Sürdürülebilir Kalkınma Amaçları

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• MakaleAçık Erişim2025Scopus SJRQ1/r/munzur/1950
  Evaluation of the seismic response of reinforced concrete buildings in the light of lessons learned from the February 6, 2023, Kahramanmaraş, Türkiye earthquake sequences

  Yön, Burak; Dedeoğlu, İbrahim Özgür; Yetkin, Musa; Erkek, Hakan; Calayır, Yusuf

  On 6 February 2023, two significant seismic events occurred in the southeastern region of Türkiye. The seismic activity, which was perceptible in numerous countries beyond Türkiye, resulted in a considerable number of fatalities. A considerable number of individuals lost their lives and were rendered homeless as a result of the disaster. Two of the most significant factors contributing to the occurrence of these tragedies are the magnitude of the earthquake and structural deficiencies. The present study is concerned with a detailed analysis of these two factors. This study initially considers the seismicity of the region where the earthquakes that occurred on 6 February 2023 took place, as well as the seismic characteristics of these earthquakes. Subsequently, the findings of the field studies conducted in Hatay, Adıyaman, Kahramanmaraş and Malatya, the cities where the earthquakes caused the most destruction, are presented. The objective of the field study is to ascertain the collapse patterns, structural damages and the factors influencing these damages in reinforced concrete structures in the region. The primary causes of damage to structures can be attributed to several factors, including the presence of a strong beam-weak column mechanism, the soft story-weak story mechanism, the pounding effect, the short column damage, the long cantilever and overhangs, the short beam damage, the buckling damage, the torsion effect, the quality of the materials, the insufficient transverse reinforcement, the compressive failure due to over-reinforcement, the corrosion effect, the damage to reinforced concrete shear walls, the infill wall damage, and the damage caused by the soil and foundation system. These causes have been evaluated and recommendations have been formulated to prevent structural damage.

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  AtıfScopus55·WoS50·OpenAlex61

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• MakaleAçık Erişim2025Scopus SJRQ1/r/munzur/793
  Investigating forest fire causes through an integrated Bayesian network and geographic information system approach

  Konurhan, Zekeriya; Yucesan, Melih; Gul, Muhammet

  Forests are among the most critical natural resources worldwide and are essential for a sustainable ecosystem. In recent years, climate change and human activities have increased their impact on forests, and forest fires-especially in summer- have caused significant forest losses globally. This paper integrates Bayesian Network (BN) and Geographic Information Systems (GIS) to pinpoint the possible causes of forest fires and analyze their complex interactions. The study used data from 1465 fires between 2017 and 2022 in Muğla province in southwestern Türkiye. Within the scope of the study, 11 variables were chosen, such as elevation, slope, aspect, wind speed, population density, and road network, to build a BN model that combines physical and human geographical features. These variables overlapped using the pairwise intersect tool from ArcGIS Pro during the BN model setup, and probability values were calculated. The overall probability of fire in the BN model was determined to be 0.81, with probabilities ranging from 0.81 to 0.56 in low-altitude, moderately sloped, and south-facing areas. Scenario analyses examined fire risk under different conditions, highlighting the most influential variables for fire prevention efforts. The study identified fire-prone areas as spatial data, revealing that densely forested coastal and certain inland regions are at higher risk, whereas bare high-altitude areas with steep slopes pose lower risk. The BN model can be further enhanced by incorporating additional variables, making it a valuable tool for future fire risk assessment and mitigation strategies research.

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  AtıfScopus5·WoS5·OpenAlex7

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• MakaleAçık Erişim2022Scopus SJRQ1/r/munzur/2998
  A Bayesian BWM and VIKOR-based model for assessing hospital preparedness in the face of disasters

