Yazarlar
- Gul, Muhammet(16)
- Kahraman, Gökhan(13)
- Onat, Onur(13)
- Yucesan, Melih(12)
- Etli, Serkan(11)
- Mete, Suleyman(11)
- Akkurt, Nevzat(10)
- Karataş, Mehmet(10)
- Utku, Anıl(10)
- Yön, Burak(10)
- Akmaz, Düzgün(8)
- Cemalgil, Selim(8)
- Demir, Yakup(8)
- Ergen, Burhan(8)
- Çil, Zeynel Abidin(8)
- Akgül, Melek(7)
- Doğan, Gürkan(7)
- Gülmez, Nihan(7)
- Xu, Qian(7)
- Yildirim, Ozal(7)
- Yilmaz, Mehmet(7)
- Özceylan, Eren(7)
- Celik, Erkan(6)
- Göçmen, Elifcan(6)
- Orhan, Ahmet(6)
- Ertarğın, Merve(5)
- Serin, Faruk(5)
- Yilmazer, Merve(5)
- Zheng, Qiang(5)
- Akpolat, Mustafa(4)
- Atangana, A(4)
- Baskonus, HM(4)
- Bulut, H(4)
- Ding, Yulong(4)
- Dogan, Akar(4)
- Eristi, Huseyin(4)
- Hammouch, Z(4)
- Işık, Erdem(4)
- Karakose, Mehmet(4)
- Kaya, Sema Kayapinar(4)
Yayınlar
- MakaleAçık Erişim2025Investigation of the Benefits of the Floating Solar Power Plant to be Established on Keban Dam Lake using Penman, Linacre and Artificial Neural Networks Methods
Esen, Hikmet; Kurt, Savaş; Toka, UnalT Unal
In this study, it will be calculated how much water evaporation will be prevented if a Floating Solar Power Plant (FSPP) is installed on a certain part of the Keban Dam lake in Elazığ (38.41˚N, 39.14˚E), Türkiye by using the Penman and Linacre methods and the Artificial Neural Networks (ANN) method, which are widely used in evaporation calculation. Thus, it will be calculated how much energy can be produced with this saved water. Keban Hydroelectric Power Plant (HPP), examined in this study, is Türkiye's 3rd largest hydroelectric power plant with an installed power of 1.330 MWe. It is the fourth largest lake in Türkiye and the second largest dam lake after the Atatürk Dam. Keban Dam meets the electrical energy needs of 1.750.733 people in their daily lives with an average electricity production of 5.794.927,279 kWh. It has been calculated that thanks to a floating photovoltaic power plant that will cover 10% of the dam lake, a total of 31.305.282 m3 of water in normal water code will be prevented from evaporating annually, and thanks to this water, energy benefit will be provided with the power plant with an installed power of 6.624,84 MWe. In addition, it has been analyzed that 11.297.018 kWh of electricity can be produced from the dam turbines with the water prevented from evaporating thanks to FSPP. Approximately 72.39 MWe energy is needed for the Ağın, Pertek, Serince, Palu-Kovancılar, Uluova, Kuzova irrigation projects around the Keban Dam lake. It has been determined that the amount of water and energy needed for irrigation of these plains can be obtained thanks to FSPP.
- MakaleAçık Erişim2025The potential effects of the Zangezur Corridor on Türkiye’s foreign trade
Ates, Erdem
The Zangezur corridor connects Azerbaijan and the Nakhchivan Autonomous Republic. An exclave of Azerbaijan. After the 2020 Nagorno-Karabakh War, the obstacles to opening the Zangezur corridor were solved through the ceasefire agreement. Implementing the Zangezur corridor will shorten the route used nowadays by the Baku—Tiflis—Kars railway by almost 150 km. For this reason, it is thought that this corridor would decrease transportation costs and increase trade volumes. Türkiye, which will have direct land contact with Turkic countries, is the country of one of the most prominent effects of the opening of the Zangezur corridor. The main objective of this study is to determine the effect of Türkiye’s trade volumes on the opening of the Zangezur corridor. To investigate this, the study constructs a built-in structural gravity model with a new economic geography perspective covering 182 countries from 2007 to 2023. Then, the coefficient values obtained from the model are used to determine the possible change in Türkiye's trade with nine Asian countries that are expected to use this corridor directly. According to the results, it is expected that Türkiye’s top three export-enlarging countries, respectively, are Armenia, Azerbaijan, and Turkmenistan, which will be import-enlarging Azerbaijan, Armenia, and Uzbekistan.
