Sürdürülebilir Kalkınma Amaçları

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Yazarlar

• Kaya Yildiz, Cagla Melisa(1)
• Kiymis, Ibrahim(1)
• Reis, Hatice Catal(1)
• Tatli, Ramazan(1)
• Turk, Veysel(1)
• Ustuner, Mustafa(1)
• Yuce, Damla Nur(1)

• MakaleAçık Erişim2025/r/gumushane/14116
  Post-seismic structural assessment: advanced crack detection through complex feature extraction using pre-trained deep learning and machine learning integration

  Reis, Hatice Catal; Turk, Veysel; Ustuner, Mustafa; Kaya Yildiz, Cagla Melisa; Tatli, Ramazan

  Earthquakes can often cause significant damage to buildings. After an earthquake, experts/managers need to make quick and accurate damage assessments. Traditionally, manual analysis processes have been widely used in damage assessment studies. The fact that these methods are time-consuming and based on human observation leads to certain limitations in damage assessment studies. In recent years, artificial intelligence techniques such as deep learning and machine learning have frequently been preferred in damage detection studies, and significant success has been achieved. This study aimed to automatically detect cracks/damages in the buildings in Diyarbakir city after the February 6, 2023 Kahramanmaras, Turkey earthquake. Our experimental dataset was collected by the researchers and named Kahramanmaras-Diyarbakir Earthquake Building Crack Dataset (KDBECD-2023). The data set consists of four categories in terms of damage level: undamaged, slightly damaged, moderately damaged, and heavily damaged buildings. DenseNet201 deep learning architecture and popular machine learning algorithms, Support Vector Machine, Random Forest, Decision Tree, and K-Nearest Neighbors (KNN) were used to classify cracks at different damage levels. In the experimental phase, feature extraction was performed with the DenseNet201 architecture. In addition, dimensional reduction was applied with the Principal Component Analysis method to reduce the computational complexity of the proposed hybrid study. According to the experimental results, the DenseNet201-KNN hybrid model gave the most successful result with an accuracy value of 94.62%. The results of this study can make important contributions to decision makers and experts in detecting cracks and damages in buildings after an earthquake.

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• MakaleAçık Erişim2025/r/gumushane/13958
  The effect of disaster resilience and trauma exposure on PTSD, depression, and sleep disorder among healthcare workers involved in the Kahramanmaraş Earthquakes (2023): a structural equation model

  Kiymis, Ibrahim; Yuce, Damla Nur

  Healthcare workers (HCWs) serve as the cornerstone of health services, which are among the primary needs during disasters. The chaotic environment caused by disasters can lead to mental health disorders in HCWs, similar to those experienced by disaster victims. Experiencing mental health disorders can hinder HCW’s professional approach to intervention. HCWs should not be overlooked for the possibility of experiencing mental health disorders while providing healthcare services during disasters. Therefore, this study aims to examine the impact of psychological resilience and trauma exposure on PTSD, depression, and sleep disorders among HCWs involved in the Kahramanmaraş Earthquakes, which were Turkey’s most devastating earthquakes. In this quantitative research, a survey technique was employed, reaching 642 hCWs involved in the Kahramanmaraş Earthquakes. Structural Equation Modeling (SEM) was used to test the impact of variables on each other. According to the SEM results, trauma exposure in the HCWs had a significant and positive effect on PTSD (β=+0.899, p = 0.000), depression (β=+0.685, p = 0.000), and sleep disorders (β=+0.603, p = 0.000). Psychological resilience had a significant and negative effect on PTSD (β=−0.278, p = 0.004) and depression (β=−0.322, p = 0.008). Surprisingly, psychological resilience had a significant and positive effect on sleep disorders (β=+0.692, p = 0.000). In conclusion, while trauma exposure led to PTSD, depression, and sleep disorders in the HCWs, psychological resilience mitigated PTSD and depression. Unexpectedly, psychological resilience increased sleep disorders. For this reason, it is recommended that future studies investigate in detail the reasons why HCWs experience sleep disorders and examine them in depth.

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