Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

Permanent URI for this collectionhttps://hdl.handle.net/20.500.11779/1926

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Citation - Scopus: 1
Spatial Narratives in Mixed Reality: Immersive Gamified Heritage Experience at Yedikule Fortress
(Education and Research in Computer Aided Architectural Design in Europe, 2025) Gül L.F.; Özer D.G.; Yağmur-Kilimci E.S.; Coşkun E.; Moralioglu B.; Yücel V.; Türkaslan E.
A comparative study was conducted to assess the impact of simple game elements on user experience and awareness of the historical Yedikule Fortress. The objective of this study was to ascertain whether the incorporation of gamified virtual overlays, which were designed to remain historically authentic, contextually grounded, and aligned with the physical environment, had an impact on participants' recollection of the visited space. An iterative development process was employed, commencing with expert evaluations followed by an extensive user study. The developed interface and features of the HoloLens 2 device are detailed, and the results of the user study are presented. Our findings highlight that, in contrast with previous assertions in the literature, there is no statistically significant difference in the impact of game elements on users' experiences of spatial technologies. © 2025, Education and research in Computer Aided Architectural Design in Europe. All rights reserved.
Γ-Type High-Entropy Disilicates (Y0.2er0.2tm0.2yb0.2x0.2)2si2o7 (X = Dy, Gd, Ho): Phase Stability, Thermal Behavior, and CMAS Corrosion Resistance
(Elsevier Ltd, 2026) Kavak S.; Yüksek A.N.; Gökçe H.; Lütfi Öveçoğlu M.; Ağaoğulları D.
Three quinary high entropy disilicate (HEDS) compositions namely, (Y0.2Er0.2Tm0.2Yb0.2Dy0.2)2Si2O7, (Y0.2Er0.2Tm0.2Yb0.2Gd0.2)2Si2O7 and (Y0.2Er0.2Tm0.2Yb0.2Ho0.2)2Si2O7 were synthesized via ball milling and sintering using commercially available oxides for environmental barrier coating (EBC) applications. XRD, SEM, and EDS analyses confirmed the formation of single-phase γ-type pyrosilicates with dense and homogenous elemental distribution. Moreover, it was found that the newly developed (5RE0.2)2Si2O7 high entropy disilicate materials exhibit low thermal conductivities, high temperature phase stability and similar coefficients of thermal expansion (CTE) with SiC. CMAS corrosion resistances of HEDS samples were investigated at 1300 °C for 2, 12, and 24 h. The findings highlight the potential of high entropy engineering to enhance the high-temperature corrosion resistance, high temperature phase stability and improved thermal properties making these materials promising candidates for advanced EBC systems for gas turbine applications. © 2026 Elsevier B.V.
Blume–Capel Model in D=1 With Long-Range Interactions: Giant Reentrance in the Finite-Temperature Tricritical Phase Diagrams
(Elsevier B.V., 2026) Artun E.C.; Berker A.N.
The Blume–Capel model in one dimension with long-range power-law interactions is studied by renormalization-group theory. A series of finite-temperature tricritical phase diagrams is found, as a function of the power-law exponent of the power-law interactions. These calculated phase diagrams exhibit a giant reentrance in the form disorder-order–disorder as temperature is lowered. The first-order transition takes over the entire phase boundary at longest-range interactions, as a near-equivalent-neighbor regime is approached. © 2026 Elsevier B.V.
Churn Prediction for Subscription-Based Applications Using Machine Learning
(Institute of Electrical and Electronics Engineers Inc., 2025) Gozukara H.; Patel J.; Kara E.; Yildiz A.; Mese Y.K.; Obali E.; Cakar T.
