Araştırmacılar(54)

• Akoğuz, Harun
• Alperen Bulut, H.
• Aslay, Semi Emrah
• Atlı, Sefa
• Baş, Fatih İrfan
• Bayata, Halim Ferit
• Bayçinar, Metin
• Cosgun, Suleyman Istemihan
• Çakı, Mustafa
• Çelik, Alper
• Çolak, Muhammed Ali
• Çolak, Muhammet Ali
• Dalkilic, H. Yildirim
• Demircioğlu, Muhammed Sami
• Demiriz, Ahmet Oğuz
• Deniz, Fatih Ahmet
• Dogru, E.
• Ergun, Ugurcan
• Ertuc, İbrahim
• Ertugay, Neşe
• Isik, Adem
• Isik, Adem
• Işık, Suna
• İnce, Elif Gökçe
• Kandil, Ufuk
• Karimi, A.
• Katipog, Okan Mert
• Katipoğlu, Okan Mert
• Keleş, Ömer Faruk
• Kohistani, R.
• Korkmaz, Emre
• Kucuk, Mesut
• Kumbasaroglu, Atila
• Kurt, Zeynep Ö.
• Mengi, Gamze Şeyda
• Mermit, M.F.
• Oz, Mehmet Furkan
• Ozturk, Akif Berke
• Öllükçü, Meva Nur
• Özkal, Fatih Mehmet
• Öztürk, Muhammed Orhan
• Polat, Mehmet
• Polat, Melih Sakir
• Şimşek, Mutlu
• Terzioğlu, Zeynep Özge
• Turan, Ahmet İhsan
• Ulusu, İsmet
• Unal, Goktug
• Yağan, Mahmud
• Yalciner, Hakan
• Yalciner, Kürşad
• Yeşilyurt, S. Nur
• Zirek, Zühal
• Zorlu, Elif

• ArticleYayıncıda açık erişim2026/r/ebyu/19297
  2023 Kahramanmaraş Depremleri sonrasında Malatya’daki yığma binaların sismik performansı: Saha incelemesi ve nümerik analizler

  Turan, Ahmet Ihsan; Celik, Alper

  Türkiye, aktif deprem kuşağında yer almakta olup tarih boyunca yıkıcı depremler yaşamıştır. 6 Şubat 2023 Pazarcık (Mw 7.7) ve Elbistan (Mw 7.6) depremleri, özellikle bölgede yaygın olan mühendislik hizmeti almamış yığma yapılarda büyük hasara yol açmıştır. Bu çalışma, Malatya ilindeki yığma yapı stokunda gözlenen hasar türlerini incelemekte ve saha gözlemlerine dayanarak bu hasarların olası nedenlerini tartışmaktadır. Nitel bulguları desteklemek amacıyla, temsilî az katlı yığma yapı prototiplerinin deprem davranışını karşılaştırmalı olarak değerlendiren sayısal analizler gerçekleştirilmiştir. Doğrusal-elastik yanıt spektrumu analizleri ve modal esaslı yaklaşım kullanılarak iki ana parametre dikkate alınmıştır: taşıyıcı duvar malzemesi türü (dolu kil tuğla, boşluklu kil tuğla ve gazbeton) ve kat sayısı (1–3). Saha gözlemleri, yapı stokunun önemli bir bölümünün yeterli mühendislik hizmeti olmadan inşa edildiğine işaret etmekte; ayrıca katlar arasında farklı yığma birimlerinin kullanılması gibi düzensiz uygulamaların kritik zafiyetlere yol açabildiğini göstermektedir. Sayısal sonuçlar da rijitlik ve deplasman taleplerinin hem yığma birimi türüne hem de yapı yüksekliğine güçlü biçimde bağlı olduğunu ortaya koymakta ve özellikle daha hafif yığma sistemlerde kat sayısı arttıkça daha olumsuz talep eğilimlerinin gelişebileceğine işaret etmektedir.

