Seismicity of East Anatolian of Turkey and Failures of Infill Walls Induced by Major Earthquakes

Doğangün, Adem; Yön, Burak; Onat, Onur; Öncü, Mehmet Emin; Sağıroğlu, Serkan

doi:10.1142/s1793431121500172

Cited by 24 publications

(9 citation statements)

References 15 publications

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“…Lastly, we produced seismic maps based on the estimated earthquake magnitude. Figure [10] is a flowchart that details the complete procedure, from data collection through model evaluation, to give an in-depth understanding of our technique. Our approach has the potential to enhance the evaluation of earthquake susceptibility and provide better disaster management solutions.…”

Section: Methodsmentioning

confidence: 99%

“…1 https://www.emsc-csem.org/Earthquake/earthquake.php?id=1218444 2 U.S. Geological Survey, The M7.8 and M7.5 Kahramanmaraş Earthquake Sequence struck near Nurdagı, Turkey (Türkiye) on February 6, 2023, at URL https://www.usgs.gov/news/featured-story/m78-and-m75-kahramanmaras-earthquake-sequence-near-nurdagi-turkey-turkiye The East Anatolian Fault (EAF), a significant active left-lateral strike-slip fault in Turkey, experienced seismic activity in the 19th century [13,10]. Seismically, it is currently active once more.…”

Section: Introductionmentioning

confidence: 99%

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Prediction of earthquake magnitude and seismic vulnerability mapping using artificial intelligence techniques: a case study of Turkey

Biswas

Kumar

Bera

2023

Preprint

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Earthquake threats can result in fatalities, property destruction, and other cascading effects. Since it is nearly impossible to prevent earthquakes, anticipating the location of future earthquakes and figuring out their likelihood could be very helpful in reducing the seismic threat. In this work, seismic hazard prediction is executed to forecast adverse results using a range of potential artificial intelligence (AI) techniques, including ML and ANN. In the case study, we have looked at Turkey, which was recently and badly damaged by two earthquakes in February 2023. To predict earthquake magnitude, this study used a variety of regression algorithms, including Decision Tree Regressor, Extra-Trees Regressor, Random Forest Regressor, Bayesian Ridge Regressor, and advanced gradient boosting decision tree (GBDT) algorithms such as XGBoost, LightGBM, and CatBoost, as well as three artificial neural networks (ANN). The predicted magnitude and risk zone of an earthquake are mapped using a geographic information system (GIS), and the maps performed well in terms of prediction. The generated maps is showing the expected earthquake risk based on historical data using the statistical computations. The ANN models perform exceptionally well, with R2 scores of 0.99 and 0.98 for training and case study data, respectively, and low values for MSE, MAE, and RMSE. ML models have demonstrated an exceptional ability to properly generalize from a single dataset, which implies they can accurately anticipates results for new and untested data. The results would be helpful to many local emergency preparedness and infrastructure planning organizations.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Prediction of earthquake magnitude and seismic vulnerability mapping using artificial intelligence techniques: a case study of Turkey

Biswas

Kumar

Bera

2023

Preprint

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“…One of the most important effects of infill walls is that they increase the horizontal load carrying capacity of a structure. However, it must be stated that the contribution of infill walls to the rigidity of a structure is suddenly reduced when they become cracked or damaged (Yön et al 2017 ; Onat et al 2018 ; Braga et al 2011 ; Onat 2019 ; Doğangün 2021 ; Onat and Panto 2021 ).…”

Section: Damage Assessment Of Reinforced Concrete Buildingsmentioning

confidence: 99%

“…Currently, Turkey, which is in the East Mediterranean, is one of the most active seismic zones in the world. This high seismicity results in numerous earthquakes (Calayır et al 2012 ; Sayın et al 2013 ; Sayın et al 2014 ; Yön et al 2019 ; Yön et al 2020 ; Doğangün et al 2021 ; Günaydın et al 2021 ; Demir et al, 2020 ; Sayın et al 2021 ; Doğan et al 2021 ; Yön 2021 ) even in the previous year, 2020. This seismicity is caused by 326 separate faults, fault zones or combined systems.…”

