Epistemic uncertainty of probabilistic building exposure compositions in scenario-based earthquake loss models

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.

Risk-informed representative earthquake scenarios for Valparaíso and Viña del Mar, Chile

“…The uncertainty in the median prediction is modelled with a Gaussian random field, with the spatial correlation model of Jayaram and Baker (2009). The choice of these models is based on the epistemic uncertainty analysis of different ground motion and correlation models by Gómez-Zapata et al (2022b), who analyzed the same study area. For the scope of this study, we do not consider cross-correlated ground motion fields.…”

Towards a Sensitivity Analysis in Seismic Risk with Probabilistic Building Exposure Models: An Application in Valparaíso, Chile Using Ancillary Open-Source Data and Parametric Ground Motions

“…The 1730 event with an inferred magnitude of Mw 9.1-9.3 is recognised as one of the largest earthquakes to occur in Chile in written history [52]. Another destructive earthquake with an inferred moment magnitude Mw 8.0-8.2 occurred in 1906 and caused significant damage [53]. Since then, 12 earthquakes with a macroseismic intensity greater than VII (Mercalli scale) have affected this area [54].…”

Bayesian Quantification and Reduction of Uncertainties in 3D Geomechanical‐Numerical Models

“…Data‐based weights for individual stress data records will be estimated which results in an improved significance of the modeled stress states. For such a task the Bayesian approach is common practice in other research fields (Gómez Zapata et al., 2022; Nawaz & Curtis, 2019). It allows a formal weighting, but to our knowledge, this has not been applied in forward geomechanical modeling of the 3D stress field.…”