Comparison of national and local building inventories for damage and loss modeling of seismic and tsunami hazards: From parcel-to city-scale

Sanderson, Dylan R.; Cox, Daniel T.

doi:10.1016/j.ijdrr.2023.103755

Cited by 4 publications

(1 citation statement)

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“…Senouci et al [12] explored different building aggregation options and their effects on the uncertainties of event loss estimates. Sanderson and Cox [13] compared the losses estimated using national and local building inventories and found that using the national inventory underpredicted the losses aggregated at the city level. Some studies have focused on the feasibility of developing fragility and vulnerability models using limited data.…”

Section: Introductionmentioning

confidence: 99%

Bias in the Estimation of Seismic Risk for Municipal Building Stocks Due to Limited Data

Babič,

Žižmond,

Dolšek

2023

Buildings

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This study investigated the effect of the building data knowledge level on seismic risk estimation for municipal building stocks, focusing on identifying the characteristics that influence loss estimation bias. Fifteen municipalities in two Slovenian regions were analysed using twelve building data knowledge levels, defined by combining different knowledge levels about building location and floor area. The knowledge levels ranged from those using data aggregated at the municipality level to those using building-specific data. The bias was quantified as the log residual between the expected annual losses estimated for the given knowledge level and the base-case level, characterised by building-specific data. The results indicate that loss estimation bias is affected by both the building location and floor-area knowledge levels. The data on building density distribution across the municipality and building-class-specific floor areas are sufficient for estimating loss with low bias with respect to the base-case level. The effect of potential data improvement on bias reduction can be assessed using building stock homogeneity and hazard variation indexes determined from readily available data. Further research is needed to explore loss estimation bias for building data knowledge levels not considered in this study and generalise the concepts to other regions and building classifications.

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Section: Introductionmentioning

confidence: 99%