In this study, a predictive model was developed using machine learning techniques to forecast customer churn in subscription-based video streaming services. The data such as user login records, content viewing information, subscription details, and content-related features were used to interpret usage patterns and customer churn was defined based on subscription renewal status and renewal timing. Several usage-based features are extracted for users and several algorithms were used for modeling, such as Random Forest, CatBoost, XGBoost, Logistic Regression, K-Nearest Neighbors, and Gradient Boosting. Occurring class imbalance on the target variable is handled via BorderLineSMOTE. The model's performance was evaluated using training-test accuracy plots, classification reports, and hyperparameter tuning. Even though most of the models performed similarly, the CatBoost model emerged as the top performer, achieving a macro F1-score of 0.60. However, while effective in identifying churners, the models struggled to precisely classify non-churning customers, a common challenge in imbalanced datasets even after applying oversampling techniques. The analysis of feature importance yielded a crucial insight, early and consistent user engagement is the strongest predictor of customer retention. These findings provide valuable, actionable insights for streaming platforms, emphasizing that retention strategies should focus on maximizing engagement immediately after a user subscribes. © 2025 IEEE.
Attention-Enhanced Dual-Head LSTM With Rich Feature Engineering for Risk-Adjusted Stock Return Forecasting
(Institute of Electrical and Electronics Engineers Inc., 2025) Patel J.; Gunes P.; Ertugrul S.; Sayar A.; Benli H.; Makaroglu D.; Cakar T.
Stock return forecasting is a challenging task due to the complex, nonlinear, and volatile nature of financial markets. In this paper, we propose a comprehensive deep learning framework that integrates: a two-layer Long Short-Term Memory (LSTM) network augmented with a learnable attention mechanism, a dual-head output for simultaneous regression of next-day returns and classification of price direction, with an extensive suite of technical and macro-financial features. Our feature set comprises lagged log-returns, trend indicators (simple and exponential moving averages), momentum oscillators (RSI, MACD), volatility measures (rolling variance and GARCH conditional volatility), price bands (Bollinger Bands, Donchian channels), volume metrics (On-Balance Volume, Volume Rate of Change), Hidden Markov Model regime states, market index returns, and calendar effects. We train and validate the model using a rolling-window cross-validation scheme with early stopping and hyperparameter tuning to ensure temporal robustness. Empirical results on a large multi-stock dataset demonstrate that our attention-enhanced, dual-task LSTM outperforms single-task LSTMs and traditional machine learning benchmarks, achieving lower forecasting error and more stable generalization. © 2025 IEEE.
A Predictive Model for Bounced Check Risk Using Machine Learning
(Institute of Electrical and Electronics Engineers Inc., 2025) Kaya K.; Sayar A.; Memis E.C.; Ozlem S.; Ertugrul S.; Cakar T.
Bounced checks result in direct monetary losses. Traditional rule-based systems cannot adapt to new patterns and lack flexibility. In this study, we used a large and imbalanced check dataset with customer profiles, credit limits, and historical check outcomes. We applied feature engineering emphasizing time-based transaction patterns, extensive clustering, anomaly detection, and inflation adjustment. We trained six models each for two datasets, which are undersampled to handle class imbalance: Logistic Regression, Random Forest, XGBoost, LightGBM, Extra Trees, and CatBoost. The best performing model, CatBoost, achieved macro F1 scores of 88.5 percent on individual checks dataset with a gross sunk rate of 4.92 percent, and 91.7 percent on corporate checks dataset with a gross sunk rate of 4.28 percent. These results show the model can identify checks most likely to bounce before granting and maintain a low gross sunk rate overall. This study presents a data-driven machine learning solution that enables financial companies to predict and prevent bounced checks before they occur. © 2025 IEEE.
Rag Based Interactive Chatbot for Video Streaming Services
(Institute of Electrical and Electronics Engineers Inc., 2025) Gözükara H.; Patel J.; Kara E.; Yildiz A.; Köseoǧlu O.; Makaroǧlu D.; Çakar T.
