Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

Permanent URI for this collectionhttps://hdl.handle.net/11147/7148

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Author: search.filters.author.Dağlier, Y.Publication Index: search.filters.publicationIndex.Scopus

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  • 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.