AtıfOpenAlex1TR Dizin1
İncele - MakaleAçık Erişim2025A comprehensive case study on structural behavior before and after soil improvement
Etli, Serkan; Icen, Abdullah
Addressing the issue of inadequate engineering properties in soil is crucial for enhancing structural behavior and reducing damages, especially during seismic events. This study addresses the critical issue of inadequate engineering properties in soil and their impact on structural behavior, particularly during seismic events. Employing a case study methodology, structural responses are assessed before and after soil improvement, emphasizing the problem's significance. Site response analyses for pre-improvement phases reveal design insufficiencies and areas prone to soil liquefaction. The proposed solution is soil reinforcement through cement injection. Examining six cases under various earthquake scenarios and ground conditions, 132 dynamic analyses are conducted, highlighting substantial improvements post-soil improvement, especially in Interstory Drift Ratios (IDR) and roof drift ratios. These findings underscore the effectiveness of soil reinforcement in mitigating seismic damage, emphasizing its critical role in enhancing structural behavior and contributing to a better understanding of addressing soil inadequacies for improved seismic performance.
AtıfOpenAlex2TR Dizin2İncele - MakaleAçık Erişim2025Three-Dimensional (3D) Airport Runway Design Model for Tunceli Province
Cemalgil, Selim; Kaçmaz, Muhammed Nur
This research article addresses the design of an airport runway for the province of Tunceli, which is characterized by a geographically challenging topography. The study highlights the inadequacies of traditional two-dimensional (2D) analyses in complex terrains and proposes a decision support approach based on three-dimensional (3D) modelling. In this context, two alternative sites were evaluated. The first site, assessed using conventional 2D methods, was found unsuitable due to excessive natural slope and significant obstacle penetrations. The second site, analyzed through a 3D environment using Digital Elevation Model (DEM) data in Blender, enabled the volumetric modelling of Obstacle Limitation Surfaces (OLS) and provided a more realistic representation of terrain-obstacle interactions. In this study, the Boeing 737-800 classified under ICAO Code 4C and widely used in Türkiye’s domestic flights, was selected as the reference aircraft for the airport runway design. Considering site-specific altitude and temperature conditions, the required runway length was calculated as 3200 m. The 3D analyses indicated full compliance with approach, takeoff-climb and inner horizontal surface criteria for the north-south orientation. Overall, the results demonstrate that 3D data-driven methods offer a more reliable and precise framework for airport planning in mountainous regions compared with traditional 2D approaches.
- MakaleAçık Erişim2025Improving earthquake resistance in rc buildings via steel bracing members
Yön, Burak; Tam, Gürkan
In this study, the effect of retrofitting with steel bracing members on the seismic behavior of reinforced concrete buildings was examined. A reinforced concrete building with low concrete strength was modeled for the numerical study. This modeled building was strengthened with steel X-braced members to create a total of two buildings. To evaluate the seismic behavior of the modeled buildings, incremental dynamic analyses were performed using static pushover analysis and 11 different earthquake records. Due to the symmetrical structure of the modeled buildings, a total of 220 analyses were carried out simultaneously in the x and y directions. As a result of the analysis, the capacity curves of the existing building and the strengthened building with steel bracing members, maximum responses, inter-storey drifts, and fragility curves according to the damage status were obtained. As a result of the analysis, it was determined that the maximum response of the building strengthened using X steel cross members increased significantly compared to its current state, while the damage to the structural elements was significantly reduced.