The proliferation of content within video streaming services presents a significant challenge for users seeking personalized recommendations and specific information. This research addresses this challenge by developing a Retrieval-Augmented Generation (RAG) chatbotn designed to enhance user experience through conversational AI. The primary contribution of this work is a novel Retrieval-Augmented Generation (RAG) architecture featuring a dual-retrieval system that combines semantic search for descriptive requests and structured queries for fact based inquiries. This approach grounds the Large Language Model (LLM) in a factual knowledge base, mitigating the risk of hallucinations. The system is engineered to handle empty data retrieval scenarios by dynamically relaxing search filters, ensuring a robust user experience. The effectiveness of this RAG approach was validated through a comprehensive set of automated evaluations. The system demonstrates high precision in ranked list retrieval with questions like "Recommend me the top 5 action movies with highest IMDb scores", achieving an average NDCG@k of 0.837. While the chatbot shows strong semantic understanding by achieving 91% accuracy with contextual clues such as "Which Batman movies are directed by Christopher Nolan?", its performance with more ambiguous, plot-only queries (59.5% accuracy) indicates clear opportunities for future refinement. These results confirm that the dual-tool architecture successfully combines the flexibility of semantic search with the precision of structured queries, paving the way for more intuitive and efficient content discovery on streaming platforms. © 2025 IEEE.
Combining Similar Trajectories and XGBoost via Residual Learning for Traffic Flow Forecasting
(Institute of Electrical and Electronics Engineers Inc., 2025) Işlak U.; Yilmaz E.; Arslan I.; Çakar T.
In this study, we propose novel hybrid forecasting models that integrate the method of similar trajectories with machine learning techniques, particularly the XGBoost algorithm, for traffic flow prediction. Traditional statistical models, such as ARIMA, often struggle to accurately capture the complex, non-linear patterns characteristic of traffic flow data. To address these limitations, we develop an additive hybrid forecasting framework that combines the strengths of linear models (similar trajectories method) and non-linear models (XGBoost). Our proposed methods are evaluated on two different stations from the California PEMS dataset. Experimental results demonstrate that the proposed hybrid models consistently outperform individual benchmark models, including ARIMA, standalone similar trajectories, and XGBoost. The superiority of the hybrid approach, particularly the XGBST model, is further validated through the Diebold-Mariano statistical test, confirming significant predictive improvements at various significance levels. Additionally, using weighted Euclidean distance within the similar trajectories method further enhanced forecasting accuracy. The interpretability and flexibility of our hybrid framework make it especially suitable for practical implementation in traffic management systems. These findings underline the effectiveness of hybrid modeling strategies in traffic flow forecasting and suggest future research directions, such as comprehensive hyperparameter optimization and broader validation across diverse datasets. © 2025 IEEE.
A Multimodal AI and ML Framework for Fashion Image Segmentation, Recommendation, and Similarity Recognition
(Institute of Electrical and Electronics Engineers Inc., 2025) Soyhan M.E.; Ay T.B.; Memis E.C.; Fatih Capal M.; Cakar T.; Gunay S.; Coskun H.
This study presents a scalable multimodal Artificial Intelligence (AI) and Machine Learning (ML) framework designed to enhance decision making in the fashion industry. The proposed system integrates garment segmentation, multimodal feature extraction, and similarity recommendation into a unified pipeline. Using Segformer for segmentation, along with the convolutional neural network (CNN)-based feature extraction models ResNet152V2 and Xception, and the transformer-based vision-language model LLaVA, the framework generates visual and semantic embeddings of garments. These representations are processed through similarity detection using OpenAI embedding models and stored in the Pinecone vector database for efficient retrieval. Real-time similarity scoring is enabled through FastAPI endpoints, offering interactive search capabilities. Preliminary results demonstrate the system's strong ability to identify conceptually and visually similar items across a large catalog, providing actionable insights for designers. This framework lays the groundwork for intelligent, interpretable, and production-ready AI systems in the fashion industry. © 2025 IEEE.
Predicting Customer Churn in Retail Using Machine Learning on Transaction Data
(Institute of Electrical and Electronics Engineers Inc., 2025) Bozan M.T.; Gozukara H.; Patel J.; Kizilay A.; Sahin Z.; Tosun B.; Cakar T.
