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

Author(s): ORCID
ORCID
ORCID
Medium: journal article
Language(s): English
Published in: Buildings, , n. 9, v. 13
Page(s): 2245
DOI: 10.3390/buildings13092245
Abstract:

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.

Copyright: © 2023 by the authors; licensee MDPI, Basel, Switzerland.
License:

This creative work has been published under the Creative Commons Attribution 4.0 International (CC-BY 4.0) license which allows copying, and redistribution as well as adaptation of the original work provided appropriate credit is given to the original author and the conditions of the license are met.

  • About this
    data sheet
  • Reference-ID
    10740659
  • Published on:
    12/09/2023
  • Last updated on:
    14/09/2023
 
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