Cumincad : CUMINCAD Papers : Search Results

CumInCAD is a Cumulative Index about publications in Computer Aided Architectural Design
supported by the sibling associations ACADIA, CAADRIA, eCAADe, SIGraDi, ASCAAD and CAAD futures

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_id	caadria2023_1
id	caadria2023_1
authors	Koh, Immanuel
year	2023
title	AI-Bewitched Architecture of Hansel and Gretel: Food-to-Architecture in 2D & 3D with GANs and Diffusion Models
source	Immanuel Koh, Dagmar Reinhardt, Mohammed Makki, Mona Khakhar, Nic Bao (eds.), HUMAN-CENTRIC - Proceedings of the 28th CAADRIA Conference, Ahmedabad, 18-24 March 2023, pp. 9–18
doi	https://doi.org/10.52842/conf.caadria.2023.1.009
summary	Architects such as Le Corbusier, Frank Gehry, Aldo Rossi, and Greg Lynn have implicitly turned culinary formalism into architectural formalism during their careers. How might AI assist in a similar act of bisociation (or conceptual blending)? The paper is the first to explore this food2architecture bisociation explicitly, and specifically with generative adversarial networks (GANs) such as CycleGAN and VQGAN-CLIP, and diffusion models such as OpenAI’s DALL-E 2, Midjourney and DreamFusion (using Stable Diffusion). Instead of using textual input prompts to generate images of architecture only with the discipline’s own vocabulary, the research merges them with the vocabulary of food, thus exploiting their potential in blending their respective conceptual and formal characteristics. While these diffusion models have recently been used by the general public to generate 2D imagery posts on various social media platforms, no existing work has conducted a detailed and systematic analysis on their exclusive capacity in bisociating food and architecture. Imagery outputs generated during two workshops involving 150 designers and non-designers are included here as illustrations. Beginning and ending the paper with the all-familiar fairy tale of the gingerbread house, the research explores the creative design bisociative affordance of today's text-to-image and text-to-3D models by turning culinary inputs into architectural outputs -- envisioning an explicitly computational version of the implicit 'food2architecture' mental models plausibly used by some of the most creative architects.
keywords	Deep Learning, Midjourney, DALL-E 2, DreamFusion, Stable Diffusion, GANs
series	CAADRIA
email
last changed	2023/06/15 23:14

_id	sigradi2023_114
id	sigradi2023_114
authors	Huang, Sheng-Yang, Wang, Yuankai and Jiang, Qingrui
year	2023
title	(In)Visible Cities: Exploring generative artificial intelligence'screativity through the analysis of a conscious journey in latent space
source	García Amen, F, Goni Fitipaldo, A L and Armagno Gentile, Á (eds.), Accelerated Landscapes - Proceedings of the XXVII International Conference of the Ibero-American Society of Digital Graphics (SIGraDi 2023), Punta del Este, Maldonado, Uruguay, 29 November - 1 December 2023, pp. 717–728
summary	The rise of generative AI has redefined architectural design by introducing latent space, challenging traditional methods. This paper aims to explore, structure, and analyse latent journeys, drawing from analytical design discourses. We construct journeys towards 'Isaura' from 'Invisible Cities' by Italo Calvino, bridging literature and visual narratives, utilising the text-image generating software, Midjourney. The objective is to identify spatial configurations that align with the designer's interpretation of the text, ensuring the accuracy of visual elements. Structured as a Markov (stochastic) process, the experiment encompasses four primary stages to offer a rational explanation for the journey and the role of each segment. Findings emphasise the potential of latent space in augmenting architectural design and underscore the necessity for analytical tools to avert the reduction of design to trivial formalism. The study's outcome suggests that understanding and leveraging the traits of latent space can nurture a more meaningful engagement with AI-driven design, presenting a novel approach to architectural creativity.
keywords	Latent Space, Generative Artificial Intelligence, Text-to-image Generation, Architectural Creativity, Spatial Analysis
series	SIGraDi
email
last changed	2024/03/08 14:07