AtıfOpenAlex1İncele - MakaleAçık Erişim2025Regional Variations in the Physical and Rheological Properties of Asphalt Binders Recovered from Earthquake-Damaged Roads Following the February 6, 2023 Türkiye Earthquakes
Furtana, Beyza
This study investigates the physical and rheological properties of asphalt binders recovered from earthquake damaged road sections in Hatay, Kahramanmaras, Gaziantep, and Malatya following the February 6, 2023, earthquakes in southern Türkiye (Mw = 7.8 and Mw = 7.5). Asphalt mixture samples were collected from severely deteriorated pavements, and bitumen was extracted through solvent extraction and rotary evaporation. The recovered binders were subjected to penetration, softening point, rotational viscosity, and Dynamic Shear Rheometer (DSR) tests to evaluate aging, stiffness, high-temperature performance, and viscoelastic behavior. Results revealed significant regional differences: Hatay binders exhibited the lowest penetration, highest softening point, highest viscosity, and the greatest rutting resistance (high G*/sinδ values) while maintaining lower phase angles, indicating enhanced stiffness and elasticity. In contrast, Kahramanmaras binders showed the highest penetration, lowest softening point, and lower viscosity, combined with high phase angles, reflecting a softer, more viscous character with reduced deformation resistance. Malatya and Gaziantep binders displayed intermediate characteristics, with Malatya showing moderate stiffness and Gaziantep retaining greater flexibility. Across all samples, viscosity decreased markedly with increasing temperature, and rutting resistance diminished at higher service temperatures; however, binders with higher initial G*/sinδ values retained better performance. The findings highlight the influence of regional seismic intensity, climatic conditions, and pavement history on binder behavior and emphasize the need for region-specific maintenance and rehabilitation strategies in post-disaster pavement management.
- MakaleAçık Erişim2025Elazığ İli Örneğine Ait On-Grid Çatı Üstü Güneş Enerji Santralinin Yıllık Gerçek Üretim Verilerinin PV*SOL Simülasyon Değerleri ile Karşılaştırılması
Celik, Ertugrul; Özdemir, Osman
Geçmişten bugüne kadar insanoğlunun temel gereksinimlerinin karşılanması için enerjiye ihtiyaç duyulmaktadır. Dünyada nüfusun hızlı bir şekilde yükselmesi, teknolojik faaliyetlerin gelişimiyle birlikte enerji tüketimi gün geçtikçe artmaktadır. Günümüzde artan enerji talebini karşılamak için fosil yakıtlar ve yenilenebilir enerji kaynakları kullanılmaktadır. Yenilenebilir enerji kaynakları, doğal süreçler aracılığıyla sürekli olarak yenilenen ve fosil yakıtlarla karşılaştırıldığında düşük emisyon değerlerine sahip olan çevre dostu enerji üretim kaynaklarıdır. Güneş enerjisi sistemlerindeki teknolojik ilerlemeler, maliyetlerin düşmesi ve verimliliğin artması, güneş enerjisini yenilenebilir enerji kaynakları arasında güçlü bir alternatif haline getirmektedir. Özellikle arazi ve çatılara kurulan Güneş Enerji Santrallerinin (GES) sayısı gün geçtikçe artmaktadır. Bu çalışmada Elazığ ili sınırları içinde sanayi bölgesinde bir işletmenin çatısına kurulmuş şebekeye bağlı çatı (on-grid) üstü GES sistemi ele alınmıştır. Çatı üstü GES toplam 165 adet, 545 Watt gücünde monokristal panellerden oluşmakta olup toplam gücü 89,9 kWp’tir. Sistem performansını artırmak amacıyla seçilen alan için en iyi yönelimi ve eğim açısını bulmak amacıyla PV*SOL yazılımı kullanıldı. Bu çalışmada mevcut GES sisteminin bir yıllık enerji üretim verileri aylara göre incelenerek, PV*SOL simülasyonunda elde edilen benzetim sonuçları ile karşılaştırılarak PV*SOL yazılımının doğruluk oranı değerlendirilmiştir. Çalışma sonucunda PV*SOL simülasyon programından elde edilen veriler ile çatı üstü GES gerçek verileri arasında %0,03 fark olduğu tespit edilmiştir. Bu sonuçlar göz önüne alındığında, uygulaması yapılan enerji sisteminin teknik ve ekonomik olarak uygun olduğu söylenebilir.
