Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12416/8651
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Article Importance of Zoning for Vertical Circulation Planning of Densely Populated Buildings: A Simulation Based Approach for Elevator Traffic Analyses(Gazi Univ, 2025) Deligoz, Dostcan; Harputlugil, TimucinElevator systems are essential in multi-story buildings, affecting circulation, travel time, and user comfort. Traditional design methods, based on mathematical calculations, provide initial estimates of elevator numbers and capacities by considering basic operational criteria. However, these methods cannot fully capture dynamic passenger flows and temporal variations in demand. Dynamic simulation-based elevator traffic analysis, on the other hand, allows for more comprehensive evaluation of elevator operations and enables testing of alternative zoning scenarios. In this study, a dynamic simulation-based analysis is applied as a case study for a hospital outpatient building. Different zoning strategies are implemented for elevator groups to evaluate their effect on system performance. Performance criteria, including Average Waiting Time (AWT), Average Time To Destination (ATTD), and Interval (INT), are assessed across different zoning scenarios and compared with values commonly reported in the literature. The results highlight the potential of zoning to improve elevator performance, including passenger handling, waiting times, and travel efficiency. Especially in buildings where physical modifications are difficult, the combination of simulation-based analysis and carefully designed zoning strategies can reveal the potential for enhancing operational performance and optimizing elevator efficiency within existing physical constraints.Article Citation - WoS: 1Citation - Scopus: 1Process Simulation of Pseudo-Static Seismic Loading Effects on Buried Pipelines: Finite Element Insights Using RS2 and RS3(MDPI, 2025) Alrubaye, Maryam; Sengor, Mahmut; Almusawi, AliBuried pipelines represent critical lifeline infrastructure whose seismic performance is governed by complex soil-structure interaction mechanisms. In this study, a process-based numerical framework is developed to evaluate the pseudo-static seismic response of buried steel pipelines installed within a trench. A comprehensive parametric analysis is conducted using the finite-element software Rocscience RS2 (version 11.027) to examine the influence of burial depth, pipeline diameter, slope angle, groundwater level, soil type, and permanent ground deformation. The seismic loading was represented using a pseudo-static horizontal acceleration, which approximates permanent ground deformation rather than full dynamic wave propagation. Therefore, the results represent simplified lateral seismic demand and not the complete dynamic soil-structure interaction response. To verify the reliability of the 2D plane-strain formulation, a representative configuration is re-simulated using the fully three-dimensional platform Rocscience RS3. The comparison demonstrates excellent agreement in shear forces, horizontal displacements, and cross-sectional distortion patterns, confirming that RS2 accurately reproduces the dominant load-transfer and deformation mechanisms observed in three-dimensional (3D) models. Results show that deeper burial and stiffer soils increase shear demand, while higher groundwater levels and larger permanent ground deformation intensify lateral displacement and cross-sectional distortion. The combined 2D-3D evaluation establishes a validated computational process for predicting the behavior of buried pipelines under a pseudo-static lateral load and provides a robust basis for engineering design and hazard mitigation. The findings contribute to improving the seismic resilience of lifeline infrastructure and offer a validated framework for future numerical investigations of soil-pipeline interaction.Conference Object Citation - Scopus: 12Computation of Supervisors for Fault-Recovery and Repair for Discrete Event Systems(IFAC Secretariat, 2014) Sülek, A.N.; Schmidt, K.W.In this paper, we study the fault-recovery and repair of discrete event systems (DES). To this end, we first develop a new method for the fault-recovery of DES. In particular, we compute a fault-recovery supervisor that follows the specified nominal system behavior until a fault-occurrence, that continues its operation according to a degraded specification after a fault and that finally converges to a desired behavior after fault. We next show that our method is also applicable to system repair and we propose an iterative procedure that determines a supervisor for an arbitrary number of fault occurrences and system repairs. We demonstrate our