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• ArticleYayıncıda açık erişim2026/r/ebyu/19210
  Erzincan Ham Perlit Agregası Kullanılarak Taşıyıcı Hafif Beton Üretilebilirliğinin Araştırılması

  Gül, Rüstem; Ulusu, İsmet

  Maksimum agrega tane çapı 12.5 mm olan ve granülometrisi Fuller denklemine uygun olan ham perlit agregası kullanılarak taşıyıcı hafif beton üretimi üzerine bir çalışma gerçekleştirilmiştir. Bu amaçla; 150, 200, 250, 300 ve 350 kg/m³ olmak üzere beş farklı çimento dozajında ve iki farklı çökme (slump) değerinde beton numuneleri hazırlanmıştır. Taze betonun işlenebilirliğini düzenlemek amacıyla, tüm karışımlara çimento ağırlığının %1’i oranında akışkanlaştırıcı katkı ilave edilmiştir. Taze ve sertleşmiş betonun fiziksel ve mekanik özellikleri ASTM standartlarına göre belirlenmiştir. Araştırma sonuçlarına göre; perlit agregası kullanılarak 1600 ile 1900 kg/m³ birim hacim ağırlığı arasında taşıyıcı hafif beton üretiminin mümkün olduğu görülmüştür. En yüksek basınç dayanımı, 350 kg/m³ bağlayıcı miktarıyla 90 gün sonunda 35 MPa olarak elde edilmiştir. Araştırma sonuçlarına göre; betonun mekanik ve fiziksel özellikleri üzerinde en etkili iki parametre çökme değeri ile bağlayıcı miktarıdır. Üretilen betonların yarmada çekme dayanımı, basınç dayanımının %11’i olarak belirlenmiş ve su emme oranı %8 olarak tespit edilmiştir.

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• ArticleYalnız künye2026/r/ebyu/11491
  Air quality index modeling using ensemble and data-intelligence modeling

  Pande, Chaitanya Baliram; Katipoğlu, Okan Mert; Radwan, Neyara; Sidek, Lariyah Mohd; Pal, Subodh Chandra; Yaseen, Zaher Mundher; Ali, Tarig; Chakrabortty, Rabin; Refadah, Samyah Salem; Khan, Mohd Yawar Ali

  Urban population growths, expansion of the industrial economy, burning stubble, and other major sources have significantly contributed to the increasing levels of air pollution in urban areas. Therefore, accurate prediction of the Air Quality Index (AQI) is essential for effective monitoring and planning within cities. In this research, an ensemble machine learning (ML) called Gradient Boosting (GB) model with 5- and 10-folds’ cross-validation was developed to achieve more precise and operative prediction outcomes based on important air quality related input variables. The results demonstrated that the suggested hybrid model attained the best accuracy in comparison with other benchmark ML models for predicting AQI values of Delhi city. Statistically, GB model reported second best model based on statistical metrics (R2 = 0.96, R = 0.98, RMSE = 23.03, MSE = 530.71, MAE = 15.16 and MARE = 11.96). Whereas Light Gradient Boosting Machine (LightGBM) model third best accuracy reported (R2 = 0.96, R = 0.98, RMSE = 23.21, MSE = 538.74, MAE = 14.68, and MARE = 11.33). The AQI prediction values are presented as spatial thematic maps for better understanding, highlighting city areas with high AQI levels to support air quality planning. The daily AQI prediction maps were prepared using various ML models. Further, the current research forecasted five days of AQI values and compared with the observed values. The results of five-day forecasting of GB model again indicated the best match with observed data of AQI values. Finally, forecasting accuracy results of GB (Boosting) models was attained 90% match with the observed values of AQI as compared with hybrid model. In conclusion, the reported results provided better understanding of AQI modeling for values and classes of spatial maps.

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• ArticleAçık Erişim2026/r/ebyu/11464
  Hybrid VMD-EMD Enhanced Machine Learning Models for Daily Reference Evapotranspiration Forecasting

