Measuringg and Improving the Resilience of and Improving ... · Undesired outcome An Undesired...

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Measuring g and Improving the Resilience of Measuring g and Improving the Resilie a Built Environment in a Community 4 X D Q Z D Q J J / L 'HSDUWPHQW RI &LYLO (QJLQHHULQJ 7VLQJKXD 8QLYHUVLW\ YLO (QJLQ 2FW WK HULQJ J 7V QHH WK International Workshop on Modeling of Physical, Economic, and Social Systems for Resilience Assessment 6 6FKRRO 6FKRRO 3 3XEOLF VHUYLFH 3XEOLF VHUYLFH 3XEOLF VHUYLFH 3XEOLF VHUYLFH 5HVLGHQWLDO 5HVLGHQWLDO 5HVLGHQWLDO 5HVLGHQWLDO %XVLQHVV %XVLQHVV %XVLQHVV %XVLQHVV 5HVLGHQWLDO 5HVLGHQWLDO 5HVLGHQWLDO 5HVLGHQWLDO %XVLQHVV % %XVLQHVV Built environment within a a c community 1 Residential Commercial Education government %XLOGLQJ 6HFWRUV LQWHUGHSHQGHQW LQ WKHLU IXQFWLRQV Pre-Workshop Draft for Workshop Attendees Only. 4-43

Transcript of Measuringg and Improving the Resilience of and Improving ... · Undesired outcome An Undesired...

Page 1: Measuringg and Improving the Resilience of and Improving ... · Undesired outcome An Undesired Outcome is an event whose occurrence would adversely impact a community’s ability

Measuringg and Improving the Resilience of Measuringg and Improving the ResilieaBuilt Environment in a Community

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Built environment within a a ccommunity

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Residential Commercial

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Natural Hazards

� Hurricanes

� Earthquakes

� Landslides

� Floods and storm surge

� ȾȾȾȾ

2

Resilience of built environment

3

Modifications before disruptive events

Time

Functionality

LostFunctionality

ResidualFunctionality

Time to full recovery

Repair after the disruptive events to restore system functionality

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Undesired outcome

� An Undesired Outcome is an event whose occurrence would adversely impact a community’s ability to function normally.

� Population Outmigration (PO) is chosen as a community resilience metric.

� Community Functionality is defined as the percentage of population remaining in the community.

4

An Undesired Outcome is an event whose o

Essential community functions

5

� 4 essential community functionsHousing (1); Employment(2); Education(3); Public service (4)

The functions are supported by building sectors

po,i: Probability of PO conditioned on the loss of function i onlypo,ij: Probability of PO conditioned on the loss of both functions i &jpo,ijk or po,ijkh : Probability of PO conditioned on the loss of more functions

Pi: Probability of function i being lost (independent)

� Measuring community functionality

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Measuring community functionality

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Calibration according to o Mieler’ss work

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Mieler, M., Stojadinovic, B., Budnitz, R., Comerio, M., & Mahin, S. (2015). “A framework for linking community-resilience goals to specific performance targets for the built environment”. Earthquake Spectra, 31(3), 1267-1283.

OutcomesFunction

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Residents P1�2% P1�10% P1�20% P1>20%

Business P2�5% P2�20% P2�50% P2>50%

Education P3�5% P3�10% P3�20% P3>20%

Public Service P4�5% P4�20% P4�50% P4>50%

If two or more functions are in read, or three or more functions are in yellow, significant outmigration may occur

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Beijing urban area

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An assumed community

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BuildingSector

BuildingCategory Number of Buildings Design Seismic

Intensity Level (SIL)

Residential buildings

R6 162 6 (0.07g)R7 203 7 (0.13g)R8 41 8 (0.25g)

Businessbuildings

B6 24 6 (0.07g)B7 32 7 (0.13g)B8 6 8 (0.25g)

Educationbuildings

E6 23 6 (0.07g)E7 23 7 (0.13g)E8 12 8 (0.25g)

Public service buildings

P6 13 6 (0.07g)P7 13 7 (0.13g)P8 6 8 (0.25g)

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Seismic behavior

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A power-law relationshipbetween structural inter-story drift ratio, T, and seismic intensity measure, PGA.