method with a manufacturing system example. © 2021 Elsevier B.V., All rights reserved.Article Citation - WoS: 2Citation - Scopus: 2Comparative Evaluation of YOLO and Gemini AI Models for Road Damage Detection and Mapping(MDPI, 2025) Demirel, Zeynep; Nasraldeen, Shvan Tahir; Pehlivan, Oyku; Shoman, Sarmad; Albdairi, Mustafa; Almusawi, AliEfficient detection of road surface defects is vital for timely maintenance and traffic safety. This study introduces a novel AI-powered web framework, TriRoad AI, that integrates multiple versions of the You Only Look Once (YOLO) object detection algorithms-specifically YOLOv8 and YOLOv11-for automated detection of potholes and cracks. A user-friendly browser interface was developed to enable real-time image analysis, confidence-based prediction filtering, and severity-based geolocation mapping using OpenStreetMap. Experimental evaluation was conducted using two datasets: one from online sources and another from field-collected images in Ankara, Turkey. YOLOv8 achieved a mean accuracy of 88.43% on internet-sourced images, while YOLOv11-B demonstrated higher robustness in challenging field environments with a detection accuracy of 46.15%, and YOLOv8 followed closely with 44.92% on mixed field images. The Gemini AI model, although highly effective in controlled environments (97.64% detection accuracy), exhibited a significant performance drop of up to 80% in complex field scenarios, with its accuracy falling to 18.50%. The proposed platform's uniqueness lies in its fully integrated, browser-based design, requiring no device-specific installation, and its incorporation of severity classification with interactive geospatial visualization. These contributions address current gaps in generalization, accessibility, and practical deployment, offering a scalable solution for smart infrastructure monitoring and preventive maintenance planning in urban environments.Article Citation - Scopus: 7COVID-19 Classification Using Hybrid Deep Learning and Standard Feature Extraction Techniques(Institute of Advanced Engineering and Science, 2023) El Shenbary, H. A.; Ebeid, Ebeid Ali; Baleanu, Dumitru I.There is no doubt that COVID-19 disease rapidly spread all over the world, and effected the daily lives of all of the people. Nowadays, the reverse transcription polymerase chain reaction is the most way used to detect COVID-19 infection. Due to time consumed in this method and material limitation in the hospitals, there is a need for developing a robust decision support system depending on artificial intelligence (AI) techniques to recognize the infection at an early stage from a medical images. The main contribution in this research is to develop a robust hybrid feature extraction method for recognizing the COVID-19 infection. Firstly, we train the Alexnet on the images database and extract the first feature matrix. Then we used discrete wavelet transform (DWT) and principal component analysis (PCA) to extract the second feature matrix from the same images. After that, the desired feature matrices were merged. Finally, support vector machine (SVM) was used to classify the images. Training, validating, and testing of the proposed method were performed. Experimental results gave (97.6%, 98.5%) average accuracy rate on both chest X-ray and computed tomography (CT) images databases. The proposed hybrid method outperform a lot of standard methods and deep learning neural networks like Alexnet, Googlenet and other related methods. © 2022 Elsevier B.V., All rights reserved.Article W-Band RCS Prediction of Small Objects: Comparing Two Widely Used Methods with Experimental Validation(Gazi Univ, 2025) Kara, Ali; Aydın, Elif; Yardım, Funda Ergün; Sezgin, Deniz; Ergun Yardim, FundaThis paper compares the accuracy of Shooting and Bouncing Rays and Electric Field Integral Equation methods for Radar Cross Section prediction of small objects at 77-81 GHz band. Existing studies on RCS prediction methods often lack comprehensive comparisons between computational and experimental results, particularly for small objects measured with a 77 GHz radar. This study addresses this gap by presenting an in-depth analysis of both simulation and measurement data. In this work, three targets with varying geometries and materials were measured with a frequency modulated continuous wave radar and simulated using Ansys HFSS and CST Studio Suite. The measurements were performed with a commercial off-the-shelf (COTS) frequency modulated continuous wave radar operating at 77-81 GHz. This study aims to emphasize the importance of considering both efficiency and accuracy when opting for an RCS prediction method. Overall, the outcomes of both methods have largely demonstrated good alignment. It has been noted