  Acar, Reşat; Katipoğlu, Okan Mert; Çırağ, Burak

  Getting accurate daily estimates of reference evapotranspiration (ET₀) is essential for hydrological modeling, irrigation planning, and climate research. This study evaluated the performance of four machine learning models: Adaptive Boosting (AdaBoost), Categorical Boosting (CatBoost), Deep Neural Network (DNN), and Light Gradient Boosting Machine (LGBM) for daily ET₀ prediction. Feature selection was implemented using Partial Autocorrelation (PACF) and Autocorrelation (ACF) analysis on the basis of an 8-day lag structure, while the CatBoost feature importance framework was implemented to identify the most important inputs. Moreover, the methodologies of decomposition-based preprocessing, Empirical Mode Decomposition (EMD) and Variational Mode Decomposition (VMD) were implemented on the lagged inputs to build the hybrid models augmented with intrinsic constituents. These augmented hybrid models were created to improve the data representation as well as predictive capability. Model performance was evaluated using key statistical metrics, including RMSE, MAE, NSE, KGE, R2, and bias-related indicators. CatBoost produced the lowest RMSE (1.57 mm/day) and MAE (1.13 mm/day), and the highest NSE (0.67), KGE (0.78), and R2 (0.67), along with the lowest AIC score (627.15), indicating strong and reliable predictive performance. Furthermore, the VMD based CatBoost model yielded even greater improvements, highlighting the added value of decomposition enhanced learning. On the contrary, DNN showed the weakest results, with the highest RMSE (2.39 mm/day) and lowest NSE (0.23). These findings suggest that CatBoost and its hybrid variants are highly effective and reliable tools for daily ET₀ prediction, particularly in data limited environments and mountainous regions.

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• ArticleAçık Erişim2026/r/ebyu/11418
  Identifying Agricultural Crops with Similar Spectral Properties Using Machine Learning Classifiers and SenseFly eBee SQ Multispectral UAV Images

  Akar, Özlem; Akar, Alper; Bayata, Halim Ferit

  This study investigates the use of vegetation indices for accurately identifying crops with similar spectral characteristics as grapes, apricots, tomatoes, wheat, and clover for enhancing crop monitoring and management. A 59-hectare area in Karakaya Village, located in the Üzümlü district of Erzincan Province, Türkiye, was selected as the study area. This area contains crops with varying textures, object height, and spectral characteristics. In the study, multispectral (MS) images were acquired using the SenseFly eBee SQ unmanned aerial vehicle (UAV), and subsequently processed to generate an orthophoto, digital terrain model (DTM), and digital surface model (DSM). Fifteen vegetation indices, Gabor texture features, and object heights were integrated into MS bands. Crop classification was performed using two high-accuracy machine learning algorithms: Random Forest (RF) and Support Vector Machine (SVM). According to the overall classification accuracy results, the use of vegetation indices improved classification accuracy by 9% for RF and 5% for SVM. Incorporating Gabor texture features with the top-performing indices (MACARI1, OSAVI, ADVI, and DVI) further increased accuracy to 20% for RF and 12% for SVM. Additionally, including object height alongside the indices and Gabor features resulted in further accuracy gains of 10% and 11% for RF and SVM, respectively. F1-score, specificity, and accuracy analyses, along with various kappa statistics, also the significant improvements in classification performance. According to the McNemar test, the χ^2 values comparing orthophoto images with those incorporating indices, texture, and object height ranged from 6.353 to 35.556 for RF, and from 7.220 to 11.021 for SVM. Since all χ^2 values exceeded 3.84, the results indicate statistically significant improvements in the classification accuracy at the 95% confidence interval.

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• ArticleYalnız künye2026/r/ebyu/11366
  Monitoring of the compatibility between the radon gas emanation and the self-compacting concrete properties using different mineral admixture

  Bulut, H. Alperen; Şahin, Remzi

  The relationships between radon gas emanation, permeability, and compressive strength in Self-Compacting Concretes containing different ratios of fly ash, silica fume, and ground granulated blast furnace slag (respectively of 5%, 12.5%, and 20%) were comprehensively investigated. Permeability tests were performed according to both traditional (sorptivity) and indirect (rapid chloride ion permeability) methods, and the relationship between Brunauer-Emmett-Teller analyses and radon concentration of self-compacting concretes was also analysed. Based on 120-day data, the optimum ratios for the combination of high compressive strength and low Average Radon Activity Concentration were determined to be 5% fly ash and 20% silica fume. As the sorptivity coefficients of self-compacting concretes containing silica fume and ground granulated blast furnace slag increased, the Average Radon Activity Concentration also increased. This later decreased with the increase in the sorptivity coefficient of the mixtures. The rapid chloride ion permeability of the control, fly ash-, ground granulated blast furnace slag-, and silica fume-added concrete was found to be 6077 Coulomb, 2989 Coulomb, 1668 Coulomb, and 1137 Coulomb, respectively. The relationship between rapid chloride ion permeability and Average Radon Activity Concentration of the mixtures varied depending on the type of mineral additive. The 90-day sample containing 12.5% ​​fly ash had the highest Brunauer-Emmett-Teller surface area (33.01 m2/g) and cumulative pore volume (0.071 cm3/g) values. This study provides a novel perspective by simultaneously examining the relationships between radon activity concentration, permeability properties, and compressive strength of self-compacting concretes produced with different mineral additives. Unlike previous studies, this work establishes direct correlations between radon emanation and both sorptivity and chloride ion permeability, identifying silica fume and ground granulated blast furnace slag as effective additives for achieving low-permeability and low-radiation concrete. These findings offer a new approach for developing durable and radiologically safer self-compacting concrete mixtures.

