi-hazard Scenarios Local Level Network Level Local resilience indicators Network resilience indicators ASSESSMENTandMITIGATION (Analysisforeachnodeandlink) Scenario output before mitigation Scenario output after mitigation ResISt framework 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 L0 i 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 Quality L0 = initial losses TR = 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 ResilienceWorkshop.RomeJuly02-032014
  28. 28. WU FAIL HY FAIL? CU FAIL? Y WU + WD +HY+ CU TOTAL FLOW TOTAL FLOW TOTAL FLOW NO R + E CR FAIL? WU WU + WD WU + WD + HY WD FAIL? N N N Y Y N N N N CR FAIL? CR FAIL? CR FAIL? NO R NO R NO U + E NO U+ E + R N N N N Y Y Y Y Fault-Treeanalysis Criticalseriesofcomponents WU WD HY CBCR CU Energy and water supply infrastructure: scenarios Network Model for resilience Multi-hazard Scenarios Network Level Infrastructure representation Hazard Analysis 1 Load Network Model for resilience Multi-hazard Scenarios Local Level Network Level Local resilience indicators Network resilience indicators ASSESSMENTandMITIGATION (Analysisforeachnodeandlink) Scenario output before mitigation Scenario output after mitigation ResISt framework 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 L0 i 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 Quality L0 = initial losses TR = 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 ResilienceWorkshop.RomeJuly02-032014
  29. 29. Load Network Model for resilience Multi-hazard Scenarios Local Level Network Level Local resilience indicators Network resilience indicators ASSESSMENTandMITIGATION (Analysisforeachnodeandlink) Scenario output before mitigation Scenario output after mitigation ResISt framework 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 L0 i 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 Quality L0 = initial losses TR = 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 ASSESSMENTandMITIGATION (Analysisforeachnodeandlink) 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 WD HY 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
  30. 30. Interactions on seismic fragility Load Network Model for resilience Multi-hazard Scenarios Local Level Network Level Local resilience indicators Network resilience indicators ASSESSMENTandMITIGATION (Analysisforeachnodeandlink) Scenario output before mitigation Scenario output after mitigation ResISt framework 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 L0 i 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 Quality L0 = initial losses TR = 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 L0 i TR i Local resilience indicators are evaluated for each node and Link and for each scenario IM (g) P(EDP|IM) WUWU WDWD++ Francesco Petrini. Co-founder and Director ResilienceWorkshop.RomeJuly02-032014 (0,0) (92,0) (92,29) (0,29) (0,54) (0,62) (28.5,6 2) (53,56) (63,45) (92,32) (92,34) WUWU WDWD
  31. 31. Francesco Petrini. francesco.petrini@ustronger2012.com ONGOING: Real application of the resilience concept Structural analysis of sea port defence structures for durability and robustness
  32. 32. ONGOING: Real application of the resilience concept Structural analysis of sea port defence structures for durability and robustness www.francobontempi.org Str o N GER
  33. 33. ONGOING: Real application of the resilience concept Structural analysis of sea port defence structures for durability and robustness www.francobontempi.org Str o N GER
  34. 34. “…. to provide, through innovation, advanced products and services for a sustainable and safe world.” StroNGER – Vision ResilienceWorkshop.RomeJuly02-032014 www.stronger2012.com

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