_id	ascaad2023_134
id	ascaad2023_134
authors	Salman, Huda; Dounas, Theodoros; Clarke, Connor
year	2023
title	Fluency of Creative Ideas in the Digital Age: Exploring Emergent AI Influences on Design Methodology and Visual Thinking in Architectural Education
source	C+++: Computation, Culture, and Context – Proceedings of the 11th International Conference of the Arab Society for Computation in Architecture, Art and Design (ASCAAD), University of Petra, Amman, Jordan [Hybrid Conference] 7-9 November 2023, pp. 815-832.
summary	Research has explored the concept of originality in visual thinking and architectural education, using different methods. The new state of Artificial Intelligence (AI) in architectural design represents another shift from traditional modes of architectural design and education, into a more authentic approach to the digital age. An experiment is designed to highlight the originality of this approach in design thinking and its futuristic trends and impact on education and creativity studies. The intent of the study we present here is twofold: one to revisit key design studies of design exploration and secondly to explore students' design activity while interacting with text-to-image diffusion machine learning (ML) generative models such as Midjourney, DALL-E and Stable Diffusion, as these might have the potential to change the way that architectural students approach the concept stages of designing projects and products. In addition, we are interested in how the new shift in interfaces and modes of stimulus will influence the students' design process and perceptions. Participants in the design process are final year students who had spent at least four years in a school of architecture and can be classified as semi-experienced designers. Further within the evaluation also lies a critique of the diffusion ML tools themselves as producers of architectonic images, rather than complete concepts for architecture that encapsulate spatial, formal, structural arrangements of elements.
series	ASCAAD
email
last changed	2024/02/13 14:41

_id	acadia23_v2_582
id	acadia23_v2_582
authors	Wu, Kaicong; Li, Chenming; Su, Wenjun
year	2023
title	The Chair Game Experiment: Transforming Multiplayer Design Processes with Text-to-Image Generation and 2D-to-3D Modelling
source	ACADIA 2023: Habits of the Anthropocene: Scarcity and Abundance in a Post-Material Economy [Volume 2: Proceedings of the 43rd Annual Conference for the Association for Computer Aided Design in Architecture (ACADIA) ISBN 979-8-9860805-9-8]. Denver. 26-28 October 2023. edited by A. Crawford, N. Diniz, R. Beckett, J. Vanucchi, M. Swackhamer 582-595.
summary	The urgency for an inclusive architectural design process in conceptualizing the built environment stems from the need to establish effective communication between under- represented groups and design professionals. However, various challenges hinder the development of an inclusive design process that accommodates diverse stakeholders. Individual designers or selective design teams are frequently limited by their own visions, causing them to potentially overlook alternative solutions. Moreover, stakeholders who lack professional training might struggle to articulate their expectations. The emergence of generative AI (artificial intelligence) technologies has significantly reduced the tech- nical barriers in design, and has empowered non-professionals to vividly express their ideas regarding forms and spaces. This has presented a valuable opportunity to better understand the perspectives of underrepresented groups through visual representations. Therefore, this research aims to explore the impact of image generation on the democ- ratization of the design process. Using chair design as a testing ground, we propose an evolutionary computing framework that simulates interactions among designers and participants empowered by emerging AI technologies. To investigate the potential impact of image generation, we have implemented a multiplayer design game to allow computing agents to compete in exploring 3D chair forms. Through this approach, we aim to gain insights into how image generation influences design decisions, whether it generates more diversified solutions, and what values could be introduced into the built environment.
series	ACADIA
type	paper
email
last changed	2024/04/17 13:59

_id	ecaade2023_266
id	ecaade2023_266
authors	Çiçek, Selen, Turhan, Gözde Damla and Özkar, Mine
year	2023
title	Reconsidering Design Pedagogy through Diffusion Models
source	Dokonal, W, Hirschberg, U and Wurzer, G (eds.), Digital Design Reconsidered - Proceedings of the 41st Conference on Education and Research in Computer Aided Architectural Design in Europe (eCAADe 2023) - Volume 1, Graz, 20-22 September 2023, pp. 31–40
doi	https://doi.org/10.52842/conf.ecaade.2023.1.031
summary	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.
keywords	Deep Learning, Diffusion Models, Design Education, Basic Design, Design Problems
series	eCAADe
email
last changed	2023/12/10 10:49

