IZTECH GCRIS Premium Database
The scientific memory of Izmir Institute of Technology. Publications, projects, and researchers—all in one place. The heart of open science beats here. 'Open Science. Visible Impact.'
Most Viewed Publications
Most Downloaded Publications
Recent Submitted Publications
Open Science Principles and EOSC Integration for Turkish RIs
(İzmir Institute of Technology, 2026-02-16) Gürdal, Gültekin
Technical Assistance for Türkiye in Horizon Europe Project Focused Group Training on Horizon Europe Research Infrastructure başlıklı 16 Şubat 2026'da İstanbul'da düzenlenen toplantıda gerçekleştirilen sunumdur.
Open Science, FAIR Data, and EOSC
(Izmir Institute of Technology, 2026-02-16) Gürdal, Gültekin
Horizon Europe Programme Research Infrastructures (Research Infrastructures – INFRA) Information Day kapsamında 17 Şubat 2026 tarihinde Trakya Üniversitesinde gerçekleştirilen toplantının sunumudur.
An Experimental Study to Investigate the Efficiency of Floating Pontoons on the Wave Overtopping Reduction
(International Society of Offshore and Polar Engineers, 2025) Ozbahceci, B.O.; Eroglu, N.
There are many studies focused on the wave transmission performance of floating structures. However, the performance of floating structures to prevent coastal floods has not yet been investigated considering wave overtopping. This study aimed to experimentally assess the wave overtopping performance of a concrete floating pontoon in front of an existing vertical sea wall. The mean wave overtopping discharge, q, was compared for the cases with and without the floating pontoon model in front of the wall. Results showed that the baseline floating pontoon model reduced wave overtopping discharge by 30-90% compared to the case with the wall alone. Furthermore, the study revealed that an increase in freeboard or draft of the floating pontoon led to a greater reduction in wave overtopping. These findings suggest that the integration of a floating pontoon with optimized freeboard and draft could be an effective solution for reducing wave overtopping in coastal defense applications. © 2025 by the International Society of Offshore and Polar Engineers (ISOPE).
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.
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.