Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7148
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Conference Object A General Predictive Model to Evaluate Daylight Levels of Residential Buildings in the Mediterranean (Next Med) Region(Education and Research in Computer Aided Architectural Design in Europe, 2025) Ekici, B.Conceptual design is one of the most critical phases, as design decisions affect the buildings’ performance throughout their life cycle. Researchers consider various computational methods to achieve effective design proposals. Nevertheless, optimization algorithms are necessary to cope with the complexity and increase the efficiency of design alternatives in various aspects. In sustainable building design, these decisions require computationally expensive processes due to the simulation tasks. Besides, making sustainable design decisions is even more challenging in a Mediterranean climate due to changing conditions throughout the year. Therefore, recent studies frequently consider combining predictive models with optimization algorithms to decrease the burden of expensive simulation time. Relevant works present promising outcomes, yet they are limited to predicting the building performance of specific cases; thus, the proposed predictive models are limited to different design problems. This paper investigates the development of a general machine learning (ML) model to overcome this issue. With this motivation, a parametric test box consisting of twenty parameters related to weather data of twelve Mediterranean (Next Med) countries, space dimensions, vertical/horizontal louvers, and material type is developed using Grasshopper 3d. Moreover, a parametric urban model, which considers eight parameters related to the density of the surrounding buildings, is also created to generate numerous environments. The LadyBug tools simulate the daylight autonomy to generate 12,000 samples. Five different ML models involving artificial neural networks (ANN) are built in Python. Statistical results showed that train and test scores achieved promising outcomes in all ML models. However, when predicting user-defined scenarios not involved in the generated dataset, only ANNs perform generalizable, accurate predictions. The paper discusses the ability of ANN models to accurately predict different design scenarios and locations, and the trustworthiness of the training and test scores based only on collected data. © 2025, Education and research in Computer Aided Architectural Design in Europe. All rights reserved.Conference Object Material Optimisation for Future Double Skin Façade System Design(Institute of Physics, 2025) Unluturk, M.S.; Kazanasmaz, Z.T.; Ekici, B.; Göksal Özbalta, T.G.Façades have a significant impact on energy consumption in interiors. Designers aimed to reduce energy consumption by developing different façade systems. Double Skin Façade (DSF) aims to increase thermal and ventilation performance in the interior. The depth of the cavity gap between the two façade layers with air inside may adversely affect indoor daylight performance. In addition, studies in the literature indicate that this façade system shows optimum performance in cold climates. With the right design decisions, the DSF system can provide optimum performance in hot climates. In building designs with DSF systems in these climate zones, daylight and energy simulations can make the right design decisions. However, the climate crisis (CC) is increasing air temperatures and sunshine hours in hot and arid climate zones. Simulations are based on current climate data, and the recommendations obtained may not show optimum performance in the future. The study aims to propose an educational building model with a DSF system that will provide optimum visual comfort for 50 years in the Mediterranean climate type (CSA). Meteonorm has created weather scenarios for Izmir for 2050 and 2080. Opossum and Galapagos carried out the optimisation process using this data. The study proposes models that will perform optimally in Izmir for 50 years. © Published under licence by IOP Publishing Ltd.Conference Object Design of Adaptive Shading Device with Rigid Origami Technique: Improving Outdoor Thermal Comfort on Pathways of University Campus(Institute of Physics, 2025) Dağlier, Y.; Ekici, B.; Korkmaz, K.Since urbanization emerged with consequences for the built environment, shadows have played a key role in outdoor comfort. In hot climates, shadow has become a vital element in public spaces as it significantly affects social interaction on various occasions, such as university campus areas. The current state of the art shows that the role of shadings in outdoor environments is crucial to increasing pedestrian comfort and supporting overall well-being. While trees and canopies are commonly used for shading, their applicability is sometimes limited in pedestrian pathways. For example, the Izmir Institute of Technology (IZTECH) campus copes with outdoor discomfort during the extremely hot summer days. Due to the changing environmental conditions, static shading devices offer effective shadows only at specific times. This creates a necessity to design shading devices that can rotate and fold to mitigate temperatures more effectively and increase outdoor thermal comfort. A parametric shading model was developed using Grasshopper and Kangaroo Physics®, and its effectiveness was analyzed using Building Performance Simulation (BPS) tools. The research integrates heuristic optimization techniques to enhance shading performance, including Galapagos (Genetic Algorithm) and Opossum (RBF-opt and CMA-ES). Results indicate that the proposed kinetic shading devices reduced the universal thermal climate index (UTCI) by approximately 20% during peak sunlight hours. These findings suggest that adaptive shading strategies efficiently improve outdoor thermal comfort in urban public spaces. © 2025 Published under licence by IOP Publishing Ltd.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.Conference Object Combining Generative Adversarial Networks and Reinforcement Learning for Floor Plan Layout Generation(Education and Research in Computer Aided Architectural Design in Europe, 2025) Güldilek, M.; Ilal, M.E.; Ekici, B.Generative Adversarial Networks (GANs) are among artificial intelligence (AI) methods for generating architectural floor plan layouts to approximate spatial distribution with a reasonable degree of accuracy. However, when used exclusively, GAN-based tools may fail to capture architectural patterns and often produce unrealistic layouts. To address this limitation, researchers have proposed integrating Reinforcement Learning (RL) into GANs. While RL has been combined with generative algorithms in other fields, a systematic multi-scenario integration of GANs and RL remains underexplored in architecture. This paper introduces a new solution by combining RL and GANs to generate floor plan layouts tailored to user requirements. The research design involves three different integration strategies: (1a) mere generation, where RL refines GAN outputs by eliminating inconsistencies and errors; (1b) objective optimization, where RL targets measurable attributes such as spatial size and morphological legibility; and (1c) refinement of non-quantifiable attributes, where RL incorporates user feedback to improve flexibility and perceived comfort. Additionally, the study employs House-GAN++ as the GAN model and the PPO algorithm as the RL framework. Three case studies are presented to evaluate performance. Results demonstrate that integrating RL with GANs yields floor plan layouts more responsive to user needs than those produced by GANs alone. Each scenario illustrates how RL optimizes GAN-generated outputs according to functional, measurable, and perceptual goals. The methodology acknowledges user expectations and translates them into realistic, adaptable plans. Key outcomes include more realistic layouts, designs with distinctive characteristics, and user-customized floor plans created through interaction. The proposed framework enables automatic floor plan generation that combines design, optimization, and user input at the conceptual stage. This integration enhances architectural design processes by balancing computational efficiency with user-oriented adaptability, thus broadening the potential of AI-assisted design. © 2025, Education and research in Computer Aided Architectural Design in Europe. All rights reserved.