_id	ijac202321208
id	ijac202321208
authors	Ennemoser, Benjamin; Mayrhofer-Hufnagl, Ingrid
year	2023
title	Design across multi-scale datasets by developing a novel approach to 3DGANs
source	International Journal of Architectural Computing 2023, Vol. 21 - no. 2, 358–373
summary	The development of Generative Adversarial Networks (GANs) has accelerated the research of Artificial Intelligence (AI) in architecture as a generative tool. However, since their initial invention, many versions have been developed that only focus on 2D image datasets for training and images as output. The current state of 3DGAN research has yielded promising results. However, these contributions focus primarily on building mass, extrusion of 2D plans, or the overall shape of objects. In comparison, our newly developed 3DGAN approach, using fully spatial building datasets, demonstrates that unprecedented interconnections across different scales are possible resulting in unconventional spatial configurations. Unlike a traditional design process, based on analyzing only a few precedents (typology) according to the task, by collaborating with the machine we can draw on a significantly wider variety of buildings across multiple typologies. In addition, the dataset was extended beyond the scale of complete buildings and involved building components that define space. Thus, our results achieve a high spatial diversity. A detailed analysis of the results also revealed new hybrid architectural elements illustrating that the machine continued the interconnections of scale since elements were not explicitly part of the dataset, becoming a true design collaborator.
keywords	3D Generative adversarial networks, architectural design, Spatial Interpolations
series	journal
last changed	2024/04/17 14:30

_id	acadia23_v3_49
id	acadia23_v3_49
authors	A. Noel, Vernelle; Dortdivanlioglu, Hayri
year	2023
title	Text-to-image generators: Semiotics, Semantics, Syntax, and Society
source	ACADIA 2023: Habits of the Anthropocene: Scarcity and Abundance in a Post-Material Economy [Volume 3: Proceedings of the 43rd Annual Conference for the Association for Computer Aided Design in Architecture (ACADIA) ISBN 979-8-9891764-1-0]. Denver. 26-28 October 2023. edited by A. Crawford, N. Diniz, R. Beckett, J. Vanucchi, M. Swackhamer 24-32.
summary	Text-to-image generators, such as Midjourney, DALL-E, and Stable Diffusion, are becoming increasingly popular. These generators, whose models are trained on large datasets of text-image pairs, often scraped from the web, take text prompts as input, and use them to generate images—text-to-image prompting. In this visual essay, we raise questions about the entanglement of semiotics, semantics, syntax, and society in these text-to-image generator tools. We are intrigued by how these technologies are “intrawoven” with social and cultural contexts. How are their constructions and presentations reconfigurations? How do, or might they, inform pedagogy, theory, methods, and our publics? To explore these questions, we entered six prompts related to the built environment in six different languages, eight months apart in Midjourney (“Midjourney” n.d.). The generated images (Figure 1), require that we ask deep questions of each image, in comparison with each other, across each group of four, and across time (eight months apart). We argue that text-to-image generators call for a rigorous exploration of semiotics, semantics, syntax, and the society, with implications for pedagogy, theory-building, methodologies, and public enlightenment. Furthermore, we assert that these tools can facilitate pertinent questions about the relationships between technology and society. This is just the beginning. For now, we have questions.
series	ACADIA
type	field note
email
last changed	2024/04/17 13:59

_id	ecaade2023_328
id	ecaade2023_328
authors	Andreou, Alexis, Kontovourkis, Odysseas, Solomou, Solon and Savvides, Andreas
year	2023
title	Rethinking Architectural Design Process using Integrated Parametric Design and Machine Learning Principles
source	Dokonal, W, Hirschberg, U and Wurzer, G (eds.), Digital Design Reconsidered - Proceedings of the 41st Conference on Education and Research in Computer Aided Architectural Design in Europe (eCAADe 2023) - Volume 2, Graz, 20-22 September 2023, pp. 461–470
doi	https://doi.org/10.52842/conf.ecaade.2023.2.461
summary	Artificial Intelligence (AI) has the potential to process vast amounts of subjective and conflicting information in architecture. However, it has mostly been used as a tool for managing information rather than as a means of enhancing the creative design process. This work proposes an innovative way to enhance the architectural design process by incorporating Machine Learning (ML), a type of Artificial Intelligence (AI), into a parametric architectural design process. ML would act as a mediator between the architects' inputs and the end-users' needs. The objective of this work is to explore how Machine Learning (ML) can be utilized to visualize creative designs by transforming information from one form to another - for instance, from text to image or image to 3D architectural shapes. Additionally, the aim is to develop a process that can generate comprehensive conceptual shapes through a request in the form of an image and/or text. The suggested method essentially involves the following steps: Model creation, Revisualization, Performance evaluation. By utilizing this process, end-users can participate in the design process without negatively affecting the quality of the final product. However, the focus of this approach is not to create a final, fully-realized product, but rather to utilize abstraction and processing to generate a more understandable outcome. In the future, the algorithm will be improved and customized to produce more relevant and specific results, depending on the preferences of end-users and the input of architects.
keywords	End-users, Architects, Mass personalization, Visual programming, Neural Network Algorithm
series	eCAADe
email
last changed	2023/12/10 10:49

