Abstract
In seismic risk assessment, the sources of uncertainty associated with building exposure modelling have not received as much attention as other components related to hazard and vulnerability. Conventional practices such as assuming absolute portfolio compositions (i.e., proportions per building class) from expert-based assumptions over aggregated data crudely disregard the contribution of uncertainty of the exposure upon earthquake loss models. In this work, we introduce the concept that the degree of knowledge of a building stock can be described within a Bayesian probabilistic approach that integrates both expert-based prior distributions and data collection on individual buildings. We investigate the impact of the epistemic uncertainty in the portfolio composition on scenario-based earthquake loss models through an exposure-oriented logic tree arrangement based on synthetic building portfolios. For illustrative purposes, we consider the residential building stock of Valparaíso (Chile) subjected to seismic ground-shaking from one subduction earthquake. We have found that building class reconnaissance, either from prior assumptions by desktop studies with aggregated data (top–down approach), or from building-by-building data collection (bottom–up approach), plays a fundamental role in the statistical modelling of exposure. To model the vulnerability of such a heterogeneous building stock, we require that their associated set of structural fragility functions handle multiple spectral periods. Thereby, we also discuss the relevance and specific uncertainty upon generating either uncorrelated or spatially cross-correlated ground motion fields within this framework. We successively show how various epistemic uncertainties embedded within these probabilistic exposure models are differently propagated throughout the computed direct financial losses. This work calls for further efforts to redesign desktop exposure studies, while also highlighting the importance of exposure data collection with standardized and iterative approaches.
Highlights
Epistemic uncertainties stem from the incomplete knowledge of the actual problem and its parameters, or from, often unavoidable, modelling and methodology errors (e.g., Vamvatsikos et al 2010)
Considering that PGA alone is not a sufficient IM to model the various structural fragility functions that a real heterogeneous building portfolio requires (Luco and Cornell 2007), when we decide to use more realistic fragility functions for the heterogeneous portfolio, the in Fig. 14 we present normalized loss curves using the results obtained from the top–down assumption and without correlation per scheme
Expert-elicited models used by top–down approaches, even when carefully crafted (e.g., Ma et al 2021) may often provide only a partial perspective of the real composition of the building stock, while bottom–up approaches based on field-surveys are usually resource-intensive and are seldom carried out systematically
Summary
Epistemic uncertainties stem from the incomplete knowledge of the actual problem and its parameters, or from, often unavoidable, modelling and methodology errors (e.g., Vamvatsikos et al 2010). Together with the hazard and vulnerability components, the exposure contributes to most quantitative risk assessment applications In such studies, the degree of knowledge of the hazard and exposure components plays a fundamental role since their associated uncertainties are propagated to the final loss estimates. Being able to track and disaggregate the influence of the hazard and exposure is a crucial factor for decision making, urban planning, and finance (e.g., the insurance industry). In the latter, the smaller the variation in the mean loss values, the lower the risk is perceived (Wesson and Perkins 2001)
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