Multiobjective Risk Partitioning: An Application to Dam Safety Risk Analysis

Abstract

Risk -- a measure of the probability and severity of adverse events - - has commonly been measured by the traditional Bayesian expected value approach. While a reasonable measure for some cases, the expected value approach is inadequate and may lead to fallacious conclusions when applied to risks associated with extreme and catastrophic events and where public policy issues are involved. Furthermore, risk analysis is often divided into two components: risk assessment of hazards, both natural and technological, and risk management options designed to solve or ameliorate a hazardous situation. While conventional, statistically based risk assessment methods are appropriate in characterizing hazards, they are not always appropriate for the evaluation and management of those hazards. In particular, the use of the transitional expected value in the assessment of low-probability/high-consequences (LP/HC) risk is inadequate because this approach does not distinguish between events with high probability of exceedance and catastrophic consequence. To study the risks associated with dam failure, the traditional unconditional expectation will be augmented with the conditional expectation generated by the partitioned multiobjective risk method (PMRM). This report documents an application of the PMRM to a real, albeit somewhat idealized, dam safety case study.

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Document Details

Document Type
Technical Report
Publication Date
Apr 01, 1988
Accession Number
ADA197011

Entities

People

  • James Mitsiopoulos
  • Per-ola Karlsson
  • Raja Petrakian
  • Yacov Y. Haimes

Tags

Communities of Interest

  • Biomedical
  • Energy and Power Technologies
  • Human Systems

DTIC Thesaurus Topics

  • Civil Engineering
  • Climate Change
  • Computational Science
  • Computers
  • Cost Analysis
  • Databases
  • Distribution Functions
  • Economic Analysis
  • Engineers
  • Flood Control
  • Floods
  • Information Science
  • Probability Distributions
  • Random Variables
  • Risk
  • Risk Analysis
  • Systems Engineering

Readers

  • Aviation Safety Risk Assessment.
  • Operations Research
  • Statistical inference.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - DoD AI Strategy