Customer churn prediction is critical for businesses to retain customers and reduce revenue loss. This paper presents a retail customer churn prediction study. We preprocess transactional data from a retail dataset comprising approximately 19.7 million transactions involving over 1 million customers. Temporal behavioral features, such as purchase frequency, monetary value, product variety, and promotional engagement metrics, are engineered using a four-month observation window. A Random Forest classifier is trained, utilizing balanced class weighting to address churn class imbalance. The churn label is defined as customers not purchasing in the subsequent six-month period. Our Random Forest model achieves approximately 84% accuracy, 86% precision, 85% recall, and an F1- score of 85%. Additionally, an XGBoost model achieves similar accuracy (≈ 84%) but higher recall (93%) and F1-score (89%), indicating improved churn prediction. The confusion matrix illustrates clear model performance. This study demonstrates that carefully engineered RFM-based features and ensemble learning approaches significantly enhance churn prediction in retail contexts. © 2025 IEEE.
Multi-Output Vs Single-Output Deep Learning for Plant Disease Detection
(Institute of Electrical and Electronics Engineers Inc., 2025) Taha Kara H.B.; Sayar A.; Gunes P.; Guvencli M.; Ertugrul S.; Cakar T.
AI-based image processing plays a crucial role in agriculture by enabling early detection of plant diseases, thereby increasing crop productivity and minimizing economic losses. In this study, we present a comparative analysis between a multi-output deep learning model, which simultaneously classifies plant species and assesses their health status, and two separate single-output models trained for each task individually. The publicly available PlantVillage dataset was used for training and evaluation. Performance metrics such as classification accuracy, F1 score, training time, and confusion matrices were used to assess each model. Our results indicate that the multi-output architecture achieves remarkably high classification performance (Plant: 99.98%, Health: 99.78%) while significantly reducing training time by nearly 50% compared to the combined cost of training two individual models. This demonstrates that a unified model not only provides computational efficiency but also maintains predictive strength, making it a practical alternative for real-time agricultural decision support systems. The findings suggest that integrated modeling can contribute to the development of scalable, resource-efficient solutions in precision agriculture. © 2025 IEEE.
Graph Theory-Based Fraud Detection in Banking Check Transactions
(Institute of Electrical and Electronics Engineers Inc., 2025) Behsi Z.; Memis E.C.; Ertugrul S.; Sayar A.; Gunes P.; Seydioglu S.; Cakar T.
Traditional banking fraud detection systems rely on rule-based approaches that analyze individual transactions in isolation, failing to capture complex relationship patterns indicative of coordinated fraud schemes such as check-kiting and artificial credit score manipulation. We p resent our study, a novel similarity-based graph theory approach that constructs weighted networks between check issuers using Jaccard Similarity Index and employs advanced graph analysis to identify suspicious entity clusters without requiring complete transaction relationship data. Our approach combines Jaccard Similarity Index for behavioral pattern analysis (addressing payee information unavailability) with comprehensive graph analysis including centrality measures, community detection, and anomaly identification. Through comprehensive evaluation on real banking data containing 458,399 transactions from 121,647 unique issuers - the largest confirmed dataset in fraud detection literature - we demonstrate the effectiveness of our methodology. Following parameter optimization using grid search methodology (similarity threshold: 0.55, risk percentile: 0.75), our study achieves competitive detection rates in optimal configurations with an average F1-score of 0.447 (±0.164) and peak performance reaching an F1-score of 0.557, while providing superior network topology analysis with 0.923 clustering coefficient. The system operates under significant data privacy constraints, lacking personal identification information (names, account numbers, IDs) and complete payee data. Despite these limitations, our study outperforms traditional approaches by leveraging similarity-based indirect relationships, and we project that performance could reach 85-95% levels with complete data access. © 2025 IEEE.
