A Bayesian benefit-risk model applied to the South Florida Building Code

James D. Englehardt, Chengjun Peng

Research output: Contribution to journalArticle

24 Scopus citations

Abstract

A Bayesian compound Poisson benefit-risk model is described in this paper, and used to evaluate recent revisions to the South Florida Building Code (SFBC). The model accounts for natural variability in hurricane frequency and severity, and uncertainty in the effectiveness of the revised code. Ranges of residential growth rate, code effectiveness, construction cost increase, and planning period length are assumed, to show the ranges of cost-to- performance ratio within which the code wilt make sense economically. The expected cost of residential hurricane damage over 50 years for ten South Florida counties assuming continuation of previous building practices was $93 billion, equivalent to the residential damage of 5.2 Andrews. Assuming a reduction in the growth of damageable housing in South Florida from 5.5% to 2% as a result of code revision, estimated damages under the new code were $45 billion. At a per-house construction cost increase of 5%, the probability of at least recovering the estimated $40 billion cost of the specified wind- resistant construction was estimated to be 47%. Expected return on investment was estimated at $7 billion over 50 years. The expected return lies between a $44 billion loss and a $47 billion gain, when growth in damageable housing is allowed to range from 1% to 4% and construction cost increases are assumed to lie between 3% and 8%. Actual monetary return for a 5% cost increase and 2% growth in damageable housing ranges from a $20 billion loss to a $100 billion gain with 95% probability, as a result of weather variability alone. Results support SFBC revisions on solely economic grounds, a conclusion strengthened considerably in light of potentially avoided deaths and hurricane traumas. The model represents one approach to evaluating economic aspects of the sustainability of new technological measures on the basis of available information.

Original languageEnglish (US)
Pages (from-to)81-91
Number of pages11
JournalRisk Analysis
Volume16
Issue number1
DOIs
StatePublished - Jan 1 1996

Keywords

  • Bayesian
  • Benefit
  • construction
  • disaster
  • hurricane

ASJC Scopus subject areas

  • Safety, Risk, Reliability and Quality
  • Physiology (medical)

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