Section: Introductionmentioning

confidence: 99%

Field reconnaissance and structural assessment of the October 30, 2020, Samos, Aegean Sea earthquake: an example of severe damage due to the basin effect

et al. 2022

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An earthquake with a magnitude ranging from Mw = 6.9 (KOERI) to Mw = 7.0 (USGS) struck Samos Island in the Aegean Sea on October 30, 2020, with an epicentre 70 kms from the İzmir city centre in Turkey. The earthquake took place at 14:51 local time (11:51 UTC). The peak ground acceleration (PGA) of this earthquake was recorded to be 0.179 g at the epicentre of the earthquake. This earthquake occurred at a depth of 17.26 km (AFAD (2020) İzmir Earthquake Report, (In Turkish)) and lasted 16 s. The main shock from the earthquake triggered a tsunami that hit the building stocks built near the coast. During the gradual deregulation of COVID-19 pandemic regulations, various events caused considerable damage to the building stock, particularly in the Izmir Seferihisar and Bayraklı regions and resulted in a massive disruption of daily habits. The main shock caused 117 deaths in both Turkey and Greece, and 1632 people were also injured in Turkey. Moreover, several injuries occurred in Greece. A total of 103 buildings collapsed, 700 were severely damaged, 814 buildings were moderately damaged, and 7889 were slightly damaged. The basic aim of this paper is to briefly present the past and present seismotectonic characteristics of the region, present building stock, and former structural conditions before the earthquake, assess structural performance and classify distinguished earthquake-induced failures and damage due to the basin effect.

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“…The structural element in this hinge model is divided into three types of fibers: one type of fiber is used for modeling longitudinal steel reinforcing rods; the second type of fiber is used to define nonlinear behavior of confined concrete, which includes core concrete; and the third type of fiber is defined for unconfined concrete, which includes cover concrete. Also, the stress/strain diagram is determined for the nonlinearity by using constitutive laws for each fiber element like rebar, confined concrete and cover concrete, according to defined materials [30][31][32][33][34][35][36]. Fig 3 shows typical fiber modelling for a rectangular reinforced concrete section.…”

Section: Numerical Models and Materials Lawsmentioning

confidence: 99%

A novel inter-story drift limit proposal for TBEC2018 and fragility prognosis with TSC2007

Onat¹,

Yön²

2021

JSEAM

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The basic purpose of this paper is to investigate and propose a novel inter-story drift limits for the current Turkish Seismic Code to get easy structural assessment by using software. For this aim, numerical analysis was performed by modeling two types of RC frame structures. One of them is 5 stories, the other of them is 7 stories. Two different concrete classes, C20 and C25, were considered and three tension reinforcement ratios were considered for analysis. Tension reinforcement ratios were determined by half of the compressive reinforcement, equal to compressive reinforcement and double the compressive reinforcement ratio. Incremental dynamic analyses (IDA) were performed on these buildings. In this study to execute IDA, eleven seismic acceleration benchmark records were multiplied with various scaling factors from 0.2 to 1.0. Maximum base shear and corresponding roof displacement responses obtained from IDA curves were generated according to these responses. IDA curves were compared with each other by deriving fragility graphs. According to results, proposed limits for the current Turkish seismic code (TBEC-2018) provide, 0.6%, 2.4% and 3.3% respectively for MN, GV and GC, rather safe limits compared to drift limits presented in the foredate seismic code (TSC-2007).

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Seismicity of East Anatolian of Turkey and Failures of Infill Walls Induced by Major Earthquakes

Cited by 24 publications

References 15 publications

Prediction of earthquake magnitude and seismic vulnerability mapping using artificial intelligence techniques: a case study of Turkey

Prediction of earthquake magnitude and seismic vulnerability mapping using artificial intelligence techniques: a case study of Turkey

Field reconnaissance and structural assessment of the October 30, 2020, Samos, Aegean Sea earthquake: an example of severe damage due to the basin effect

A novel inter-story drift limit proposal for TBEC2018 and fragility prognosis with TSC2007

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