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Lessons from the Nisqually Earthquake for PBEE

Lessons from the Nisqually Earthquake for PBEE. Stephanie E. Chang. PBEE Premise.

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Lessons from the Nisqually Earthquake for PBEE

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  1. Lessons from the Nisqually Earthquake for PBEE Stephanie E. Chang

  2. PBEE Premise • “Performance-based earthquake engineering implies design, evaluation, and construction of engineered facilities whose performance under common and extreme loads responds to the diverse needs and objectives of owner-users and society. PBEE is based on the premise that performance can be predicted and evaluated with sufficient confidence for the engineer and client jointly to make intelligent and informed decisions...” (Krawinkler, 1999)

  3. Pioneer Square SoDo Nisqually Earthquake Study • 2 business districts • “Complete” sample • 107 businesses in 62 bldgs. • In-person, structured interviews: • business info • damage type / cost • financing repair • business interruption • mitigations

  4. Costs and Stakeholders

  5. 1. Hidden costs and consequences are very important. What kinds of impacts…?(N=107) 46% had no structural damage 89% because of “lack of customers”; 87% are retail 93% because of “loss of customer base”; 86% are retail

  6. Hidden costs (cont’d) How will you pay for…?

  7. 2. Structural damage is a very imprecise predictor of business impacts. Short-term revenue loss?

  8. Damage as Predictor (cont’d) Yellow-tag building occupants only: Short-term revenue loss?

  9. Own-business problems Financing (11) Permits for repair (5) Dislocation (3) _____ 19 Externality effects can be a major source of loss. Most important recovery problem? • Externality-type problems • Customer loss (11) • Street closure – parking (11) • Media perception (6) • 1st Ave. parking lane (4) • Return to status quo (parking/attitudes…) (2) • Ongoing Repairs in area (6) • _____ • 40

  10. Pioneer Square (Jackson St./Fenix Bl.)

  11. SoDo (buslane)

  12. Lessons for PBEE • Selection of appropriate decision variable(s) (e.g., annual loss, exceedance of limit states) is highly complex and ambiguous. • Multiple relevant categories of loss • Difficult to predict & loosely correlated with structural damage state • Different stakeholder perspectives

  13. Lessons (cont’d) • Need to recognize high degree of uncertainty. • As important for DV as for damage and intensity prediction. • Need much more empirical data, model validation.

  14. Lessons (cont’d) • Divergence between public and private objectives should be considered. • Impacts of performance decisions on neighbors, local area. • Role of public sector?

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