Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7148
Browse
15 results
Search Results
Article AI-Supported Seismic Performance Evaluation of Structures: Challenges, Gaps, and Future Directions at Early Design Stages(Elsevier Sci Ltd, 2026) Ak, Fatma; Ekici, Berk; Demir, UgurThis study reviews 91 journal articles that intersect with earthquake-resistant building design and artificial intelligence (AI)- based modeling, utilizing machine learning, deep learning, and metaheuristic optimization algorithms. Previous reviews on AI applications have examined engineering problems without considering the impact of architectural design parameters and structural irregularities on seismic performance. This review discusses the role of AI in integrating architectural design variables and seismic performance objectives, highlighting challenges, gaps, and future directions in the early design phase. The reviewed articles demonstrate that AI is successful in addressing seismic performance objectives; however, a holistic framework for assessing architectural and structural variables has not been presented. The review highlights key findings, gaps, and future directions for those involved in earthquake-resistant building design utilizing AI.Article Artificial Intelligence for Improving Thermal Comfort through Envelope Design in Residential Buildings: Recent Developments and Future Directions(Elsevier Science Sa, 2026) Bayraktar, Arda; Ekici, BerkEnvelopes are vital components for improving thermal comfort in almost all building typologies. Yet, the design and analysis of envelopes are complex, as they involve multiple aspects and various parameters, ensuring comfort standards. Improving thermal comfort in residential buildings is within the scope of researchers to suggest sustainable design alternatives that consider multiple performance aspects and design parameters. Previous review articles have focused on improving thermal performance in residential buildings from the perspective of envelope technology, materials, and design strategies. However, none of them investigated current developments using artificial intelligence (AI), which inevitably supports decision-making in complex circumstances for a sustainable built environment. This review examines the contribution of AI methods, which consist of metaheuristic optimization and machine learning algorithms as sub-branches, to envelope parameters. The paper systematically reviews 95 relevant works on AI, including early approaches, to provide a comprehensive overview of current developments, following PRISMA guidelines. The results showed that early applications considered conventional approaches to improve thermal comfort and energy performance, which mostly limit the results to specified cases. On the other hand, studies utilizing AI methods dealt with numerous parameters, allowing them to cope with complex envelope systems in a reasonable amount of time. The study addresses relevant research questions related to the trends, research methods, system types, AI methods, data types, and their relation to performance and envelope parameters. The study also provides actionable insight, underlining gaps and future works for utilizing machine learning methods in the reviewed research domain.Conference Object Developing Machine Learning Models to Predict Outdoor Thermal Comfort of Kinetic Shading Devices: An Approach for Global Optimization(Education and Research in Computer Aided Architectural Design in Europe, 2025) Dağlier, Y.; Ekici, B.; Korkmaz, K.Utilizing artificial intelligence (AI) methods in the design process supports the achievement of sustainable alternatives during the conceptual design. In various AI methods, optimization and machine learning (ML) algorithms are the most common methods to develop predictive models and discover favorable design alternatives with significantly reduced computational time. Recent works focused on limited datasets, as well as the evaluation of the developed prediction models based on collected data. During the optimization process of complex design problems, the number of design parameters becomes enormous; thus, search areas contain many design alternatives that might lead the search outside of the collected data. Therefore, evaluating the accuracy of prediction models only based on the collected samples may result in scenarios where the predicted outcome during the optimization process aligns with an unrealistic solution. This study investigates how accurately prediction models developed using different ML algorithms can perform in optimization processes. The proposed framework is used to cope with outdoor thermal performance, considering kinetic shading devices with rigid origami techniques. A parametric shading device model with kinematic principles and 10 design parameters is created in Grasshopper 3d. LadyBug is used to analyze the performance of the universal thermal climate index (UTCI). To minimize the UTCI, the radial basis function optimization (RBFOpt) algorithm in the Opossum plugin is used. To compare the optimization results with the prediction results, multiple linear regression, support vector machines, random forest, polynomial regression algorithms, and artificial neural networks (ANN) are developed to predict outdoor thermal comfort performance targets on each collected data set with 2000 samples. Results showed that ANN models can provide more accurate predictions during the optimization process. The paper aims to discuss the way ML algorithms are applied and evaluated for ML-based optimization domains in design problems. © 2025, Education and research in Computer Aided Architectural Design in Europe. All rights reserved.Article Comparative