It is assumed the building becomes unoccupiable as Texceeds 1%.

Community functionality vs. seismic intensity

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Communityperformance needs to be improved

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Retrofit Cost

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Optimum retrofit strategy

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Mainly enhances building sectors which are relatively less in amount as well as in retrofit costs, resulting higher cost efficiencies

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Support of traffic system to recovery

� Traffic system provides support to the recovery process of damaged buildings

� recourse supply rate of an individual building

� Weighted supply rate of a community

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Bridge Fragility

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Bridges on second and third ring roads Bridges on forth and fifth ring roads

Damage State Slight Moderate Severe Collapse

Remained Capacity 1.0 0. 75 0.5 0.0

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Assumptions

� The damages of individual bridges are assumed to be independent.

� For each zone, the needed resource

� An optimization problem

Target: maximizing the weighted supply rate

using Simplex algorithm

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Probability distribution of supply rate

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Mean = 0.567 Mean = 0.820

0.02

0.06

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0.22

0.25

0.12

0.02 0.01

0.02 0.04

0.09

0.17

0.29 0.29

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Immediately after the disruptive event One month after the disruptive event

Subjected to an earthquake with seismic intensity of 8

Recovery trajectory

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Averagely, the community is back to normal after 3 months

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Recovery time (month)

90 percentile

Mean

10 percentile

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Seismic retrofitting to the bridges

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All bridges are retrofitted to the seismic intensity of level 9

Retrofitting effect ct –– Comparison of supply rate

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0.02 0.06

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0.25

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retrofitted

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current

retrofitted

Immediately after the disruptive event One month after the disruptive event

Mean: 0.567 vs. 0.703 Mean: 0.820 vs. 0.922

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Retrofitting effect ct –– Comparison of recovery

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The average time to be back to normal: 3 months

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Recovery time (month)

90 percentile

Mean

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Reconvery time (month)

90 percentile

Mean

10 percentile

The average time to be back to normal: 2 months

Traffic system is not retrofitted Traffic system is retrofitted

Summary

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� A measurement of community functionality is proposed, and its application is demonstrated

� The methodology to optimize the retrofitting strategy is developed.

� The supply rate is propposed to evaluate the performance of traffic system to support the recovery of built environment

Thank you for your attention!

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Dueñas-Osorio, L., Craig, J. I., Goodno, B. J., & Bostrom, A. (2007). Interdependent response of networked systems. Journal of Infrastructure Systems, 13(3), 185-194.

Bocchini, P., and Frangopol, D. M. (2012). Restoration of bridge networks after an earthquake: multicriteria intervention optimization. Earthquake Spectra, 28(2), 426-455.

Bruneau, M., Chang, S., Eguchi, R., Lee, G., O’Rourke, T., Reinhorn, A.M., Shinozuka, M., Tierney, K., Wallace, W., and Winterfelt, D.V. (2003). “A framework to quantitatively assess and enhance the seismic resilience of communities.” Earthquake Spectra, 19 (4), 733–752.

Chang S E, Shinozuka M. (2004). “Measuring improvements in the disaster resilience of communities”. Earthquake Spectra, 20(3): 739-755.

Cutter, S.L. Burton, C.G., and Emrich, C.T. (2010). “Disaster resilience indictor for benchmarking baseline conditions.” J. Homeland Security and Emergency Management, 7(1): Article 51.

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Lin P., Wang N. and Ellingwood BR. (2016). “A Risk De-aggregation Framework that Relates Community Resilience Goals to Building Performance Objectives”. Sustainable and Resilient Infrastructure add DOI

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Lounis, Z., & McAllister, T. P. (2016). Risk-based decision making for sustainable and resilient infrastructure systems. Journal of Structural Engineering, F4016005.

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Mieler, M., Stojadinovic, B., Budnitz, R., Comerio, M., & Mahin, S. (2015). “A framework for linking community-resilience goals to specific performance targets for the built environment”. Earthquake Spectra, 31(3), 1267-1283.

Miles, S.B. and Chang, S.E. (2006) “Modeling community recovery from earthquakes,” Earthquake Spectra, 22(2): 439–458.

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