Developing Autonomous Steering Algorithm To Improve Cornering Slip Performance of a Four-Wheel Car Using Neural Network Tools
(Institute of Electrical and Electronics Engineers Inc., 2025) Alatciyan D.R.; Emeryan B.J.; Barbaros B.; Cakar T.; Kilic N.
This study investigates a neural network-based predictive steering control using simulation data generated from ADAMS Car. A Long Short-Term Memory (LSTM) architecture is employed to estimate steering angle and longitudinal velocity from sequential input features, with the goal of analyzing the model's behavior in cornering scenarios. The experimental setup includes multiple simulation runs under varying configurations, particularly exploring the effect of different sliding window sizes on prediction performance. Results show that the proposed model can effectively capture temporal patterns in the input data and produce consistent estimations across test conditions. While the study is limited to a simulation environment, it provides initial insights into how AI-based models may support steering control tasks and lays the groundwork for future extensions involving additional vehicle dynamics inputs. © 2025 IEEE.
Empowering Electric Vehicle Adoption: Innovative Strategies for Optimizing Charging Station Placement Based on Projected Demand
(Wiley, 2025) Cekyay, Bora; Kabak, Ozgur; Ozaydin, Ozay; Isik, Mine; Toktas-Palut, Peral; Topcu, Y. Ilker; Ulengin, Fusun
Electric vehicles (EVs) are pivotal for reducing transportation-related emissions; however, the lack of adequate charging infrastructure remains a significant barrier to their widespread adoption. This study presents a comprehensive methodology for optimizing EV charging station placement. It combines a gravity model, scenario analysis, and mixed-integer linear programming (MILP) to ensure a thorough and robust approach. The model aims to maximize accessibility by ensuring both path-level and overall system demand coverage across diverse scenarios, providing reassurance about the validity of the findings. The methodology is tested on the Bursa-& Idot;zmir motorway in Turkey, a strategic intercity route with rapidly growing EV penetration. Results reveal that the optimal configuration involves locating charging stations in seven of the nine service areas. This allocation secures a minimum path coverage ratio of 0.903, meaning 90.3% of the route is covered by charging stations, and an overall demand coverage ratio of 0.935, indicating that 93.5% of total demand is covered across all scenarios. A sensitivity analysis further shows that increasing the network to 45 chargers elevates reachability levels to above 97%, indicating the infrastructure scale required for reliable service quality. The findings underscore the practical applicability of the proposed framework, providing policymakers and infrastructure planners with robust, data-driven guidance for charging network expansion. By integrating demand forecasting with resilient optimization, this study advances both methodological and empirical insights, empowering the audience to make informed decisions for sustainable EV adoption.
A Comparative Study of Branch-And Algorithms for Vehicle Routing With Time Windows and Waiting Time Costs
(Wiley, 2026) Michelini, Stefano; Kucukaydin, Hande; Arda, Yasemin
Branch-and-price is one of the most commonly used methodologies for solving routing problems. In recent years, several studies have investigated advanced labeling algorithms to solve the related pricing problem, which is usually a variant of the elementary shortest path problem with resource constraints. Such algorithms include efficient techniques such as decremental state space relaxation, ng-route relaxation, and several hybridizations of these two relaxation methods. In this study, we compare the performance of these labeling algorithms in a branch-and-price framework when applied to the vehicle routing problem with time windows and a variant of this problem in which waiting times have a linear cost. For the latter problem, we also propose an appropriate label structure with associated resource extension functions and dominance rules. We perform these comparisons by using a rigorous methodology, which consists of parameterizing several features of these algorithms, obtaining a good parameter configuration for each algorithm, and analyzing the performance of these configurations on benchmark instances. In order to obtain good configurations, we make use of irace, which is a tool for automated parameter tuning, while statistical tests are used for performance comparisons. Our results show that a class of hybrid algorithms with certain features based on ng-route relaxation outperforms all the others.
The Relational Nature of Punishment: Responses To Close Versus Distant Others' Moral Transgressions
(Sage Publications Inc, 2026) Tepe, Beyza; Faber, Nadira S.
