Çiçek, Selen
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Cicek, Selen
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selen.cicek@ieu.edu.tr
Main Affiliation
06.04. Interior Architecture and Environmental Design
Status
Former Staff
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Sustainable Development Goals
5
GENDER EQUALITY
0
Research Products
9
INDUSTRY, INNOVATION AND INFRASTRUCTURE
1
Research Products
13
CLIMATE ACTION
0
Research Products
8
DECENT WORK AND ECONOMIC GROWTH
0
Research Products
14
LIFE BELOW WATER
0
Research Products
17
PARTNERSHIPS FOR THE GOALS
0
Research Products
1
NO POVERTY
0
Research Products
2
ZERO HUNGER
0
Research Products
4
QUALITY EDUCATION
1
Research Products
11
SUSTAINABLE CITIES AND COMMUNITIES
2
Research Products
16
PEACE, JUSTICE AND STRONG INSTITUTIONS
1
Research Products
3
GOOD HEALTH AND WELL-BEING
0
Research Products
6
CLEAN WATER AND SANITATION
0
Research Products
12
RESPONSIBLE CONSUMPTION AND PRODUCTION
0
Research Products
10
REDUCED INEQUALITIES
0
Research Products
15
LIFE ON LAND
0
Research Products
7
AFFORDABLE AND CLEAN ENERGY
0
Research Products
Documents
9
Citations
13
h-index
2
Documents
0
Citations
0
Scholarly Output
8
Articles
2
Views / Downloads
0/0
Supervised MSc Theses
0
Supervised PhD Theses
0
WoS Citation Count
6
Scopus Citation Count
13
WoS h-index
2
Scopus h-index
2
Patents
0
Projects
0
WoS Citations per Publication
0.75
Scopus Citations per Publication
1.63
Open Access Source
2
Supervised Theses
0
| Journal | Count |
|---|---|
| Proceedings of the International Conference on Education and Research in Computer Aided Architectural Design in Europe | 3 |
| Proceedings of the International Conference on Education and Research in Computer Aided Architectural Design in Europe -- 43rd Conference on Education and Research in Computer Aided Architectural Design in Europe, eCAADe 2025 -- 2025-09-01 through 2025-09-05 -- Ankara -- 344709 | 2 |
| Architectural Science Review | 1 |
| International Journal of Architectural Computing | 1 |
| Internatıonal Journal of Archıtectural Computıng | 1 |
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8 results
Scholarly Output Search Results
Now showing 1 - 8 of 8
Conference Object Insights From AI-Driven Architectural Design Competition: Challenging Conventional Paradigms(Education and Research in Computer Aided Architectural Design in Europe, 2025) Mersin, G.; Çiçek, S.; Basarir, L.The rapid advancements in artificial intelligence (AI) are fundamentally transforming the design landscape, prompting a critical reflection: how might architectural design competitions adapt to leverage AI's potential as a collaborative design assistant, challenging conventional paradigms and fostering broader awareness of technological advancements within the architectural profession? Addressing this inquiry, a pioneering competition was organized by a Non-Governmental Organization (NGO) to explore new trajectories in integrating AI into architectural design practices. The competition engaged participants in proposing innovative architectural interventions for a historically and industrially significant urban site. Its central aim was to encourage the development of AI-assisted workflows tailored to each participant’s unique design methodologies, reframing architectural design as an iterative process of thinking, seeing, and making, rather than a static outcome. This paper examines the competition’s methodology, detailing stages such as the preparation of specifications that emphasized AI workflow customization, and the evaluation framework of the jury, which prioritized originality, contextual relevance, and the depth of AI integration. Particular attention is given to how participants utilized AI to document and enhance their creative processes, fostering dynamic and personalized approaches to design. The findings underscore the potential of AI to redefine architectural workflows, offering insights into how computational tools can augment design thinking and practice. By reframing the role of AI in architectural design competitions, this study proposes a transformative model for integrating emerging technologies into the profession, emphasizing the importance of process-driven innovation to inspire broader engagement and understanding within the architectural community. © 2025, Education and research in Computer Aided Architectural Design in Europe. All rights reserved.Conference Object Citation - Scopus: 2Biobased Material Computation and Digital Fabrication for Bacterial Cellulose-Based Biofabrics(Education and research in Computer Aided Architectural Design in Europe, 2023) Turhan, Gözde Damla; Çiçek, Selen; Özbengi Uslu, FilizThe collaboration with biological organisms, biomaterial computation, and digital fabrication offers new possibilities for reconsidering the relationship between human and non-human living forms. These organisms allow for the creation of materials, design and manufacturing processes, and end products to become more closely aligned with natural systems and processes, as they are derived from renewable resources and have a lower environmental impact than synthetic materials. In this research, by focusing on nature and non-human living organisms, biobased material computation and digital fabrication were explored to develop biofabrics. This research offers a fully biodegradable process with zero waste and unlimited supply, enhanced with the resources provided by nature, including nature's design and manufacturing methods. To create this sustainable, circular cycle, one of the most abundant materials in the world, the purest form of cellulose, is produced by bacteria such as Acetobacter Xylinus (A. xylinus). In collaboration with A. xylinus, bacterial cellulose-based biofabrics were grown and harvested. The methodology was divided into four main stages: Digital fabrication of a customized fashion dummy which involves 