AtıfOpenAlex1İncele - MakaleAçık Erişim2025Predicting daily energy consumption in ev charging stations using tree-based models
Ertarğın, Merve
Electric vehicles (EVs) have emerged as a cornerstone in the transition toward sustainable transportation, offering a cleaner alternative to conventional fossil fuel-based mobility. Their integration into modern transportation systems not only supports global decarbonization efforts but also requires robust energy management solutions to address the growing demands on charging infrastructure. Ensuring efficient energy consumption forecasting is vital for optimizing EV charging station operations and maintaining grid stability. This study aims to predict the daily energy consumption of an EV charging station using a public dataset. Key features such as charging duration, vehicle count, mean and median connection time were utilized for the prediction. Both the original dataset and its z-score-cleaned version were employed to evaluate the models' performance under different data conditions. Decision Tree (DT), Random Forest (RF), Extreme Gradient Boosting (XGBoost) and Light Gradient Boosting Machine (LightGBM) models were implemented, with hyperparameter optimization conducted via GridSearchCV. The results revealed that all four models achieved high accuracy in predicting daily energy consumption across both datasets, with R2 values exceeding 0.97. Specifically, for the z-score-cleaned dataset, LightGBM demonstrated the best performance with the lowest RMSE (37.35 kWh) and MAE (28.93 kWh), as well as the highest R2 value (0.9727) followed by XGBoost, RF and DT.
AtıfOpenAlex1İncele - MakaleAçık Erişim2025Effects of Alternative Binder and Aggregate Use on the Compressive Strength of Concrete
Gülmez, Nihan; Pala, Mustafa
The use of industrial wastes in concrete production is of significant importance in reducing environmental impacts and lowering costs. In this study, concrete mixtures were designed by using olivine minerals as a substitute for Portland cement (PC), chromite powder as fine aggregate, and ferrochromium slag as coarse aggregate. The effects of different binder and aggregate ratios on the compressive strength of the concrete were examined after a 7-day curing period. Experimental results showed that mixtures with 5% olivine substitution achieved the highest compressive strength of 22.30 MPa, particularly in mixtures with a cement dosage of 500 kg/m³. This result indicates that the low water/cement ratio and the addition of olivine contribute to increased early-age strength. Furthermore, increasing the chromite powder content to 20% in mixtures with a water/cement ratio of 0.61 led to an 11.7% increase in compressive strength, demonstrating the positive impact of chromite powder on the mechanical performance of the concrete. These findings highlight that cement replacements and aggregate types can significantly affect the mechanical properties of concrete, emphasizing the need for careful selection of both material compositions.
