TY  - JOUR
A1  - Harirchian, Ehsan
A1  - Lahmer, Tom
A1  - Kumari, Vandana
A1  - Jadhav, Kirti
T1  - Application of Support Vector Machine Modeling for the Rapid Seismic Hazard Safety Evaluation of Existing Buildings
JF  - Energies
N2  - The economic losses from earthquakes tend to hit the national economy considerably; therefore, models that are capable of estimating the vulnerability and losses of future earthquakes are highly consequential for emergency planners with the purpose of risk mitigation. This demands a mass prioritization filtering of structures to identify vulnerable buildings for retrofitting purposes. The application of advanced structural analysis on each building to study the earthquake response is impractical due to complex calculations, long computational time, and exorbitant cost. This exhibits the need for a fast, reliable, and rapid method, commonly known as Rapid Visual Screening (RVS). The method serves as a preliminary screening platform, using an optimum number of seismic parameters of the structure and predefined output damage states. In this study, the efficacy of the Machine Learning (ML) application in damage prediction through a Support Vector Machine (SVM) model as the damage classification technique has been investigated. The developed model was trained and examined based on damage data from the 1999 Düzce Earthquake in Turkey, where the building’s data consists of 22 performance modifiers that have been implemented with supervised machine learning.
KW  - Erdbeben
KW  - Maschinelles Lernen
KW  - earthquake vulnerability assessment
KW  - rapid visual screening
KW  - machine learning
KW  - support vector machine
KW  - buildings
KW  - OA-Publikationsfonds2020
Y1  - 2020
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20200707-41915
UR  - https://www.mdpi.com/1996-1073/13/13/3340
VL  - 2020
IS  - volume 13, issue 13, 3340
PB  - MDPI
CY  - Basel
ER  - 
TY  - JOUR
A1  - Harirchian, Ehsan
A1  - Lahmer, Tom
A1  - Buddhiraju, Sreekanth
A1  - Mohammad, Kifaytullah
A1  - Mosavi, Amir
T1  - Earthquake Safety Assessment of Buildings through Rapid Visual Screening
JF  - Buildings
N2  - 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 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.
KW  - Maschinelles Lernen
KW  - Machine learning
KW  - Erdbeben
KW  - buildings
KW  - earthquake safety assessment
KW  - earthquake
KW  - extreme events
KW  - seismic assessment
KW  - natural hazard
KW  - mitigation
KW  - rapid visual screening
Y1  - 2020
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20200331-41153
UR  - https://www.mdpi.com/2075-5309/10/3/51
VL  - 2020
IS  - Volume 10, Issue 3
PB  - MDPI
ER  - 
TY  - THES
A1  - Hatahet, Tareq
T1  - On the Analysis of the Disproportionate Structural Collapse in RC Buildings
N2  - Increasing structural robustness is the goal which is of interest for structural engineering community. The partial collapse of RC buildings is subject of this dissertation.  Understanding the robustness of RC buildings will guide the development of safer structures against abnormal loading scenarios such as; explosions, earthquakes, fine, and/or long-term accumulation effects leading to deterioration or fatigue. Any of these may result in local immediate structural damage, that can propagate to the rest of the structure causing what is known by the disproportionate collapse.
This work handels collapse propagation through various analytical approaches which simplifies the mechanical description of damaged reinfoced concrete structures due to extreme acidental event.
T3  - ISM-Bericht // Institut für Strukturmechanik, Bauhaus-Universität Weimar - 2018,2 
KW  - Beton
KW  - disproportionate collapse
KW  - buildings
KW  - reinforced concrete
KW  - catenary action
KW  - compressive arching
KW  - dynamic amplifification
KW  - structural robustness
Y1  - 2018
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20180329-37405
ER  -