How do people respond when a close other, as opposed to a distant other, commits a moral transgression against a third person? Across five preregistered experiments (total N = 2,170), supplemented by pilot studies, we find that people navigate punishment differently depending on relational closeness: they seek less punishment by authorities (institutional punishment) for close others but impose more punishment by themselves (relational punishment) and are more likely to confront the perpetrator directly (Experiments 1-5). Moreover, transgressions of close others elicit both other-blaming and self-blaming emotions, and they prompt individuals to adopt both victim and perpetrator roles (Experiments 2-5). These effects intensify with increasing relational closeness (Experiment 3) and persist across transgressions of varying moral and criminal severity (Experiment 4).
Techno-Philosophical and Techno-Pedagogical Implications of a Nonformal Technology and Design Education Model to Empower Youth: T3 Foundation's Deneyap Technology Workshops Program
(Springer, 2026) Bulut, Mehmet Akin; Kocoglu, Merve; Bas, Fatma Ruveyda; Gulunay, Oksana; Birgili, Bengi
This mixed-methods analysis explores the DENEYAP Technology Workshops program, launched by the T3 Foundation in 2017, which aims to develop scientific thinking and problem solving at the intersection of teaching technology skills and design thinking among 4th- and 9th- graders through nonformal learning. The study sought to investigate the program's techno-philosophical and techno-pedagogical formation. Data collection involved qualitative interviews with founders (n = 20) and program developers (n = 20). Additionally, to provide a comprehensive understanding of the program from multiple perspectives, lesson plans (n = 11) were analyzed to assess the curriculum, whereas classroom observations (n = 5) offered insights into instructional methods and learner engagement. The findings obtained through theories such as technology, pedagogy and content knowledge; technology philosophy; and design thinking reveal that the harmony between leaders' and instructional teams' visions, and the presence of a solid techno-philosophy in a technology and design education program lead to considerable success; the program's collaboration with official and unofficial institutions provides incalculable benefits; empathizing (needs analysis) stage at design cycle is crucial and yields critical insights; and the program fosters interest and competency in techno-scientific thinking skills among learners. Conversely, indicating areas in need of improvement in the program, continuous trainer professional development is pivotal; infrastructure and material provision are essential, and there is a lack of quality assurance in assessment practices, in other words, the test stage at design cycle. This study of the innovative, practical and skills-based program points to the critical role of nonformal learning in preparing the next generation for a technology-driven future through the intersection of technology and design education immersed in a strong and rigorous techno-philosophical and techno-pedagogical design.
Seismic Behavior and Design of Reinforced Autoclaved Aerated Concrete Load-Bearing Panel Walls
(Taylor & Francis Ltd, 2026) Ugurlu, Koray; Halici, Omer Faruk; Demir, Cem; Comert, Mustafa; Ilki, Alper
Since the 1970s, numerous low-rise buildings in T & uuml;rkiye constructed with AAC load-bearing panels have withstood devastating earthquakes without significant damage, demonstrating a lightweight yet robust solution for seismic regions. This study investigates the seismic performance of AAC load-bearing panel wall systems through material tests, member-level cyclic in-plane testing, and finite element micro-modeling. The experimental results showed that individual panel behavior initiated at low lateral drift ratios of 0.25-0.50%, accompanied by measurable uplift and rocking at panel bases, with flexure governing failure in two-panel walls and combined flexure and diagonal tension - shear governing failure in four- and six-panel walls. Numerical models exhibited adequate reliability in terms of strength, stiffness, and cumulative energy, when validated against experimental data. The load-bearing capacity in the numerical simulations increased with both the number of panels and higher axial loads, consistent with observed experimental trends. These combined findings were used to determine seismic design factors leading to recommended values of D = 2 for overstrength and R = 4 for structural behavior. Experimental results were compared with corresponding design documents, including ACI 523.4 R and the Turkish Building Earthquake Code (TBEC). The findings indicated that flexure predominantly governed the failure of two-panel walls, while combined flexure and diagonal tension - shear mechanisms governed the failure of four- and six-panel walls. Accordingly, a revised diagonal tension capacity expression is proposed for the seismic design of AAC systems in future versions of TBEC.