  Saner, Halit Serdar; Yucesan, Melih; Gul, Muhammet

  Hospitals are the first point of contact for people in the face of disasters that interfere with the daily functioning of life and endanger health and social life. All preparations should be made considering the worst possible conditions and the provided service should continue without interruption. In this study, a multi-criteria decision-making model was developed to evaluate disaster preparedness of hospitals. This decision model includes Bayesian best–worst method (BBWM), the VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) and technique for order preference by similarity to ideal solution (TOPSIS) methods. With the proposed decision model, six main criteria and 34 sub-criteria related to disaster preparedness of hospitals were considered. The criteria and sub-criteria evaluated in pairwise comparison manner by the experts were weighted with BBWM. These weight values and the data obtained from the six Turkish hospitals were combined to provide inputs for VIKOR and TOPSIS. In addition, a comparative study and sensitivity analysis were carried out using weight vectors obtained by different tools. BBWM application results show that the “Personnel” criterion was determined as the most important criterion with an importance value of 26%. This criterion is followed by “Equipment” with 25%, “Transportation” with 14%, “Hospital building” and “Communication” with 12%, and “Flexibility” with 11%. Hospital-2 was determined as the most prepared hospital for disasters as a result of VIKOR application. The VIKOR Q value of this hospital was obtained as 0.000. According to the results of the comparative study, Hospital-2 was determined as the most disaster-ready hospital in all six different scenarios. This hospital is followed by Hospital-4 (Q = 0.5661) and Hospital-5 (Q = 0.7464). The remaining rankings were Hospital-6, Hospital-3 and Hospital-1. The solidity of the results was checked with TOPSIS. Based on TOPSIS application results, Hospital-2 was again found the most-ready hospital. The usage of BBWM in this study enabled the expert group’s views to be combined without loss of information and to determine the criteria and sub-criteria weights with less pairwise comparisons in a probabilistic perspective. Via the “Credal ranking”, which is the contribution of BBWM to the literature, the interpretation of the hierarchy between each criterion has been performed more precisely.

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  AtıfScopus48·WoS37·OpenAlex51

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• MakaleAçık Erişim2022Scopus SJRQ1/r/munzur/2433
  Field reconnaissance and structural assessment of the October 30, 2020, Samos, Aegean Sea earthquake: an example of severe damage due to the basin effect

  Onat, Onur; Yön, Burak; Öncü, Mehmet Emin; Varolgüneş, Sadık; Karaşin, Abdulhalim; Cemalgil, Selim

  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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  AtıfScopus30·WoS28·OpenAlex29

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• MakaleYalnız künye2016Scopus SJRQ1/r/munzur/4116
  An artificial neural network-based earthquake casualty estimation model for Istanbul city

  Gul, Muhammet; Guneri, Ali Fuat

  Earthquakes over the world cause serious damages for people and the economy. The establishment of an appropriate model to estimate potential losses or injuries in earthquake disasters is fundamental to decrease their impacts and losses and effectively respond and mitigate. Turkey as a country that experienced many earthquakes in the last century and had serious human and financial losses needs a comprehensive knowledge of consequences of devastating earthquakes to be able to plan for the future. Artificial neural networks (ANNs) have abilities to solve and analyse complex relations as an appropriate method to estimate number of injuries. In this study, an ANN model is built up for the earthquake casualty prediction, which takes earthquake occurrence time, earthquake magnitude, and population density as the predictors and employs 21 Mw > 5 earthquake disasters occurred in Turkey from 1975 as samples for the training of the network. The model was then tested on a study region consisting of four districts in Istanbul which is estimated to have the highest injury rate according to the earlier reports and generated estimations of the expected number of injured people. Results show that 99.9 % of the variability in the number of injured people is predictable with using this ANN model. Comparison of actual values and estimated output values in the ANN model was also found apparently very close to each other. According to the test case study results, when the value of earthquake magnitude is 6.5 Mw, number of injured people has increased in a sharp trend compared to the previous magnitude value (6 Mw) for all districts. Estimated number of injured people in daytime is obtained higher than at night when the earthquake magnitude is 5 and 5.5 Mw. The highest value in estimated number of injured people has emerged in Fatih district as 7241 for a 7.5 Mw daytime earthquake. On conclusion, it was deduced that the model can reveal accurate estimation of casualties and it can provide information in order to develop mitigation policies, especially in earthquake emergency service management.

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  AtıfWoS32·OpenAlex46

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