Methods for evaluation and treatment of epistemic uncertainty in portfolio losses due to earthquakes

Abstract

Assessment of seismic losses in a portfolio of buildings can be a challenging task, since there can be large epistemic uncertainties associated with the different steps of the probabilistic seismic risk analysis: hazard estimation, exposure modeling, fragility functions, and damage-to-loss estimation. Refining models and gathering more data to reduce the epistemic uncertainties can require substantial time investment and incur significant costs; therefore, to make this process more efficient, variables that drive the epistemic uncertainty must be identified. This paper explores the use of two sensitivity analysis methods to evaluate the effect of uncertain variables on the epistemic uncertainty of portfolio losses from earthquakes: 1) a well established variance-based sensitivity analysis technique and 2) a novel method that leverages regression tree ensemble methods with functional outputs. The two methods are examined using a fictional portfolio of 20 buildings in the San Francisco Bay Area. The results from the methods are compared, and advantages and disadvantages of the regression tree ensemble method are highlighted. Also discussed are recommendations for treatment of uncertain input variables based on insights about epistemic uncertainty in the losses