_id	ecaade2023_137
id	ecaade2023_137
authors	Blaas, Quintin, Pelosi, Antony and Brown, Andre
year	2023
title	Reconsidering Artificial Intelligence as Co-Designer
source	Dokonal, W, Hirschberg, U and Wurzer, G (eds.), Digital Design Reconsidered - Proceedings of the 41st Conference on Education and Research in Computer Aided Architectural Design in Europe (eCAADe 2023) - Volume 2, Graz, 20-22 September 2023, pp. 559–566
doi	https://doi.org/10.52842/conf.ecaade.2023.2.559
summary	The research in this paper is presented from the perspective of a designer interested in investigating using artificial intelligence, specifically machine learning, to act as a co-pilot during architectural design phases. Significant recent interest has been evident in, for instance, rapidly developing text-to-image and intelligent chat AI areas. However, we have a particular focus and have undertaken a series of feasibility experiments to explore the potential for enabling a designer's exploitation of machine learning, and consequently in effect, using machine learning as a co-designer. We conclude that the industry would need to develop certain protocols to take advantage of the opportunities available through such an AI-assisted approach.
keywords	Artificial Intelligence, Design Data, Algorithmic Design, Design Process, Co-Designing
series	eCAADe
email
last changed	2023/12/10 10:49

_id	ecaade2023_145
id	ecaade2023_145
authors	Dortheimer, Jonathan, Schubert, Gerhard, Dalach, Agata, Brenner, Lielle Joy and Martelaro, Nikolas
year	2023
title	Think AI-side the Box! Exploring the Usability of text-to-image generators for architecture students
source	Dokonal, W, Hirschberg, U and Wurzer, G (eds.), Digital Design Reconsidered - Proceedings of the 41st Conference on Education and Research in Computer Aided Architectural Design in Europe (eCAADe 2023) - Volume 2, Graz, 20-22 September 2023, pp. 567–576
doi	https://doi.org/10.52842/conf.ecaade.2023.2.567
summary	This study examines how architecture students use generative AI image generating models for architectural design. A workshop was conducted with 25 participants to create designs using three state-of-the-art generative diffusion models and BIM or 3D modeling software. Results showed that the participants found the image-generating models useful for the preliminary design stages but had difficulty when the design advanced because the models did not perform as they expected. Finally, the study shows areas for improvement that merit further research. The paper provides empirical evidence on how generative diffusion models are used in an architectural context and contributes to the field of digital design.
keywords	Machine Learning, Diffusion Models, Design Process, Computational Creativity
series	eCAADe
email
last changed	2023/12/10 10:49

