Earthquake Safety Assessment of Buildings through Rapid Visual Screening

  • Earthquake is among the most devastating natural disasters causing severe economical, environmental, and social destruction. Earthquake safety assessment and building hazard monitoring can highly contribute to urban sustainability through identification and insight into optimum materials and structures. While the vulnerability of structures mainly depends on the structural resistance, the safetyEarthquake is among the most devastating natural disasters causing severe economical, environmental, and social destruction. Earthquake safety assessment and building hazard monitoring can highly contribute to urban sustainability through identification and insight into optimum materials and structures. While the vulnerability of structures mainly depends on the structural resistance, the safety assessment of buildings can be highly challenging. In this paper, we consider the Rapid Visual Screening (RVS) method, which is a qualitative procedure for estimating structural scores for buildings suitable for medium- to high-seismic cases. This paper presents an overview of the common RVS methods, i.e., FEMA P-154, IITK-GGSDMA, and EMPI. To examine the accuracy and validation, a practical comparison is performed between their assessment and observed damage of reinforced concrete buildings from a street survey in the Bingöl region, Turkey, after the 1 May 2003 earthquake. The results demonstrate that the application of RVS methods for preliminary damage estimation is a vital tool. Furthermore, the comparative analysis showed that FEMA P-154 creates an assessment that overestimates damage states and is not economically viable, while EMPI and IITK-GGSDMA provide more accurate and practical estimation, respectively.show moreshow less

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Document Type:Article
Author: Ehsan HarirchianORCiD, Tom LahmerORCiDGND, Sreekanth Buddhiraju, Kifaytullah Mohammad, Amir MosaviORCiD
DOI (Cite-Link):https://doi.org/10.3390/buildings10030051Cite-Link
URN (Cite-Link):https://nbn-resolving.org/urn:nbn:de:gbv:wim2-20200331-41153Cite-Link
URL:https://www.mdpi.com/2075-5309/10/3/51
Parent Title (English):Buildings
Publisher:MDPI
Language:English
Date of Publication (online):2020/03/10
Date of first Publication:2020/03/10
Release Date:2020/03/31
Publishing Institution:Bauhaus-Universität Weimar
Institutes:Fakultät Bauingenieurwesen / Institut für Strukturmechanik
Volume:2020
Issue:Volume 10, Issue 3
Pagenumber:15
Tag:Machine learning; buildings; earthquake; earthquake safety assessment; extreme events; mitigation; natural hazard; rapid visual screening; seismic assessment
GND Keyword:Maschinelles Lernen; Erdbeben
Dewey Decimal Classification:600 Technik, Medizin, angewandte Wissenschaften
BKL-Classification:52 Maschinenbau, Energietechnik, Fertigungstechnik
Licence (German):License Logo Creative Commons 4.0 - Namensnennung (CC BY 4.0)