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A Framework for Understanding Uncertainty in Seismic Risk Assessment
Author(s) -
FoulserPiggott Roxane,
Bowman Gary,
Hughes Martin
Publication year - 2020
Publication title -
risk analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.972
H-Index - 130
eISSN - 1539-6924
pISSN - 0272-4332
DOI - 10.1111/risa.12919
Subject(s) - seismic risk , vulnerability (computing) , uncertainty quantification , risk assessment , range (aeronautics) , induced seismicity , uncertainty analysis , sensitivity (control systems) , propagation of uncertainty , econometrics , computer science , risk analysis (engineering) , engineering , mathematics , civil engineering , simulation , machine learning , algorithm , medicine , electronic engineering , aerospace engineering , computer security
A better understanding of the uncertainty that exists in models used for seismic risk assessment is critical to improving risk‐based decisions pertaining to earthquake safety. Current models estimating the probability of collapse of a building do not consider comprehensively the nature and impact of uncertainty. This article presents a model framework to enhance seismic risk assessment and thus gives decisionmakers a fuller understanding of the nature and limitations of the estimates. This can help ensure that risks are not over‐ or underestimated and the value of acquiring accurate data is appreciated fully. The methodology presented provides a novel treatment of uncertainties in input variables, their propagation through the model, and their effect on the results. The study presents ranges of possible annual collapse probabilities for different case studies on buildings in different parts of the world, exposed to different levels of seismicity, and with different vulnerabilities. A global sensitivity analysis was conducted to determine the significance of uncertain variables. Two key outcomes are (1) that the uncertainty in ground‐motion conversion equations has the largest effect on the uncertainty in the calculation of annual collapse probability; and (2) the vulnerability of a building appears to have an effect on the range of annual collapse probabilities produced, i.e., the level of uncertainty in the estimate of annual collapse probability, with less vulnerable buildings having a smaller uncertainty.