- MakaleAçık Erişim2025Tek değişkenli tahminde tahmin yöntemlerinin karşılaştırılması: Türkiye çimento endüstrisi örneği
Tüzüntürk, Selim; Eteman, Fatma Sert
Bu çalışma, Türk çimento imalat sanayinde çimento üretimini tahmin etmek için bir yöntem bulmayı ve bu yöntemle çimento üretimini tahmin ederek gelecekteki üretim gelişimi hakkında bazı sonuçlar çıkarmayı amaçlamaktadır. Bu çalışmada kullanılan veri, Türk çimento sanayinin toplam aylık çimento üretimidir. Veriler, Ocak 2017'den Eylül 2024'e kadar 93 ayı kapsamaktadır. Oluşturulan modele yapay sinir ağı modelleme prosedürleri uygulanmış ve derin öğrenme araçları yardımıyla tahmin yapılmıştır. Tahminlerin performansı oldukça iyi olup, ağın genelleştirilebilir olduğu sonucuna varılmıştır. Sonuçlar, eğitilen ağdan elde edilen eşik değerleri ve ağırlıklar kullanılarak geliştirilen model ile aylık üretimlerin tahmin edilebileceğini göstermiştir. Aylık çimento tahminleri Aralık 2026'ya kadar yapılmıştır. Tek değişkenli zaman serisi verilerinin YSA'lar ile modellenmesinin başarısı, MAPE istatistikleri kullanılarak Winters ve SARIMA yöntemleri ile karşılaştırılarak doğrulanmıştır. En uygun tahmin yönteminin YSA olduğu bulunmuş ve bulgular tartışılmıştır.
- MakaleAçık Erişim2025Polipropilen lifli mikro betonlarda nano silika kullanımının etkisi
Akgül, Melek
Son yıllarda nano malzemelerin kullanımı diğer tüm uygulama alanlarında olduğu gibi çimento esaslı sistemlerde de önem arz etmektedir. Bu çalışma, harç ve beton uygulamalarında değerlendirilmiş olan nano malzemeler ve polipropilen liflerin mikro betonlarda eş zamanlı kullanımını amaçlamaktadır. Çalışma kapsamında üretilen 9 seri mikro betonun mekanik, fiziksel ve mikro yapı üzerindeki etkisi araştırılmıştır. Karışım tasarımlarında 3 farklı miktarda (0, 5, 10 kg/m3) nano silika ve 3 farklı miktarda (0, 1.2, 2.4 kg/m3) 12 mm uzunluğunda kırpılmış polipropilen lif kullanılmıştır. Referans karışımında kullanılan çimento miktarı 625 kg/m3 ve tüm karışımlarda kullanılan silis dumanı miktarı 125 kg/m3’tür. Üretilen ve kirece doygun su kürü ile olgunlaştırılan 9 seri mikro beton ile bir dizi fiziksel ve mekanik deney uygulanmıştır. Ayrıca 9 seri mikro betonu temsil edecek şekilde 6 seriden sağlanan numune örnekleriyle Taramalı Elektron Mikroskobu ve Enerji Dağılımlı X-Işını Spektroskopisi analizi yapılmıştır. Buna göre artan nano silika miktarı basınç ve eğilme dayanımı üzerinde doğrusal bir artış sunmaz, ancak 5 kg/m3 nano silika kullanımı eğilme ve basınç dayanımı üzerinde pozitif etkiye sahiptir. Ayrıca polipropilen lif kullanımı eğilme dayanımını artırmaktadır. Enerji Dağılımlı X-Işını Spektroskopisi verilerine göre artan nano silika miktarı C-S-H oluşumunu desteklemektedir. Taramalı Elektron Mikroskobu analizi görüntülerinde ise polipropilen lif miktarının karışımdaki dağılımı belirgindir.
- MakaleAçık Erişim2025YOLOv8 for Road DamageRecognition: Deep Learning-Based Segmentation and Detection System
Agdas, Mehmet Tevfik; Arik, Kaan
Road surface detection is critical for improving traffic safety and reducing road maintenance costs. Because traditional methods are time-consuming and costly, deep learning-based image processing techniques offer an important alternative in this field. This study aims to develop a model that automates the detection and segmentation of road surface defects such as potholes, manhole covers, and culverts using deep-learning-based image processing techniques. In this study, a dataset previously used in the literature was preferred. It was observed that object detection was performed using a dataset from the literature. In this study, both object detection and object segmentation were performed using different parameters. To prove the success of object segmentation, both object detection and segmentation were performed using the YOLOv8 algorithm, which has previously obtained successful results. AdamW optimization and Auto Batch parameters were selected for this study. With these parameters, object detection was first performed with the YOLOv8s model, which is one of the variances of the YOLOv8 algorithm with the most successful results in the literature, and a successful 92.8% mAP@50 performance value was obtained according to the sources in the literature. In this study, the YOLOv8n, YOLOv8s, YOLOv8m, and YOLOv8l variance models of the YOLOv8 algorithm were used with the preferred parameters, and segmentation was performed. In object segmentation, a map@50 performance value of 90.9% in all classes and 99.1% in culverts was obtained using the YOLOv8l model. A map@50 performance value of 89.1% for pothole segmentation and 88% for manhole cover segmentation was obtained using the YOLOv8s model. The results of the analyses showed consistency in precision and recall values. These findings contribute significantly to improving road safety, reducing maintenance costs, and supporting sustainable urban infrastructure. Future research could explore integrating multiple data sources and adapt these models to more complex road conditions.