_id	caadria2023_446
id	caadria2023_446
authors	Guida, George
year	2023
title	Multimodal Architecture: Applications of Language in a Machine Learning Aided Design Process
source	Immanuel Koh, Dagmar Reinhardt, Mohammed Makki, Mona Khakhar, Nic Bao (eds.), HUMAN-CENTRIC - Proceedings of the 28th CAADRIA Conference, Ahmedabad, 18-24 March 2023, pp. 561–570
doi	https://doi.org/10.52842/conf.caadria.2023.2.561
summary	Recent advances in Natural Language Processing (NLP) and Diffusion Models (DMs) are leading to a significant change in the way architecture is conceived. With capabilities that surpass those of current generative models, it is now possible to produce an unlimited number of high-quality images (Dhariwal and Nichol 2021). This opens up new opportunities for using synthetic images and marks a new phase in the creation of multimodal 3D forms, central to architectural concept design stages. Presented here are three methodologies of generation of meaningful 2D and 3D designs, merging text-to-image diffusion models Stable Diffusion, and DALL-E 2 with computational methods. These allow designers to intuitively navigate through a multimodal feedback loop of information originating from language and aided by artificial intelligence tools. This paper contributes to our understanding of machine-augmented design processes and the importance of intuitive user interfaces (UI) in enabling new dialogues between humans and machines. Through the creation of a prototype of an accessible UI, this exchange of information can empower designers, build trust in these tools, and increase control over the design process.
keywords	Machine Learning, Diffusion Models, Concept Design, Semantics, User Interface, Design Agency
series	CAADRIA
email
last changed	2023/06/15 23:14

_id	ijac202321201
id	ijac202321201
authors	Steinfeld, Kyle
year	2023
title	Clever little tricks: A socio-technical history of text-to-image generative models
source	International Journal of Architectural Computing 2023, Vol. 21 - no. 2, 211–241
summary	The emergence of text-to-image generative models (e.g., Midjourney, DALL-E 2, Stable Diffusion) in the summer of 2022 impacted architectural visual culture suddenly, severely, and seemingly out of nowhere. To contextualize this phenomenon, this text offers a socio-technical history of text-to-image generative systems. Three moments in time, or “scenes,” are presented here: the first at the advent of AI in the middle of the last century; the second at the “reawakening” of a specific approach to machine learning at the turn of this century; the third that documents a rapid sequence of innovations, dubbed “clever little tricks,” that occurred across just 18 months. This final scene is the crux, and represents the first formal documentation of the recent history of a specific set of informal innovations. These innovations were produced by non-affiliated researchers and communities of creative contributors, and directly led to the technologies that so compellingly captured the architectural imagination in the summer of 2022. Across these scenes, we examine the technologies, application domains, infrastructures, social contexts, and practices that drive technical research and shape creative practice in this space.
keywords	Machine learning, text-to-image, socio-technical study, generative AI
series	journal
last changed	2024/04/17 14:30

_id	ecaade2023_224
id	ecaade2023_224
authors	Yonder, Veli Mustafa, Dulgeroglu, Ozum, Dogan, Fehmi and Cavka, Hasan Burak
year	2023
title	The Role of the Computational Designer From Computer-Aided Design to Machine Learning-Aided Design: A study on generative models and design prompts
source	Dokonal, W, Hirschberg, U and Wurzer, G (eds.), Digital Design Reconsidered - Proceedings of the 41st Conference on Education and Research in Computer Aided Architectural Design in Europe (eCAADe 2023) - Volume 1, Graz, 20-22 September 2023, pp. 293–300
doi	https://doi.org/10.52842/conf.ecaade.2023.1.293
summary	The rising sophistication of digital design technologies and instruments requires computational designers to acquire a broader set of abilities, such as expertise in a variety of digital models, scripting languages, and the ability to manage complicated data models. In the field of design, the concepts of machine learning-aided design and data-driven techniques contribute to the production of various and numerous design possibilities. Ultimately, this will lead the computational designer to redefine his or her power over the design protocol. In this paper, ChatGPT-3.5, Dall-E v2, and Stable Diffusion, cutting-edge artificial intelligence models, are used to construct sample design scenarios. Using a text mining application, the scenario-specific prompts were examined to explore these models' computational design potential.
keywords	Artificial Intelligence-Aided Design, Text Mining, Architectural Vocabulary
series	eCAADe
email
last changed	2023/12/10 10:49