that, while Shooting and Bouncing Rays method offers promising time-saving advantages, Electric Field Integral Equation method remains a valuable tool for complex geometries where precise results are crucial.Article Citation - WoS: 2Citation - Scopus: 2An Alternative Mean Reversion Test for Interest Rates(Central Bank Republic Turkey, 2018) Ozel, Ozgur; Ilalan, DenizA number of empirical studies assert that interest rates are governed by unit root processes rejecting any form of reversion to a long term mean by resorting to certain tests, among which the Augmented Dickey Fuller (ADF) is the most widely used one. In this study, we propose an alternative testing methodology that can be applied along with ADF test, in the sense that there are times where it can capture stationarity when the other fails to do so. Moreover, our test has more power than ADF test. As an application to real-data, we consider 10-year US and Turkish T-bond rates. (C) 2017 Central Bank of The Republic of Turkey. Production and hosting by Elsevier B.V.Article Detection and Classification of Femoral Neck Fractures From Plain Pelvic X-Rays Using Deep Learning and Machine Learning Methods(Turkish Assoc Trauma Emergency Surgery, 2025) Sevinc, Huseyin Fatih; Ureten, Kemal; Karadeniz, Talha; Gultekin, Gokhan KorayBackground: Femoral neck fractures are a serious health concern, particularly among the elderly. The aim of this study is to diagnose and classify femoral neck fractures from plain pelvic X-rays using deep learning and machine learning algorithms, and to compare the performance of these methods. Methods: The study was conducted on a total of 598 plain pelvic X-ray images, including 296 patients with femoral neck fractures and 302 individuals without femoral neck fractures. Initially, transfer learning was applied using pre-trained deep learning models: VGG-16, ResNet-50, and MobileNetv2. Results: The pre-trained VGG-16 network demonstrated slightly better performance than ResNet-50 and MobileNetV2 for detecting and classifying femoral neck fractures. Using the VGG-16 model, the following results were obtained: 95.6% accuracy, 95.5% sensitivity, 93.3% specificity, 95.7% precision, 95.5% F1 Score, a Cohen's kappa of 0.91, and the Receiver Operating Characteristic (ROC) curve of 0.99. Subsequently, features extracted from the convolution layers of VGG-16 were classified using common machine learning algorithms. Among these, the k-nearest neighbor (k-NN) algorithm outperformed the others and exceeded the accuracy of the VGG-16 model by 1%. Conclusion: Successful results were obtained using deep learning and machine learning methods for the detection and classification of femoral neck fractures. The model can be further improved through multi-center studies. The proposed model may be especially useful for physicians working in emergency departments and for those not having sufficient experience in evaluating plain pelvic radiographs.Article Integrating the Seljuk Cultural Layer Into Contemporary Life: The Case of Niğde Historic City Center(Istanbul Univ, Research Inst Turkology, Dept Art History, 2025) Yavaşcan, Emel Efe; Urak, Zehra GedizGünümüz tarihî kent merkezleri, yer altı ve yer üstündeki tarihî izleriyle, kültürel zenginlikleri ve özgün kimlikle8 rinin yanı sıra, “yerin ruhu”nu yansıtan kentsel hafıza alanlarıdır. Çok katmanlı bu tarihî kent merkezleri, kültür varlıklarının fiziksel ve işlevsel eskimesi, sosyo8kültürel ve ekonomik doku bozulmaları, koruma problemleri vb. sorunlarla giderek çöküntü yerleri hâline gelmektedir. Bu sorunları barındıran Niğde kentinde yapılmış koruma uygulamalarında, kentin yer altında ve yer üstünde bulunan katmanlarının dikkate alınmamış olması çalışmada problem olarak belirlenmiştir. Kentli tarafından tepe olarak algılanan çalışma alanı uzun zamandır çöküntü alanı niteliğindedir. Çalışmanın amacı, Niğde Tarihî Kent Merkezi’nin Selçuklu Dönemi’ne ait tarihî katmanını analiz etmek, haritalan8 dırmak ve bu katmanı çağdaş koruma uygulamalarına entegre etmeye yönelik öneriler geliştirmektir. Araştırma verileri, kentin tarihî gelişiminde en belirleyici dönemin Selçuklu Dönemi olduğunu göstermektedir. Bu sebeple çalışma kapsamında bu katman odak alınmıştır. Ayrıca, diğer tarihî katmanların da korunarak günlük yaşama kazandırılmasına katkı sağlamak, bu çalışmanın bir diğer hedefidir. Bu bağlamda, Selçuklu Dönemi’ne ait yer üstü ve yer altı değerlerinin sürdürülebilir korunmasına yönelik öneriler geliştirilmiştir.Erratum Retraction: Ali Et Al. Finite Element Study of Magnetohydrodynamics (MHD) and Activation Energy in Darcy-Forchheimer Rotating Flow of Casson Carreau Nanofluid (Vol 8, 1185, 2020)(MDPI, 2025) Ali, Bagh; Rasool, Ghulam; Hussain, Sajjad; Baleanu, Dumitru; Bano, Sehrish