Prediction of local site influence on seismic vulnerability using machine learning: A study of the 6 February 2023 Türkiye earthquakes

Senkaya, Mustafa; Silahtar, Ali; Erkan, Enes Furkan; Karaaslan, Hasan

doi:10.1016/j.enggeo.2024.107605

Engineering Geology

2024

DOI: 10.1016/j.enggeo.2024.107605

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Prediction of local site influence on seismic vulnerability using machine learning: A study of the 6 February 2023 Türkiye earthquakes

Mustafa Senkaya,

Ali Silahtar,

Enes Furkan Erkan

et al.

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2024

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Cited by 3 publications

References 74 publications

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Preface

Gokceoglu,

Onder Cetin,

Kayen

et al. 2024

Engineering Geology

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Preface

Gokceoglu,

Onder Cetin,

Kayen

et al. 2024

Engineering Geology

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Machine learning (ML) algorithms for seismic vulnerability assessment of school buildings in high-intensity seismic zones

Zain,

Dackermann,

Prasittisopin

2024

Structures

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Study on Effectiveness of Regional Risk Prioritisation in Reinforced Concrete Structures after Earthquakes

Işık,

Hadzima-Nyarko,

Radu

et al. 2024

Applied Sciences

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Depending on the characteristics of the existing buildings, earthquakes can cause damage at different levels and have a significant impact on the environment. The structural damages after the earthquakes have shown the importance of analysing both the existing and the damaged buildings. In this study, the Turkish rapid seismic assessment method, which was used for the existing building stock before a possible earthquake, was applied to the damaged reinforced concrete (RC) buildings after the 6 February earthquakes in Kahramanmaraş (Türkiye). The building data were used as a result of field observations in the provinces of Adıyaman, Hatay, and Kahramanmaraş, where the greatest destruction was caused by these earthquakes. Five RC buildings from each province were considered. The rapid assessment method was applied to a total of 15 buildings with different levels of damage. For this purpose, pre-earthquake images of the buildings were obtained, and an earthquake performance score was obtained for each building, taking into account the sustained damage during the earthquake. The primary aim of this study is to show the effects of structural irregularities on earthquake behaviour and to demonstrate the applicability of the rapid assessment methods used before the earthquake. The results obtained clearly demonstrate the effectiveness of rapid evaluation methods for existing building stock. Structural analyses were also carried out in this study to address the fact that the height of the ground storey is higher than the other storeys, which is one of the factors leading to a soft storey.

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Prediction of local site influence on seismic vulnerability using machine learning: A study of the 6 February 2023 Türkiye earthquakes

Cited by 3 publications

References 74 publications

Preface

Preface

Machine learning (ML) algorithms for seismic vulnerability assessment of school buildings in high-intensity seismic zones

Study on Effectiveness of Regional Risk Prioritisation in Reinforced Concrete Structures after Earthquakes

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