Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7148
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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.Article Citation - WoS: 7Citation - Scopus: 6A Literature Review on Sustainable Buildings and Neighborhoods in Terms of Daylight, Solar Energy and Human Factors(Elsevier, 2025) Cogul, Ilgin cataroglu; Kazanasmaz, Zehra Tugce; Ekici, BerkSustainability has become the focus and interest of researchers with climate change's increasing impact and challenges. Considering various perspectives, published studies focus on sustainability in architecture and the built environment, such as using daylight more effectively, enhancing energy efficiency, and designing nearly zero-energy buildings. Given the attention to sustain- ability in this domain, this review assesses the abovementioned viewpoints in buildings regarding environmental factors in relation to the micro and macro scales of the buildings and neighborhoods. Human factor has increasingly been of interest in recent works of sustainable environments. This study identifies the gaps with respect to architectural design elements considering daylighting, energy efficiency and human factors on building and neighborhood scales. A comprehensive table of the reviewed studies summarizes the aim, methodology, optimization algorithm, objective function, machine learning algorithm, digital tools, location, independent and dependent variables, view, wellness, well-being, daylight/energy performance metrics, scale, and solar strategy. The results showed that the current state-of-the-art focus on energy efficiency mainly considers passive design strategies at the building scale. Studies in the daylight domain primarily consider window properties, shading devices, and orientation. Human-centric studies showed that daylighting improves the emotional well-being of building occupants but can have negative effects such as overheating and glare. Overall findings emphasize the necessity of a holistic approach in achieving sustainability goals in dwellings at the building and neighborhood scale.Article Citation - WoS: 7Citation - Scopus: 8A Review on Adaptive and Non-Adaptive Shading Devices for Sustainable Buildings(Elsevier, 2025) Avci, Pelin; Ekici, Berk; Kazanasmaz, Zehra TugceAdaptive and non-adaptive shading devices are noteworthy fa & ccedil;ade components in designing sustainable buildings. As the comparisons of their characteristics are limited, it becomes challenging to understand their efficiency, including their impacts on occupant behavior and comfort metrics. This comprehensive review covers (a) identifying the relationship between architectural parameters and performance targets, (b) exploring model development techniques due to performance targets, and (c) declaring both energy and visual comfort metrics. The paper covers 103 papers under architectural parameters and their corresponding performance targets, namely, daylight and visual comfort metrics with lighting energy. The aim is to identify existing research trends, methodological gaps, and potential for future study by examining how performance targets affect model development approaches. The categorizations include design parameters (shading elements and fa & ccedil;ade organization), control systems (shading device control, artificial lighting control, integrated systems), performance targets (daylight metrics, visual comfort metrics, lighting energy metrics), and modeling methods (simulation, experimental, optimization). Results showed that few studies combined daylight, visual comfort, and lighting energy due to complex modeling approaches, whereas most studies dealt only with daylight. With the increase in simulation software used to conduct research results on various focuses, an increasing trend in published papers is available in this field. Studies mostly observed changes in shading device typologies, slat angles, and numbers. The most dominant climate types were humid subtropical (Cfa) and Mediterranean (Csa). Future studies can be directed to integrated performance targets and combine suitable modeling approaches with AI technologies to produce more validated and accurate results.