Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7148
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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 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 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.Article Citation - WoS: 2Citation - Scopus: 3High-Pressure Processing of Traditional Hardaliye Drink: Effect on Quality and Shelf-Life Extension(MDPI, 2023) Atmaca, Bahar; Demiray, Merve; Akdemir Evrendilek, Gülsün; Bulut, Nurullah; Uzuner, SibelHardaliye, as one of the oldest and lesser known traditional beverages, is produced using red grape pomace from wine production. This drink production is achieved through lactic acid fermentation, with the addition of sour cherry leaves and mustard seeds-either heat-treated, grinded, or whole-in various concentrations. Hardaliye has a very short shelf life; thus, efforts have recently been made to process hardaliye with novel processing technologies in order to achieve shelf-life extension. Therefore, the high-hydrostatic-pressure (HHP) processing of hardaliye was performed to determine its impact on important properties, including in microbial inactivation and shelf-life extension, with respect to a Box-Behnken experimental design. Maximum log reductions of 5.38 & PLUSMN; 0.6, 5.10 & PLUSMN; 0.0, 5.05 & PLUSMN; 0.2, and 4.21 & PLUSMN; 0.0 with HHP were obtained for Brettanomyces bruxellensis, total mesophilic aerobic bacteria, Lactobacillus brevis, and total mold and yeast, respectively. The processing parameters of 490 MPa and 29 & DEG;C for 15 min were found as the optimal conditions, with the response variables of an optical density at 520 nm and the inactivation of L. brevis. The samples processed at the optimal conditions were stored at both 4 and 22 & DEG;C for 228 d. While the non-treated control samples at 4 and 22 & DEG;C were spoiled at 15 and 3 d, the HHP-treated samples were spoiled after 228 and 108 d at 4 and 22 & DEG;C, respectively.Article Citation - WoS: 4Citation - Scopus: 4Optimizing the Dispersion of Calcium Phosphate Nanoparticles for Cellular Studies Using Statistical Design of Experiments(Elsevier, 2023) Önder, Anıl Can; Tomak, Aysel; Öksel Karakuş, CeydaThe in vitro experimentation of ceramic nanoparticles often requires their dispersion in liquid media without causing particle clumps or deteriorating sample integrity. However, the dispersion of nanoparticles using the available protocols rarely leads to stable and uniform dispersions which, in turn, raises concerns about the validity, repeatability and comparability of the findings observed in vitro. Moreover, the ability to control the final dispersion quality of ceramic nanoparticles is an essential step to obtaining optimized nanoceramic materials with desired functionality and to enhancing their performance in subsequent applications. While the need to have a comprehensive guideline for the dispersion of nanoparticles has led to several published documents and protocols, the dispersion methodology of ceramic nanoparticles and the relative contribution of the experimental parameters to the quality of resulting dispersion are still not clear. Here, we employed the statistical design of experiment (DoE) approach to systematically assess the magnitude and source of variation in dispersion quality of two different ceramic nanoparticles, hydroxyapatite and tricalcium phosphate. Using the first-order Plackett-Burman Design (PBD), nanoparticle concentration, pH and the presence of an additive were identified as the most critical factors influencing the resulting hydrodynamic size and zeta potential of the ceramic nanoparticles. Optimization using a second-order Central Composite Design (CCD) yielded a set of quadratic regression equations that were used to predict the hydrodynamic size or zeta potential of ceramic nanoparticles with high accuracy (R2, 0.88–0.92). The results of PBD screening and CCD optimization experiments were employed to prepare nanoparticle dispersions of different quality, which were then used to compare the effect of aggregation on the viability of human osteosarcoma (SaOS-2) cells. Overall, the results of this study provided insight into the role that various experimental parameters play in the colloidal stability and dispersion of ceramic nanoparticles. © 2023Article Citation - WoS: 11Citation - Scopus: 12An Improved Passive Tuned Mass Damper Assisted by Dual Stiffness(Elsevier, 2023) Roozbahan, Mostafa; Turan, GürsoyA tuned mass damper (TMD) is one of the oldest and most commonly used passive control devices attached to structures to absorb lateral loads of energy from main systems. In the last decades, several novel tuned mass dampers have been designed to increase the performance of TMDs in decreasing the structural responses during excitation vibrations. Moreover, several formulations and numerical optimization methods have been developed to optimize the TMDs parameters. This paper proposes a novel passive tuned mass damper with dual stiffness (DSTMD). The DSTMD includes mass, primary and secondary springs, dashpot, and motion limiting chamber. The performance of DSTMDs depends on their properties such as mass, primary and secondary stiffness, damping coefficient, and the length of the motion limiting chamber. Thus, a metaheuristic optimization algorithm, called the Mouth Brooding Fish algorithm, was used to optimize the DSTMDs parameters. The effectiveness of the optimum DSTMD on two different linear ten-story structures under several earthquakes has been studied and compared with the effectiveness of classical optimum TMDs. According to the study, optimum DSTMDs generally show better effects for certain excitations, and as an average performance, they are superior compared to the classical optimum TMDs in reducing maximum displacement of the buildings. At last, structural yielding is considered, and the performance analysis on this structure shows that the DSTMD has a superior effect in reducing the maximum displacement and is among the best methods for the calculated absolute yielding amount.Article Citation - WoS: 27Citation - Scopus: 34Multi-Zone Optimisation of High-Rise Buildings Using Artificial Intelligence for Sustainable Metropolises. Part 2: Optimisation Problems, Algorithms, Results, and Method Validation(Pergamon-Elsevier Science LTD, 2021) Ekici, Berk; Kazanasmaz, Zehra Tuğçe; Turrin, Michela; Taşgetiren, M. Fatih; Sarıyıldız, I. SevilHigh-rise building optimisation is becoming increasingly relevant owing to global population growth and urbanisation