3D modeling, laser-cutting, and assembly of a fashion dummy; a stochastic scaffold design for the bacterial cellulose biofilm layer; biobased material formulation for developing a biofabric; and bio-assembly. The outcome has been exhibited at Good Design İzmir 7, a national curated exhibition among the invited guests’ section, and had a chance to meet a larger audience to raise awareness. As a result, it was seen that incorporating biobased materials into the digital fabrication process has the potential to not only improve the performance and sustainability of materials but also to encourage designers to reconsider the relationship between humans and ecology. Future studies can include the scalability of such systems for broader design realms, such as biobased architectural solutions for buildings, especially lightweight structures, as well as industrial design products such as packaging. © 2023, Education and research in Computer Aided Architectural Design in Europe. All rights reserved.Conference Object Demystifying the Patterns of Local Knowledge: the Implicit Relation of Local Music and Vernacular Architecture(Education and research in Computer Aided Architectural Design in Europe, 2023) Başarır, Lale; Çiçek, Selen; Koç, M.The development of novel design output using Artificial Neural Networks (ANNs) is becoming an important milestone in the architectural design discourse. With the recent encounter of the computational design realm with the diffusion models, it becomes even easier to generate 2D and 3D design outputs. Yet, the utilization of machine learning tools within design computing domains is confined to generating or classifying visual and encoded data. However, it is critical to evaluate the untapped potentials of machine learning technologies in terms of illuminating the implicit correlations and links underlying distinct concepts and themes across a wide range of technical domains. With the ongoing research project named “Local Intelligence", we hypothesized that the local knowledge of a certain location might be conceptualized as a distributed network to connect different forms of local knowledge. As the first case of the project, we tried to reinstate a commonality between the local music and vernacular architecture, for which we trained generative adversarial network (GAN) models with the visual spectrograms translated from the audio data of the local songs and images of vernacular architectural instances from a defined geography. The two multi-modal GAN models differ in terms of the inherent convolutional layers and data pairing process. The outcomes demonstrated that both GAN models can learn how to depict vernacular architectural features from the rhythmic pattern of the songs in various patterns. Consequently, the implicit relations between music and architecture in the initial findings come one step closer to being demystified. Thus, the process and generative outcomes of the two models are compared and discussed in terms of the legibility of the architectural features, by taking the original vernacular architectural image dataset as the ground truth. © 2023, Education and research in Computer Aided Architectural Design in Europe. All rights reserved.Conference Object Citation - Scopus: 4Reconsidering Design Pedagogy Through Diffusion Models(Education and research in Computer Aided Architectural Design in Europe, 2023) Çiçek, Selen; Turhan, Gözde Damla; Özkar, M.The text-to-image based diffusion models are deep learning models that generate images from text-based narratives in user-generated prompts. These models use natural language processing (NLP) techniques to recognize narratives and generate corresponding images. This study associates the assignment-based learning-by-doing of design studio with the prompt-based diffusion models that require fine-tuning in each image generation. The reference is a specific formal education setup developed within the context of compulsory courses in design programs’ curricula. We explore the implications of diffusion models for a model of the basic design studio as a case study. The term basic design implies a core and foundational element of design. To explore and evaluate the potential of AI tools to improve novice designers’ design problem solving capabilities, a retrospective analysis was conducted for a series of basic design studio assignments. The first step of the study was to reframe the assignment briefs as design problems and student design works as design solutions. The outcomes of the identification were further used as input data to generate synthetic design solutions by text-to-image diffusion models. In the third step, the design solution sets generated by students and the diffusion models were comparatively assessed by design experts with regards to how well they answered to the design problems defined in the briefs. The initial findings showed that diffusion models were able to generate a myriad of design solutions in a short time. It is conjectured that this might help students to easily understand the ill-defined design problem requirements and generate visual concepts based on written descriptions. However, the comparison indicated the value of design reasoning conveyed in the studio, as it gets highlighted with the lack of improvement in the learning curve of the diffusion model recorded through the synthetic design process. © 2023, Education and research in Computer Aided Architectural Design in Europe. All rights reserved.Conference Object Synthetic Interpretations: AI-Driven Scoring Framework for Architectural Design Evaluation(Education and Research in Computer Aided Architectural Design in Europe, 2025) Bingöl, K.; Koç, M.; Çiçek, S.; Aksu, M.S.; Öztürk, E.; Mersin, G.; Basarir, L.While artificial intelligence (AI) has significantly influenced architectural design through generative tasks like conceptual exploration and visualization, its capacity