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• ArticleYayıncıda açık erişim2026/r/ebyu/10920
  Evaluation of ground motions and seismic performance of RC buildings after 2023 Kahramanmaraş earthquakes: design and construction deficiencies

  İnce, Elif Gökçe; Çakıl, Burak; Şimşek, Mutlu; İnce, Ozan

  Two major earthquakes occurred on the East Anatolian Fault Zone in Türkiye on February 6, 2023. The epicenter of these earthquakes was Pazarcık and Elbistan, distinct from Kahramanmaraş province. These earthquakes occurred on the same day, 9 hours apart, and effected a very large area. This study evaluates the damage in reinforced concrete buildings during the Kahramanmaraş earthquakes. Firstly, this study examined Türkiye’s seismicity, fault lines, and recent earthquakes in Türkiye. Then, the fault rupture of the Kahramanmaraş earthquakes and the affected regions from these earthquakes were exam-ined. Acceleration records of the Pazarcık and Elbistan earthquakes were evaluated by comparing them with the Türkiye Building Earthquake Code-2018 (TBEC-2018). These acceleration records were compared with the largest earthquake (DD1) and design earthquake (DD2) defined in TBEC-2018. Requirements for earthquake-resistant building design and the deficiencies in the current Türkiye building stock were discussed. Damages in RC buildings were evaluated in terms of design and construction perspectives. It has been observed that deficiencies are frequently made in the application of reinforcement details in RC buildings. Brittle shear damage has been commonly observed in reinforced concrete buildings, even though they are expected to behave ductile. Design and construction deficiencies have been observed to be widespread even in buildings still under construction.

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• ArticleYalnız künye2026/r/ebyu/10900
  Investigating the rubberized concrete-filled steel tube composite columns and developing artificial intelligence-based analytical models for ultimate axial strength prediction

  Simsek, Oguz; Katipoğlu, Okan Mert; İpek, Süleyman; Güneyisi, Erhan; Güneyisi, Esra Mete

  The application of rubberized concrete (RuC) in structural systems has led to the development of a novel composite system: the rubberized concrete-filled steel tube (RuCFST). The characteristics of infill materials, particularly RuC, significantly influence the structural behavior and performance of these composite systems. This study aims to evaluate the ultimate axial strength of RuCFST columns, considering rubber aggregate content up to 75% and infill concrete compressive strength up to 63 MPa, and propose artificial intelligence (AI)-based design models using deep learning (CNN, DNN, Autoencoder), machine learning (CatBoost), hybrid AI/neuro-fuzzy (ANFIS), and unsupervised neural network (SOM) to predict the ultimate axial strength. A robust dataset was compiled from the literature, comprising the results of 131 RuCFST column specimens. The experimental results underwent a comprehensive statistical assessment using various analytical methods, including Pearson correlation coefficient analysis, histogram analysis, distribution fitting curve analysis, and box-and-whisker plot analysis. Additional statistical metrics such as Kolmogorov–Smirnov, skewness, and kurtosis values were also used. Furthermore, by considering various variables covering the geometrical and material properties of the RuCFST columns, the design models were developed using AI techniques. The statistical evaluation results indicated that the compiled dataset is statistically significant and encompasses various input and output parameters. The design models developed in this study displayed varying prediction performance. However, those created using the Autoencoder and ANFIS methods showed statistically satisfactory performance in determining the load-carrying capacity of the columns.