_id	ascaad2023_060
id	ascaad2023_060
authors	Çelik, Abdullah; Kurtuluº, ªeyma; Karabay, Ecem; Özdemir, Salih
year	2023
title	Integrating AI Image Generators to First-Year Design Studio: “Invisible Cities” Reimagined with AI Subtitle
source	C+++: Computation, Culture, and Context – Proceedings of the 11th International Conference of the Arab Society for Computation in Architecture, Art and Design (ASCAAD), University of Petra, Amman, Jordan [Hybrid Conference] 7-9 November 2023, pp. 976-995.
summary	AI technology has been widely used for image generating during the design process both in academia and industry. There are hesitations to integrate AI image-generating software into design education due to copyright and ethical issues. However, its spread is inevitable, and students should be aware of the pros and cons of using AI technologies. Hence, this study aims to examine the effect of AI image generators on the design process of first-year design students. Passages from Italo Calvino’s book “Invisible Cities” were given to the students, and they were expected to work in groups of 2-3 to design 3D physical models abstracted from the scenarios by the book’s utopian cities. Since AI image generators work based on text prompts, the students extracted keywords from the book’s chapters to generate images. With the support of the images generated by the AI image generators, they developed final visuals and abstract physical models of utopian cities. Based on the outcomes, we observed that AI image generators were used for three main goals by the students: (1) spatial organization, (2) details of the modules, and (3) abstraction/realization of the spatial atmosphere. Based on this analysis, we discussed the role of AI image generators among the polars of being a tool and co-designer in the design process.
series	ASCAAD
email
last changed	2024/02/13 14:40

_id	ijac202321408
id	ijac202321408
authors	Çiçek, Selen; Gozde Damla Turhan and Aybüke Taºer
year	2023
title	Deterioration of pre-war and rehabilitation of post-war urbanscapes using generative adversarial networks
source	International Journal of Architectural Computing 2023, Vol. 21 - no. 4, 695-711
summary	The 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.
keywords	Post-war, urban rehabilitation, generative adversarial network, CycleGAN, pix2pix GAN, machine learning, artificial intelligence
series	journal
last changed	2024/04/17 14:30

_id	caadria2023_339
id	caadria2023_339
authors	Herr, Christiane M. and Li, Chenxiao
year	2023
title	Articulating Facade Microbiomes at Human Scale
source	Immanuel Koh, Dagmar Reinhardt, Mohammed Makki, Mona Khakhar, Nic Bao (eds.), HUMAN-CENTRIC - Proceedings of the 28th CAADRIA Conference, Ahmedabad, 18-24 March 2023, pp. 281–290
doi	https://doi.org/10.52842/conf.caadria.2023.1.281
summary	Rapid urbanization has led to the deterioration of urban living environments and reduced urban populations’ access to green space. This has not only affected urban residents’ health but also decimated biodiversity in urban environments. In this study, we respond to both issues by introducing a new approach to façade design focusing on microbial biodiversity on building surfaces. Based on the results of an earlier empirical study, we use a custom Cellular Automata (CA) system as generative design strategy to explore the relationships between microbial community diversity and several design factors relevant to creating favourable building surface conditions, in particular surface texture, substrate material characteristics and sunlight exposure. By translating these factors into localized CA design parameters, we create micro-habitats supporting microbial biodiversity in bio-receptive façade elements. Beyond generating desired physical shapes, we employ CA to generate expressive patterns as visually comprehensible articulations of invisible scales of microbial growth.
keywords	cellular automata, generative design, bio-receptive facade design
series	CAADRIA
email
last changed	2023/06/15 23:14

_id	caadria2023_250
id	caadria2023_250
authors	Lertsithichai, Surapong
year	2023
title	Metaverse Magnifique: Making Meaningful Metaverses
source	Immanuel Koh, Dagmar Reinhardt, Mohammed Makki, Mona Khakhar, Nic Bao (eds.), HUMAN-CENTRIC - Proceedings of the 28th CAADRIA Conference, Ahmedabad, 18-24 March 2023, pp. 531–540
doi	https://doi.org/10.52842/conf.caadria.2023.2.531
summary	Being immersive online and physically isolated during the COVID-19 pandemic may not be the future of the metaverse we anticipate. Therefore, the rising challenge of creating metaverses does not lie in what novel virtual experiences can be offered but in how to make the experience more meaningful to users. This involves creating an engaging environment for both physical and virtual social interactions and providing equity for a wide range of users. Metaverse Magnifique intends to explore the design of a meaningful metaverse beyond the computer screen. It investigates a combination of the best features of digital and physical environments with human interactions seamlessly conducted and transitioned between the two worlds while also inquiring a better understanding of the changing social norms from fundamental human activities. A series of experimental Proto-metaverse projects based on a critical review of the Metaverse typology are proposed then developed into small-scale designs to highlight a meaningful behavior, action, task, or activity. The projects are then coupled with a physical site and extended into a full-fledged metaverse project integrated within the city. The projects showcase meaningful experiences by translating visual and spatial elements then enabling AI assistive features for better communication or interpretation of subtle interaction nuances.
keywords	Metaverse, Virtual Reality, Augmented Reality, Mixed Reality, Artificial Intelligence, Digital Twin, Spatial Elements, User Experiences
series	CAADRIA
email
last changed	2023/06/15 23:14

