Rapid Urban-Scale Building Collapse Assessment Based on Nonlinear Dynamic Analysis and Earthquake Observations
Author(s): |
Mahnoosh Biglari
Hiroshi Kawase Iman Ashayeri |
---|---|
Medium: | journal article |
Language(s): | English |
Published in: | Buildings, 8 October 2024, n. 10, v. 14 |
Page(s): | 3321 |
DOI: | 10.3390/buildings14103321 |
Abstract: |
Rapid damage assessment after an earthquake is crucial for allocating and prioritizing emergency actions. Building damage due to an earthquake depends on the seismic hazard and the building’s strength. While it is now possible to promptly access acceleration data as seismic input through online strong motion networks in urban areas, good models are necessary to evaluate the damage in different zones of the affected area. This study aims to present a rapid method for such an urban-scale building collapse evaluation by conducting a nonlinear dynamic analysis of modeled buildings. Based on the Nagato and Kawase model, this study estimates the yield shear strength of 3-story steel buildings, 3-story reinforced concrete buildings, and 1-story masonry buildings in Sarpol-e-Zahab City after the 2017 Mw7.3 earthquake. The damage ratio is calculated through nonlinear dynamic analyses using estimated records from the main earthquake shock in different city zones. The research found that the seismic yield shear strength of steel and reinforced concrete buildings might be weaker than that of the Iranian seismic code’s standard value. Conversely, masonry-building resistance is stronger than the guidelines assumed. The constructed numerical models can be used for the rapid building damage assessment immediately after a damaging earthquake. |
Copyright: | © 2024 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. |
27.79 MB
- About this
data sheet - Reference-ID
10804599 - Published on:
10/11/2024 - Last updated on:
10/11/2024