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• ArticleYayıncıda açık erişim2026/r/ebyu/10873
  Physically constrained air–sea heat flux driven modeling of sea surface temperature using atmospheric and oceanographic variables with explainable AI

  Katipoğlu, Okan Mert; Bazrafshan, Ommolbanin; Mohammadi, Mojtaba; Alsumaiei, Abdullah A.

  This study aims to develop a robust multivariate regression framework for accurately predicting monthly Sea Surface Temperature (SST) using a core set of ten primary atmospheric and oceanographic variables, including relative humidity, specific humidity, temperature, mean square slope of waves, mean wave direction, 2-meter air temperature, top net solar radiation, total precipitation, mean evaporation rate, and potential evaporation. The originality of this research lies in the simultaneous integration of meteorological and oceanographic parameters into multiple machine learning architectures, coupled with a detailed interpretability analysis using SHAP values, which provide a comprehensive understanding of the influence of variables on SST prediction. The dataset was split into 60 % for training, 20 % for testing, and 20 % for validation. Four machine learning models: Random Forest, CatBoost, one-dimensional Convolutional Neural Network, and Support Vector Machine were optimized using RandomizedSearchCV, with MinMaxScaler applied to ensure consistent feature scaling across models. Model performance was evaluated using Mean Squared Error, Root Mean Squared Error, Mean Absolute Error, Coefficient of Determination, and Explained Variance metrics. Meanwhile, SHAP analysis identified the most influential features as 2-meter air temperature, mean evaporation rate, mean square slope of waves, air temperature, and specific humidity. Results revealed that the Support Vector Machine achieved the highest accuracy overall, followed by CatBoost, confirming that combining meteorological and oceanographic predictors significantly enhances SST forecasting performance. The physical consistency analyses, supported by the bulk air–sea heat flux formulations for sensible and latent heat fluxes, confirm that the machine-learning model’s most influential predictors: near-surface air temperature, specific humidity, and evaporation-related variables are fully consistent with the fundamental thermodynamic controls governing SST. This research not only advances SST prediction accuracy but also provides a transparent interpretability framework, making it a valuable reference for climate modeling, marine resource management, and environmental monitoring applications.

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• ArticleYalnız künye2026/r/ebyu/10859
  Beyond Conventional Methods: Predicting Novel Modified Groundwater Drought Indices with Advanced Computing Paradigms

  Katipoğlu, Okan Mert; Kartal, Veysi; Aktürk, Gaye; Kilinc, Huseyin Cagan; Elshaboury, Nehal; Yamany, Mohamed S.; Pande, Chaitanya Baliram

  To achieve accurate and reliable predictions of groundwater drought, it is essential to incorporate relevant meteorological variables and apply advanced predictive methodologies capable of modeling their complex interrelationships. In this study, groundwater drought prediction models were developed based on the seven modified drought indices, which are obtained from groundwater levels and multiple meteorological parameters. The analysis focuses on four districts in Türkiye (Tekirdağ-Çorlu, Tekirdağ-Çerkezköy, Amasya-Merzifon, and Amasya-Centre), which are hereafter referred to collectively as the study areas. Three machine learning models were applied such as a standalone Artificial Neural Network (ANN) and two hybrid models, ANN integrated with Shuffled Frog Leaping Algorithm (SFLA-ANN) and Firefly Algorithm (FFA-ANN). The hybrid metaheuristic algorithms were chosen for their ability to model nonlinear and complex relationships between meteorological variables and groundwater levels, which traditional statistical methods cannot fully capture. Modified drought indices were employed to represent the groundwater drought better. The findings indicate that the hybrid FFA-ANN model generally provides the highest predictive accuracy for most indices, with coefficient of determination values frequently exceeding 0.90. In contrast, the ANN model occasionally outperforms the hybrid FFA-ANN model in terms of root mean square error (RMSE) for specific stations within the study areas. These results highlight the robustness of hybrid ANN approaches, especially FFA-ANN, in predicting groundwater drought under changing climate conditions. The novelty of this study is the modification of meteorological drought indices widely used to represent groundwater drought and the application of hybrid metaheuristic ANN models to estimate these indices in Türkiye. The results demonstrated that such models provide a reliable framework for predicting groundwater drought and provide decision-makers with advanced tools for water resources management, irrigation planning, and climate adaptation strategies.

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