for nuanced qualitative evaluation remains underexplored. Effective evaluation requires a convergence of subjective interpretation and objective rigor, addressing contextual relationships, formal qualities, adherence to design principles, programmatic functions, construction strategies, structural systems, and sustainable practices. This research addresses these challenges by developing an AI-driven scoring framework, ArchiJury, based on synthetic architectural reviews aligned with established evaluation criteria. Two distinct AI models form the methodological basis of the study: The first employs visual transformer models and a synergy simulation algorithm for precise, context-based criterion-specific evaluation. The second uses a ResNet-18 deep-learning architecture for multi-criteria holistic scoring, trained end-to-end with an annotated dataset and optimized through mean squared error (MSE) loss, and utilizes Grad-CAM heatmaps for interpretability by visually representing the influential image regions guiding AI scoring decisions. The outputs of both models are comparatively discussed with human expert evaluations to critically assess AI’s potential and limitations, and implications of AI driven evaluation, clarifying how these computational methods align or diverge from expert judgment and exploring their significance for scalable, consistent, and nuanced architectural evaluation. © 2025, Education and research in Computer Aided Architectural Design in Europe. All rights reserved.Conference Object Citation - WoS: 1Citation - Scopus: 1Computational Generation of a Spatial Layout Through Syntactical Evaluation and Multi-Objective Evolutionary Optimization(Sage Publications Ltd, 2022) Cicek, Selen; Turhan, Gozde DamlaThe space layout problem encompasses challenges that rely on a diverse range of contexts regarding urban planning and architectural design, during the traditional design phases which require immense effort and time for the evaluation of the spatial elements' characteristic needs. In order to eliminate the burden of considering all multidimensional design aspects at the same time, this research presents a three-bodied computational method for locating the spaces of the given architectural design program in a project site, according to the defined list of design objectives and criteria. Besides the determination of the layout according to the requirements of the spatial elements, this research proposes an integration of the space syntax theory's analytical compounds in terms of Justified Graph Analysis and Integration Values as the fitness criteria for the multi-objective evolutionary optimization in the computational model. To satisfy the integrity levels of each various characterized element within site organization, that are implied inherently by the architectural design program and generate a sustainable space network layout for the project site.Article Citation - WoS: 3Citation - Scopus: 5Deterioration of Pre-War and Rehabilitation of Post-War Urbanscapes Using Generative Adversarial Networks(Sage Publications Ltd, 2023) Çiçek, Selen; Turhan, Gözde Damla; Taşer, AybükeThe urban built environment of contemporary cities confronts a constant risk of deterioration due to natural or artificial reasons. Especially political aggression and war conflicts have significant destructive effects on architectural and cultural heritage buildings. The post-war urbanscapes demonstrate the striking effects of the armed conflicts during the hot war encounters. However, the residues of the urbanscapes become the actual indicators of damage and loss. Since today we can make future predictions using a variety of machine learning algorithms, it is possible to represent hybrid projections of urban heterotopias. In this context, this research proposes to explore dystopian post-war projections for modern cities based on their architectural styles and demonstrate the utopian scenarios of rehabilitation possibilities for the damaged urban built environment of post-war cities by using generative adversarial network (GAN) algorithms. Two primary datasets containing the post-war and pre-war building facades have been given as the input data for the CycleGAN and pix2pix GAN models. Thus, two different image-to-image GAN models have been compared regarding their ability to produce legible building facade projections in architectural features. Besides, the machine learning process results have been discussed in terms of cities' utopian and dystopian future predictions, demonstrating the war conflicts' immense effects on the built environment. Moreover, the immediate consequence of the destructive aggression on tangible and intangible architectural heritage would become visible to inhabitants and policymakers when the AI-generated rehabilitation potentials have been exposed.Article Citation - WoS: 2Citation - Scopus: 1Local Intelligence: Time To Learn From Ai(Taylor and Francis Ltd., 2025) Başarır, Lale; Çiçek, S.; Koç, M.AI research in architecture is flourishing, and there are plausible and praiseworthy experiments using generative models. These experiments could result in intelligence that uses architectural knowledge and opens new learning opportunities, although guidance is still required in this area. With the Local Intelligence (LI) framework, we hypothesize a web of distributed networks to connect different forms of knowledge linked with architectural context. We assume that the tacit knowledge of vernacular architecture corresponds to the implicit knowledge of folklore music when the anonymous designer and the user are the same- the local people. With a multimodal AI model, we call ‘music2architecture’ – ‘architecture2music’, we argue that sharing the same locality/localness may lead to the emergence of a typical, previously hidden pattern (of wisdom) that we can learn from. © 2024 Informa UK Limited, trading as Taylor & Francis Group.