AtıfOpenAlex1
İncele - MakaleYalnız künye2025Scopus SJRQ2Impact of green innovation on environmental issues: Findings from BRICS-T countries based on spatial analysis
Han, Ayşegül; Pehlivan, Ceren; Konat, Gökhan; Koncak, Ahmet
This study examines the impact of green innovations on environmental sustainability in BRICS-T countries between 2008 and 2021. Using indicators such as renewable energy consumption, R&D investments, industrial design activities, and carbon emissions, the analysis employs both conventional and spatial panel estimation techniques. The findings reveal that investments in renewable energy play a crucial role in reducing carbon emissions and positively contribute to environmental sustainability. However, the effectiveness of environmental innovation policies and R&D expenditures in mitigating emissions remains limited, particularly in the short term. Additionally, industrial design activities are identified as a significant contributor to rising emissions, highlighting the need for a transition toward more sustainable industrial practices. The study also uncovers spatial dependencies among countries, demonstrating that carbon emissions in one nation can influence those in neighboring countries, emphasizing the necessity of regional cooperation and coordinated environmental policies. Based on these findings, the study provides policy recommendations to enhance the effectiveness of green innovations, stressing the importance of increasing investments in renewable energy, aligning R&D expenditures with environmentally sustainable technologies, and promoting sustainable industrial practices across BRICS-T countries.
AtıfScopus2WoS2OpenAlex3
İncele - MakaleAçık Erişim2025Scopus SJRQ1Seismic Foresight: A Novel Multi-Input 1D Convolutional Mixer Model for Earthquake Prediction Using Ionospheric Signals
Uyanik, Hakan; Kokum, Mehmet; Senturk, Erman; Freeshah, Mohamed; Ozcelik, Salih T. A.; Halil Akpinar, Muhammed; Celik, Serenay; Sengur, Abdulkadir
This study proposes a novel deep learning approach for predicting significant earthquakes (Mw ≥ 5.0 ) in Turkey using ionospheric Total Electron Content (TEC) data and space weather indices. ConvMixer is a lightweight CNN architecture that blends spatial and channel information using depthwise convolutions and pointwise layers. Inspired by vision transformers, it offers efficient image classification with fewer parameters and high performance. We developed a multi-input one-dimensional convolutional mixer (MI-1D-ConvMixer) model to classify TEC data from the preceding five consecutive days as either precursory to an earthquake on the 6th day or normal. The model incorporates six inputs: five 1D TEC signals and one 1D space-weather index array, including the global geomagnetic index (Kp), storm duration distribution (Dst), sunspot number (R), geomagnetic storm index (Ap-index), solar wind speed (Vsw), and solar activity index (F10.7) are also utilized to reveal non-seismic related pre-earthquake ionospheric variations. Our methodology involves two stages: 1) a preprocessing stage to enhance TEC signals and 2) an end-to-end training of the MI-1D-ConvMixer model. The model architecture features depth-wise and point-wise convolutions with patch embedding, utilizing uses four tunable hyperparameters: network depth, hidden dimension size, kernel size, and patch size. We used 196 earthquakes data from Turkey from 2010-2023, and TEC data from the TNPGN-Active GNSS stations. The dataset was split into 75% for training and 25% for testing. Performance metrics, including classification accuracy, sensitivity, specificity, and F1-score, are used for evaluation. Our model achieved a classification accuracy of 97.49%, demonstrating its potential for earthquake prediction systems. This research contributes to the field by introducing a novel deep learning architecture specifically designed for integrating TEC and space weather data for earthquake prediction. Future work should focus on validating the model’s performance in different geographical regions and investigating its limitations.