trends. Previous studies have demonstrated the potential of high-rise optimisation but have been focused on the use of the parameters of single floors for the entire design; thus, the differences related to the impact of the dense surroundings are not taken into consideration. Part 1 of this study presents a multi-zone optimisation (MUZO) methodology and surrogate models (SMs), which provide a swift and accurate prediction for the entire building design; hence, the SMs can be used for optimisation processes. Owing to the high number of parameters involved in the design process, the optimisation task remains challenging. This paper presents how MUZO can cope with an enormous number of parameters to optimise the entire design of high-rise buildings using three algorithms with an adaptive penalty function. Two design scenarios are considered for quad-grid and diagrid shading devices, glazing type, and building-shape parameters using the setup, and the SMs developed in part 1. The optimisation part of the MUZO methodology reported satisfactory results for spatial daylight autonomy and annual sunlight exposure by meeting the Leadership in Energy and Environmental Design standards in 19 of 20 optimisation problems. To validate the impact of the methodology, optimised designs were compared with 8748 and 5832 typical quad-grid and diagrid scenarios, respectively, using the same design parameters for all floor levels. The findings indicate that the MUZO methodology provides significant improvements in the optimisation of high-rise buildings in dense urban areas.Article Citation - WoS: 22Citation - Scopus: 22Multiparameter-Based Product, Energy and Exergy Optimizations for Biomass Gasification(Elsevier, 2021) Çağlar, Başar; Tavşancı, Duygu; Bıyık, EmrahThe thermodynamic modelling of biomass gasification was studied by using Gibbs free energy minimization approach. Different from the studies using the same approach, the simultaneous presence of all gasifying agents (air, H2O and CO2) was considered and a multiparameter optimization was applied to determine the synergetic effect of gasifying agents for hydrogen, syngas with a specific H2/CO ratio and methane production. The performance of gasification was assessed by using technical and environmental performance indicators such as product yields, cold gas efficiency, exergy efficiency, CO2 emission and the heat requirement of the gasifier. The results show that the simultaneous presence of gasifying agents does not create considerable changes in syngas yield, H2 yield, methane yield, CGE and exergy efficiency while it allows to tune the H2/CO ratio and the heat requirement of the gasifier. The highest syngas yield is observed at T > 1100 K and 1 bar and when SBR > 0.5 and/or CBR > 0.8 with the absence of air, at which CGE changes between 114% and 122% while exergy efficiency is between 77% and 86%. The results prove that CO2 offers several advantages as a gasifying agent and suggests that CO2 recycling from gasifier outlet is a useful option for the biomass gasification.Article Citation - WoS: 38Citation - Scopus: 50Multi-Zone Optimisation of High-Rise Buildings Using Artificial Intelligence for Sustainable Metropolises. Part 1: Background, Methodology, Setup, and Machine Learning Results(Elsevier Ltd., 2021) Ekici, Berk; Kazanasmaz, Zehra Tuğçe; Turrin, Michela; Taşgetiren, M. Fatih; Sarıyıldız, I. SevilDesigning high-rise buildings is one of the complex tasks of architecture because it involves interdisciplinary performance aspects in the conceptual phase. The necessity for sustainable high-rise buildings has increased owing to the demand for metropolises based on population growth and urbanisation trends. Although artificial intelligence (AI) techniques support swift decision-making when addressing multiple performance aspects related to sustainable buildings, previous studies only examined single floors because modelling and optimising the entire building requires extensive computational time. However, different floor levels require various design decisions because of the performance variances between the ground and sky levels of high-rises in dense urban districts. This paper presents a multi-zone optimisation (MUZO) methodology to support decision-making for an entire high-rise building considering multiple floor levels and performance aspects. The proposed methodology includes parametric modelling and simulations of high-rise buildings, as well as machine learning and optimisation as AI methods. The specific setup focuses on the quad-grid and diagrid shading devices using two daylight metrics of LEED: spatial daylight autonomy and annual sunlight exposure. The parametric model generated samples to develop surrogate models using an artificial neural network. The results of 40 surrogate models indicated that the machine learning part of the MUZO methodology can report very high prediction accuracies for 31 models and high accuracies for six quad-grid and three diagrid models. The findings indicate that the MUZO can be an important part of designing high-rises in metropolises while predicting multiple performance aspects related to sustainable buildings during the conceptual design phase. © 2021 The Author(s)Article Citation - WoS: 16Citation - Scopus: 22Optimum Design of Fatigue-Resistant Composite Laminates Using Hybrid Algorithm(Elsevier Ltd., 2017) Deveci, Hamza Arda; Artem, Hatice SeçilIn this study, a fatigue life prediction model termed as Failure Tensor Polynomial in Fatigue (FTPF) is applied to the optimum stacking sequence design of laminated composites under various in-plane cyclic loadings to obtain maximum fatigue life. The validity of the model is investigated with an experimental correlation using the data available in the literature. The correlation study indicates the reliability of FTPF, and its applicability to different composite materials and multidirectional laminates. In the optimization, a hybrid algorithm combining genetic algorithm and generalized pattern search algorithm is used. It is found by test problems that the hybrid algorithm shows superior performance in finding global optima compared to the so far best results in the literature. After the verifications, a number of problems including different design cases are solved, and the optimum designs constituted of discrete fiber angles which give the maximum possible fatigue lives are proposed to discuss. A comparison study is also performed with selected design cases to demonstrate potential advantages of using non-conventional fiber angles in design.