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Statistical analysis and modeling to examine the exterior and interior building damage pertaining to the 2016 Kumamoto earthquake

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Medium: Fachartikel
Sprache(n): Englisch
Veröffentlicht in: Earthquake Spectra, , n. 1, v. 38
Seite(n): 310-330
DOI: 10.1177/87552930211035408
Abstrakt:

After an earthquake occurs, field surveys are conducted by relevant authorities to assess the damage suffered by buildings. The field survey is essential as it ensures the safety of residents and provides the necessary information to local authorities for post-disaster recovery. In Japan, a primary (mandatory) exterior survey is conducted first, and a secondary (voluntary) interior survey is performed subsequently if the residents request a reinvestigation. However, a major challenge associated with field surveys is the substantial time cost of determining the damage grades. Moreover, an interior survey is performed only after receiving the reinvestigation request from occupants, which further delays the decision-making process. In addition, the risk of incorrect damage estimation during the exterior survey must be considered because underestimating the damage can endanger the residents. Therefore, in this study, a three-part analysis (Parts I–III), where each part corresponds to a distinct stage of the standard damage assessment procedure, was performed to characterize the relationship between the building parameters and damage grades at different stages. To further explore the possibility of accelerating decision-making, predictive modeling was performed in each part. The Part I results indicate that estimating the final damage grade for all buildings immediately after the exterior survey is similar to treating the exterior survey results as the final ones. The Part II results show that buildings that potentially require an interior survey can be predicted with reasonable accuracy after the exterior survey. In buildings for which reinvestigations have been requested, Part III demonstrates that the risk of underestimation in the exterior survey can be predicted reliably.

Structurae kann Ihnen derzeit diese Veröffentlichung nicht im Volltext zur Verfügung stellen. Der Volltext ist beim Verlag erhältlich über die DOI: 10.1177/87552930211035408.
  • Über diese
    Datenseite
  • Reference-ID
    10672451
  • Veröffentlicht am:
    12.06.2022
  • Geändert am:
    12.06.2022
 
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