AtıfScopus7OpenAlex5İncele - BildiriAçık Erişim2025Toplam Elektron Içeriǧi ve Uzay Iklim Verileri Kullanilarak Transfer Öǧrenme ile Deprem Öncüllerinin Analizi
Uyanik, Hakan; Şentürk, Erman; Köküm, Mehmet; Özçelik, Salih Taha Alperen; Akpinar, Muhammed Halil; Şengür, Abdulkadir
The stress changes occurring in the Earth's crust before earthquakes may cause some anomalies in the ionosphere and cause significant changes in the Total Electron Content (TEC) and Space Climate (SCC) parameters. The analysis of these changes offers a significant potential in the detection of earthquake precursors. In this study, the analysis of earthquake precursors was performed using TEC and UC data and the effectiveness of transfer learning and machine learning techniques was investigated. Features were extracted from five-day TEC and UC data using a pre-trained Multi-Input One-Dimensional Convolutional Mixer Network (ÇG-TB-EMA) and these features were classified with different machine learning algorithms. Within the scope of the study, earthquakes with a magnitude of Mw≥6.0 that occurred during the low and high solar activity periods of the 24th solar cycle were analyzed. The earthquakes were selected from the Far East region between 10°S-45°N latitudes and 120°E-160°E longitudes, where intense seismic activity is observed. Experimental studies have shown that during the low solar activity period, support vector machines (SVM) and artificial neural networks (ANN) models exhibited the highest performance with 96.04% accuracy and 96.08% F1 score, respectively. During the high solar activity period, the classification success decreased slightly, and SVM and ANN models achieved the best results with 90.28% accuracy and 89.86% F1 score. These findings reveal that the effect of solar activity on ionospheric variables should be taken into account in earthquake precursor detection. For future studies, long-term analyses are recommended to better understand the effects of solar activity.
- MakaleAçık Erişim2025Predicting shear strength in reinforced concrete deep beams through finite element modeling of diverse concrete materials
Polat, Erkan; Karaman, Gökhan
This study investigates the prediction of the strength of reinforced concrete deep beams, critical components in urban infrastructure, by evaluating their load-carrying capacities through finite element modeling and nonlinear inelastic analyses using LSDYNA software. Four widely used concrete material models were examined: Mat084/085, Mat159, Mat072R3, and Mat016. Analyses were conducted on two single-span and four double-span beams with varying reinforcement configurations and an aspect ratio of 1.0, based on well-documented experimental setups. Comparative analyses of force-displacement behavior and stress distributions revealed significant differences in shear strength predictions across the models, with Mat159 providing the most accurate results. These findings establish a reliable and cost-effective approach for predicting the capacities of deep beams, reducing reliance on extensive experimental testing. The study contributes valuable insights for improving strength predictions in critical infrastructure applications such as bridges and foundations.
AtıfScopus2TR Dizin2İncele - BildiriAçık Erişim2025Investigation of Mechanical Properties of Hot Mix Asphalt Modified with Elvaloy and Polyphosphoric Acid
Yilmaz, Mehmet; Yalcin, Beyza Furtana; El Naggar, H.; Barros, J.; Cachim, P.