Optimization of Hot Water and Citric Acid Extraction Methods for Sericin Recovery From Silk Cocoons: In Vitro Antioxidant and Antidiabetic Activities(Springer, 2026) Sincar, Bahar; Ozdemir, Feyza; Bicakci, Beyza Tutku; Erdem, Cansu; Yalcin, Dilek; Alamri, Abdulhakeem S.; Bayraktar, OguzSilk sericin, a hydrophilic protein derived from Bombyx mori cocoons, has attracted increasing interest due to its antioxidant, moisturizing, and enzyme-inhibitory properties. Efficient extraction is essential to preserve its biofunctional potential. In this study, sericin was extracted using hot water and 1.25% (w/v) citric acid using autoclave-based heating to achieve pressurized conditions above 100 degrees C. A Box-Behnken Response Surface Methodology (RSM) was applied to systematically evaluate the effects of extraction parameters (temperature and time) and to optimize five key response variables: yield, purity, molecular weight and polydispersity index (PDI), total antioxidant capacity (ABTS), and alpha-glucosidase inhibition activity. The results revealed that higher temperatures (125 degrees C) produced the maximum sericin yield, while moderate conditions (115 degrees C for 45 min) ensured better preservation of antioxidant and antidiabetic activities. Hot acid extraction resulted in significantly enhanced purity and enzymatic inhibition compared to hot water extraction. Sericin fractions above 7 kDa exhibited the strongest bioactivity, as reflected by lower IC50 values in both ABTS and alpha-glucosidase inhibition assays. The optimized hot water citric acid-based method yielded 24.00% sericin with 100.00% purity and an IC50 of 0.67 mg/mL for alpha-glucosidase inhibition. This study compares hot water and hot acid autoclave extractions using Box-Behnken design and evaluates their effects on sericin yield, purity, and bioactivities. Citric acid-based extraction produced higher purity and stronger alpha-glucosidase inhibition, while hot water extraction preserved antioxidant potential more effectively. These findings support the use of citric acid as an eco-friendly and scalable extraction agent and highlight the potential of sericin in biomedical and nutraceutical applications.Article A Novel ORC-PEM Integrated System for Sustainable Hydrogen Production from Low-Grade Waste Heat in Oil Refineries(Elsevier, 2025) Nazerifard, Reza; Mohammadpourfard, Mousa; Zarghami, RezaThis study presents an integrated multi-generation system for sustainable hydrogen production by harnessing low-grade waste heat from the overhead stream of the NHT unit's stripper column in an oil refinery. The proposed system integrates an ORC with a PEM electrolyzer, forming a novel energy solution that efficiently converts waste heat into clean hydrogen through electricity generation. A detailed model of the proposed system is developed, enabling a comprehensive assessment of its performance from thermodynamic, economic, and environmental viewpoints. At the same time, key operational parameters are optimized using the RSM-BBD method to minimize the hydrogen production cost, thereby enhancing the system's economic viability and practical implementation. The results demonstrated that the system achieves a yearly hydrogen production of 304.53 tons under optimized conditions, for 2.36 $/kg. The integrated system's overall energy and exergy efficiencies are calculated at 8.62 % and 33.43 %, respectively, demonstrating its high thermodynamic performance. Additionally, the system mitigates 3047 tons of CO2 annually by displacing conventional hydrogen production methods.Conference Object Optimized Cooperative Routing for Autonomous Vehicles(Institute of Electrical and Electronics Engineers Inc., 2025) Saydam, B.; Ayav, T.Current traffic control systems - comprising traffic lights, signs, and right-of-way rules - are often inadequate, leading to accidents, excessive fuel consumption, and unnecessary delays. Three key scenarios contribute to these inefficiencies. First, drivers may run red lights due to a lack of traffic signal timing information, leading to indecision when encountering a yellow light, a major cause of accidents. Second, abrupt speed changes in response to traffic signals force drivers to brake suddenly, increasing fuel consumption and travel time. For instance, a driver may accelerate at a green light only to encounter a red light shortly after, resulting in inefficient fuel use. Lastly, vehicles often remain stopped at red lights despite no cross-traffic, leading to wasted fuel and time.This study simulates these scenarios using the Eclipse SUMO tool, with results aligning with expected inefficiencies. The problem is mathematically modeled using Pyomo, and a centralized optimization approach is applied to enhance traffic synchronization and efficiency. By dynamically calculating vehicle velocities based on real-time traffic data, the study proposes an optimized, traffic light-free system. The results demonstrate improved fuel efficiency, reduced accidents, and minimized delays, highlighting the potential of centralized optimization in modern traffic management. © 2025 Elsevier B.V., All rights reserved.Article Citation - WoS: 4Citation - Scopus: 4Optimization of Resource-Aware Parallel and Distributed Computing: a Review(Springer, 2025) Czarnul, Pawel; Antal, Marcel; Baniata, Hamza; Griebler, Dalvan; Kertesz, Attila; Kessler, Christoph W.; Rakic, GordanaThis paper presents a review of state-of-the-art solutions concerning the optimization of computing in the field of parallel and distributed systems. Firstly, we contribute by identifying resources and quality metrics in this context including servers, network interconnects, storage systems, computational devices as well as execution