A Framework for Understanding Uncertainty in Seismic Risk Assessment. Issue 1 (11th October 2017)

• Record Type: Journal Article
• Authors: Foulser‐Piggott, Roxane; Bowman, Gary; Hughes, Martin
• Is Part Of: Risk analysis. Volume 40:Issue 1(2020)
• Page Start: 169
• Page End: 182
• Publication Date: 2017-10-11
• Subjects: Aleatory and epistemic uncertainty -- annual probability of collapse -- risk assessment -- seismic hazard -- sensitivity analysis; Technology -- Risk assessment -- Periodicals; 658.403
• DOI: 10.1111/risa.12919
• Languages: English
• ISSNs: 0272-4332

Abstract

Abstract: 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.