In order for the bituminous hot mix layers to resist the increased traffic load and volume as well as the adverse environmental conditions for a long time without being deformed, the bituminous binder used in the mixture has been commonly modified with the Styrene-Butadiene-Styrene (SBS) polymer. The high demand for SBS makes it difficult to supply and therefore brings on the use of alternative additives. In this study, the combined use of Elvaloy and phosphoric acid (PPA) was investigated. Additionally, the effects on the mechanical properties of bituminous mixtures were examined. Modified binders were prepared using Elvaloy/PPA additives mixed under specified conditions. Hot mix asphalt (HMA) samples prepared with these binders were subjected to Marshall stability and flow, indirect tensile stiffness modulus, and indirect tensile fatigue tests to comprehensively evaluate the effects of modified bi nders on the mechanical properties of bituminous hot mixtures. As a result, the bituminous hot mix tests revealed that asphalt mixtures improved their mechanical properties at the specified ratios.
- MakaleAçık Erişim2025Scopus SJRQ1Evaluation 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.
AtıfScopus55WoS50OpenAlex61İncele - MakaleYalnız künye2025Scopus SJRQ4Deep learning-based hybrid traffic flow prediction model for freight transport
Utku, Anıl; Kaya, Sema Kayapinar; Can, Umit
Urban freight transportation is crucial to sustaining domestic and international trade and fulfilling the regular requirements of community companies and customers. Therefore, this study developed an efficient convolutional neural network (CNN) and long short-term memory (LSTM) based hybrid deep learning model for predicting traffic flow in freight transportation. The number of freight trucks that would travel along 663 Street in Anderlecht, Belgium, and their average speed was predicted. The created model was compared with support vector machine (SVM), linear regression (LR), multilayer perceptron (MLP), random forest (RF), LSTM, and CNN using the success measures. The critical finding of this study is that combining the feature extraction ability of the 1D CNN model with the learning ability of the LSTM model from multivariate traffic data increases the traffic density prediction success compared to other traditional methods. Based on the results of the experiments, the CNN-LSTM model outperformed all the other models employed in this study. With this model, freight density can be successfully predicted, assisting in planning freight transport activities.
- MakaleAçık Erişim2025Scopus SJRQ1Automatic Segmentation of Asphalt Cracks on Highways After Large-Scale and Severe Earthquakes Using Deep Learning-Based Approaches
Yilmaz, Mehmet; Yalcin, Erkut; Demir, Fatih; Ozdemir, Ahmet Munir; Atar, Muhammed; Gunes, Aysegul; Furtana Yalcin, Beyza; Cambay, Ertugrul
This study develops a deep learning-based automated system for detecting and segmenting earthquake-induced asphalt cracks, offering a rapid and reliable solution for post-disaster road condition assessments. Unlike traditional manual inspections, which are time-consuming and error-prone, our approach leverages advanced segmentation techniques to ensure accurate, pixel-level classification of various crack types. The main challenge of this study was determining the damage caused to highways by earthquakes with magnitudes greater than 7.0, which occur approximately once every 200 years. The most crucial step in the automatic detection of these damages is the reliable preparation of a high-accuracy dataset. To achieve this, pixel-based labels were created by experts in the construction field by analyzing each pixel value. Following two major earthquakes, a unique dataset for segmenting roadway deterioration was created through intensive and detailed studies. This study aims to present the performance results of popular deep learning-based segmentation models in an unbiased manner, providing a feasible infrastructure for future real-time applications. The innovative aspect of this research lies in the creation of a unique post-earthquake dataset, collected and labeled from highways affected by the February 6, 2023 earthquakes in Turkey (Mw = 7.7 and Mw = 7.6). Deep learning models, including SegNet, Attention SegNet, U-Net, FCN (8s), and DeepLab, were trained and tested on this dataset. Among these, the SegNet model achieved the best performance with an average accuracy of 86.72%, precision of 92.99%, and sensitivity of 78.45%. By demonstrating superior performance metrics compared to existing methods, this study provides a robust framework for future infrastructure monitoring and maintenance strategies, ensuring safer and more resilient transportation networks in disaster-prone regions.
AtıfScopus6OpenAlex4İncele
