Probabilistic framework for regional loss assessment due to earthquake-induced liquefaction including epistemic uncertainty

Yilmaz, C.; Silva, Vítor; Weatherill, Graeme

doi:10.1016/j.soildyn.2020.106493

Cited by 22 publications

(10 citation statements)

References 75 publications

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“…Bozzoni et al, 2020), thematic uncertainties related to visualisation and interpretation of risk metrics can arise if they are mapped over larger regional administrative units (e.g. Yilmaz et al, 2021) instead of being represented at more hazard-compliant resolutions (e.g. Bozzoni et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Variable-resolution building exposure modelling for earthquake and tsunami scenario-based risk assessment. An application case in Lima, Peru

Zapata¹,

Brinckmann²,

Harig³

et al. 2021

Preprint

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Abstract. We propose the use of variable resolution boundaries based on Central Voronoi Tessellations (CVT) to spatially aggregate building exposure models for risk assessment to various natural hazards. Such a framework is especially beneficial when the spatial distribution of the considered hazards present intensity measures with contrasting footprints and spatial correlations such as in coastal environments. This proposal avoids the incorrect assumption that a single intensity value from hazards with low spatial correlation (e.g. tsunami) are considered as representative within large sized geocells for physical vulnerability assessment, without, at the same time, increasing the complexity of the overall model. We present decoupled earthquake and tsunami scenario-based risk estimates for the residential building stock of Lima (Peru). We observe that earthquake loss models for far-field subduction sources are practically insensitive to the exposure resolution. Conversely, tsunami loss models and associated uncertainties depend on the spatial correlations of the hazard intensities as well as on the resolution of the exposure models. We observe that for the portfolio located in the coastal area exposed to both perils in Lima, the ground-shaking dominates the losses for lower magnitudes whilst the tsunami does for the larger ones. For the latter, two sets of existing empirical flow-depth fragility models are used, finding large differences in the losses. This study arises awareness about the uncertainties in the selection of fragility models and aggregations entities for exposure modelling and loss mapping.

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Section: Introductionmentioning

confidence: 99%

Variable-resolution building exposure modelling for earthquake and tsunami scenario-based risk assessment. An application case in Lima, Peru

Zapata¹,

Brinckmann²,

Harig³

et al. 2021

Preprint

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“…As an alternative to the approach presented in the work by Yilmaz et al (2021), the work presented herein uses an event’s LHMs derived from geospatial liquefaction models as LHMs to estimate expected losses directly using EFFs rather than incorporating deformation and expected DSs of individual infrastructure components and detailed regional inventories. Event-level liquefaction loss data and fragility functions are used to determine probabilities of exceeding loss levels based on event-level Htot and PopExp .…”

Section: Introductionmentioning

confidence: 99%

“…In this article, we use Htot and PopExp as LHMs (in place of IMs) to predict liquefaction-induced loss through EFFs for liquefaction. Yilmaz et al (2021) build a new tool for liquefaction loss estimation within the HAZUS framework using a geospatial liquefaction model (Zhu et al, 2015) and a simplified HAZUS deformation model applied to a detailed local building inventory. The article concludes by emphasizing the importance of improved liquefaction probability and displacement models at regional scale which do not require complex site parameterization and detailed building inventories.…”

Section: Introductionmentioning

confidence: 99%

National liquefaction loss database and event-level fragility functions

Chansky,

Baise,

Moaveni

2023

Earthquake Spectra

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Liquefaction can be a significant contributor to loss due to earthquakes as observed during the Canterbury earthquake sequence or the 1995 Kobe earthquake. Geospatial liquefaction models can be used to estimate liquefaction extent after an earthquake but do not estimate liquefaction damage or impact. This article presents a liquefaction loss database for the United States and event-level fragility functions (EFFs) using aggregate liquefaction hazard measures (LHMs) derived from geospatial liquefaction models. The liquefaction loss database for the United States is developed by sampling earthquakes with Magnitude > 5.0 between the years of 1964 and 2019 in the continental United States and Alaska. Within this sample of 42 earthquakes, 11 resulted in liquefaction loss. Estimates were characterized by the type of infrastructure (e.g. transportation, utilities, and buildings) and the subcategory (e.g. for buildings: residential, commercial, and public), and then loss was estimated using the 2018-equivalent US dollar amount. When possible, loss estimates were obtained directly from the literature. Within this sample of 42 earthquakes, 6 events resulted in estimated monetary losses from liquefaction damage greater than 1% of the total event loss, including one with liquefaction damage greater than 10%. Using estimates for aggregate liquefaction hazard and population exposure derived from geospatial liquefaction models as LHMs, EFFs are presented using cost-based damage state (DS) thresholds in the United States. The fragility functions also include confidence intervals representing the uncertainty in probabilities of exceeding DS thresholds. Aggregate liquefaction hazard was found to be a preferred LHM for liquefaction loss, especially when evaluating transportation and building loss. Aggregate population exposure was found to be a better LHM for utilities. In addition, a second set of EFFs is presented using an expanded international dataset and DSs which are assigned relative to overall earthquake damage rather than cost-based DSs.

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“…The effectiveness of the global geospatial liquefaction model proposed by Zhu et al [4] was further improved by Rashidian and Baise [5]. Yilmaz et al [6] performed a large-scale liquefaction risk assessment of Portugal, based on Zhu et al [2] approach, for estimating damage and economic losses within a probabilistic framework. A data-driven prediction model for liquefaction occurrence was developed by Bozzoni et al [7] with specific reference to continental Europe by using a set of optimal geospatial predictors.…”

Section: Introductionmentioning

confidence: 99%

A Geospatial Approach for Mapping the Earthquake-Induced Liquefaction Risk at the European Scale

et al. 2021

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This paper presents a geospatial methodology for zoning the earthquake-induced soil liquefaction risk at a continental scale and set-up in a Geographic Information System (GIS) environment by coupling data-driven and knowledge-driven approaches. It is worth mentioning that liquefaction is a phenomenon of soil instability occurring at a very local spatial scale; thus, the mega-zonation of liquefaction risk at a continental scale is a hard facing challenge. Since the risk from natural disasters is the convolution of hazard, vulnerability, and exposure, the liquefaction risk mapping is based on the combination of geospatial explanatory variables, available at the continental scale, of the previously listed three assumed independent random variables. First, by applying a prediction model calibrated for Europe, the probability of liquefaction is mapped for the whole continent. Then, the Analytical Hierarchy Process (AHP) is adopted to identify areas that have a high risk of liquefaction, taking into account proxy data for exposure. The maps are computed for different levels of severity of ground shaking specified by three return periods (i.e., 475, 975, and 2475 years). A broad variety of stakeholders would benefit from the outcomes of this study, such as civil protection organizations, insurance and re-insurance companies, and infrastructure operators.

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Probabilistic framework for regional loss assessment due to earthquake-induced liquefaction including epistemic uncertainty

Cited by 22 publications

References 75 publications

Variable-resolution building exposure modelling for earthquake and tsunami scenario-based risk assessment. An application case in Lima, Peru

Variable-resolution building exposure modelling for earthquake and tsunami scenario-based risk assessment. An application case in Lima, Peru

National liquefaction loss database and event-level fragility functions

A Geospatial Approach for Mapping the Earthquake-Induced Liquefaction Risk at the European Scale

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