time/performance, energy, security, and error vulnerability, respectively. We subsequently identify commonly used problem formulations and algorithms for integer linear programming, greedy algorithms, dynamic programming, genetic algorithms, particle swarm optimization, ant colony optimization, game theory, and reinforcement learning. Afterward, we characterize frequently considered optimization problems by stating these terms in domains such as data centers, cloud, fog, blockchain, high performance, and volunteer computing. Based on the extensive analysis, we identify how particular resources and corresponding quality metrics are considered in these domains and which problem formulations are used for which system types, either parallel or distributed environments. This allows us to formulate open research problems and challenges in this field and analyze research interest in problem formulations/domains in recent years.Article Citation - WoS: 1Citation - Scopus: 1Effect of Soil Water Content Changes on the Behavior of Buildings Equipped With Single and Double Tuned Mass Dampers Subjected To Earthquakes(Springer Science and Business Media Deutschland GmbH, 2025) Roozbahan, M.; Turan, G.Tuned mass dampers (TMDs) are one of the structural control systems that have been frequently used in the last century. A TMD is designed according to the properties of the main system. In building applications, the substructure’s soil affects the response of buildings, especially in soft-type soils. Therefore, the soil properties should be included in the analysis and design of tuned mass dampers. However, the soil properties are not always identical and vary due to different factor changes such as soil water content changes. Unlike previous research, which typically assumes constant soil properties, this study incorporates the impact of soil water content changes, a key factor that can significantly alter soil behavior. This study aims to evaluate the effectiveness of optimized single and double tuned mass dampers (DTMDs) in response reduction of buildings considering the changes in the water content of soil. In this study, a metaheuristic-based optimization method is programmed to optimize TMDs and DTMDs for low-, mid-, and high-rise buildings considering soil-structure interaction (SSI). The efficiency of the optimized tuned mass dampers on the response reduction of buildings due to changes in soil water content is evaluated. According to the investigated results of 14 near-field earthquake simulations, it is concluded that the efficiency of the TMDs is significantly affected by changes in soil water content. Moreover, according to the result, the DTMD efficiency is slightly better than the TMD-controlled structure. © Springer Nature Switzerland AG 2025.Article Citation - WoS: 3Citation - Scopus: 3Shelf-Life Extension of Traditional Licorice Root “sherbet” With a Novel Pulsed Electric Field Processing(Frontiers Media S.A., 2023) Akdemir Evrendilek, Gulsun; Demir, Irem; Uzuner, SibelPulsed electric field (PEF) processing of licorice root "sherbet" (LRS) by various electric field strengths (7.00, 15.50, and 24.10 kV/cm), treatment times (108, 432, and 756 mu sec), and processing temperatures (6, 18, and 30 degrees C) according to the Box-Behnken design were performed. The samples were analyzed for pH, titratable acidity, conductivity, turbidity, total reducing sugar, color (L*, a*, and b*), hue, chroma, total color difference, color intensity, color tone (yellow, red, and blue color tones), total antioxidant capacity, total phenolic substance content, and sensory properties. Results revealed that PEF processing did not adversely affect most of the physical, chemical, and sensory properties of LRS, with a maximum of 2.48, 4.04, 1.78, and 1.20 log reductions on the initial total mesophilic aerobic bacteria, total mold and yeast, Bacillus circulans, and Candida tropicalis. The response variable modeled for the PEF was found to be conductivity, with the optimum processing conditions of 6.90 kV/cm, 756.00 mu s, and 7.48 degrees C. After that, the samples were stored at 4 degrees C and 22 degrees C for shelf-life studies. Control samples at 4 degrees C and 22 degrees C were spoiled on the fifth and second days, whereas PEF-treated samples stored at 4 degrees C began to deteriorate after the 40th day and the samples stored at 22 degrees C after the 30th day, respectively. It was revealed that PEF is a suitable process to extend the shelf-life of licorice "sherbet" with preservation of physicochemical and sensory properties.Article Phase Shift Optimization for Ris Enabled Pnc System With Multiple Antennas(Ieee-inst Electrical Electronics Engineers inc, 2024) Ilguy, Mert; Ozbek, Berna; Musavian, Leila; Mumtaz, ShahidReconfigurable intelligent surfaces (RIS) have been developed to exploit the stochastic characteristics of the propagation environment for next generation wireless systems. On the other hand, the integration of wireless physical network coding (PNC) and multiple antennas yields notable enhancements in system performance. This paper presents a multiuser system, employing RIS enabled PNC alongside multiple antennas to minimize both delay and error probability. Our aim is to establish reliable communication between the user pairs, which communicate through a base station (BS) via RIS. Therefore, the reflecting coefficients including both phases and amplitudes of the RIS are optimized by using the alternating direction method of multipliers (ADMM) algorithm for both single and multiple RIS cases. Extensive results are presented to compare the proposed algorithm with random phase shift, network coding (NC) and the search algorithm to illustrate its superiority.