A Discourse Analysis of Bilateral Water Agreements Between Türkiye and Iraq: Legal Instruments of Water Diplomacy in the Euphrates-Tigris River Basin
(International Environmental Agreements: Politics, Law and Economics, 2026) Güleç, Cansu; Kibaroglu, Aysegul
This study examines the discursive dynamics of bilateral water diplomacy between T & uuml;rkiye and Iraq through a detailed analysis of the legal agreements governing the Euphrates-Tigris (ET) River system. Rather than focusing on the implementation or efficacy of these agreements, the paper investigates how discourse shapes the roles, identities, and power hierarchies of the involved actors over time. Employing a discourse-analytical framework, the research explores how water agreements position actors, embed values, and narrate cooperation in evolving geopolitical contexts. The paper begins with a historical overview of transboundary water relations in the ET basin, emphasizing the prevalence of bilateralism. It then lays out the conceptual and methodological foundations of discourse analysis, drawing on key literature and analytical categories such as presupposition, predication, and subject positioning. The core section applies this framework to four key water agreements between T & uuml;rkiye and Iraq, highlighting thematic shifts and evolving actor roles. A discussion section synthesizes findings through Doty's (1993) discourse model, emphasizing how identities and relations are constructed over time. Finally, the conclusion reflects on the implications of these discursive trends for the future of water diplomacy in the region. The T & uuml;rkiye-Iraq case reveals how bilateral agreements can evolve into discursive tools that align with evolving global water management paradigms, offering politically sensitive basins a transferable approach to linking contested transboundary water issues with more comprehensive and partnership-based water diplomacy.
Comparison of the Observed and Numerical Performance of a Seismic Isolated Hospital
(International Association for Earthquake Engineering, 2024) Şadan, B.; Sahin, B.; Tüzün, C.; Demircioglu-Tumsa, M.B.; Erdik, M.
The southeastern region of Turkiye was struck by powerful earthquakes on February 6, 2023, with magnitudes measuring M7.7 and M7.6. These earthquakes resulted in significant damage, destruction, and loss of life, affecting both TÃ¼rkiye and extending into northern Syria. Among the approximately 100 isolated buildings in Turkiye, 11 isolated hospitals were located in the impacted region. This paper presents a comprehensive assessment of the seismic performance of the seismically isolated Osmaniye State Hospital during the Kahramanmaras earthquakes. The evaluation comprises site observations and numerical analysis utilizing ground motion records obtained from the nearest accelerometers. Site observations involved monitoring the movements of the isolators located at the moats around the perimeter of the hospital. Using a borescope allowed for detailed internal inspection of the isolation bearings, facilitating close-up examination of the isolators and enabling the identification of movement scuff marks on the sliding surfaces. These observations were used to estimate the maximum isolation bearing movement and determine the residual displacement offset of the bearings. The observed displacements of the isolation system were compared with the design values to assess the performance of the seismically isolated structure. Discrepancies between the observed and designed displacements provide valuable insights into the actual behavior of the isolation system. A nonlinear time history analysis was conducted using ground motion records obtained from the nearest accelerometers to further analyze the seismic response. This numerical analysis allowed for the simulation of earthquake excitations and the evaluation of the dynamic behavior of the seismically isolated Osmaniye State Hospital. The combination of site observations and numerical analysis yielded important findings regarding the seismic performance of the seismically isolated Osmaniye State Hospital during the Kahramanmaras earthquakes. The comparison between observed and design displacements provided insights into the efficacy of the isolation system, while the numerical analysis further validated the structural response. These findings contribute to improving the design and implementation of seismically isolated structures. © 2024, International Association for Earthquake Engineering. All rights reserved.

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