_id	ecaade2023_436
id	ecaade2023_436
authors	Bank Stigsen, Mathias, Moisi, Alexandra, Rasoulzadeh, Shervin, Schinegger, Kristina and Rutzinger, Stefan
year	2023
title	AI Diffusion as Design Vocabulary - Investigating the use of AI image generation in early architectural design and education
source	Dokonal, W, Hirschberg, U and Wurzer, G (eds.), Digital Design Reconsidered - Proceedings of the 41st Conference on Education and Research in Computer Aided Architectural Design in Europe (eCAADe 2023) - Volume 2, Graz, 20-22 September 2023, pp. 587–596
doi	https://doi.org/10.52842/conf.ecaade.2023.2.587
summary	This paper investigates the potential of Text-to-Image AI in assisting the ideation phase in architectural design and education. The study proposes a structured workflow and tests it with first-year architecture students. It aims to create a comprehensive design vocabulary by using AI-generated images as primary design references and incorporating them into a modelling workflow. The paper implements a process combining specific vocabulary extraction, image generation, 2D to 3D translation, and spatial composition within a six weeklong design course. The findings suggest that such a process can enhance the ideation phase by generating new and diverse design inspirations, improve spatial understanding through the exploration of various design elements, and provide students with a targeted visual vocabulary that helps define design intention and streamlines the modelling process.
keywords	Artificial Intelligence, Text-to-Image, Midjourney, Architectural design, Design ideation, 2D to 3D
series	eCAADe
email
last changed	2023/12/10 10:49

_id	sigradi2023_90
id	sigradi2023_90
authors	Codarin, Sara and Daubmann, Karl
year	2023
title	Rom[AI]nterrotta
source	García Amen, F, Goni Fitipaldo, A L and Armagno Gentile, Á (eds.), Accelerated Landscapes - Proceedings of the XXVII International Conference of the Ibero-American Society of Digital Graphics (SIGraDi 2023), Punta del Este, Maldonado, Uruguay, 29 November - 1 December 2023, pp. 705–716
summary	This contribution presents the outcomes of a three-credit elective course offered at Lawrence Technological University’s College of Architecture and Design that involved a week-long travel experience in Rome with undergraduate and graduate students. The course used on-the-fly digital synthetic creations with AI text-to-image and image-to-image generation. The students collectively produced a disciplinary design-fiction tour book for a futuristic Rome, integrated into the city's historical layers. Inspired by the 1978 Roma Interrotta/Interrupted Rome project, the students reimagined the city using AI-informed storytelling to create altered narratives that explored common themes and critical insights. The digital tools allowed students to seamlessly blend AI-generated ideas with photos from the tour, linking historical contexts and contemporary design proposals. The critical use of AI served as a valuable tool in this process, educating designers on the importance of site-specific considerations and capturing the essence of a place through innovative creations informed by their experiences.
keywords	AI, Text-to-Image, Storytelling, Travel Experience, Rome
series	SIGraDi
email
last changed	2024/03/08 14:07

_id	sigradi2023_435
id	sigradi2023_435
authors	Conceiçao, Sandro, Diehl, Natália and Bruscato, Leia
year	2023
title	ChatGPT for Briefing Creation
source	García Amen, F, Goni Fitipaldo, A L and Armagno Gentile, Á (eds.), Accelerated Landscapes - Proceedings of the XXVII International Conference of the Ibero-American Society of Digital Graphics (SIGraDi 2023), Punta del Este, Maldonado, Uruguay, 29 November - 1 December 2023, pp. 819–830
summary	This study aims to investigate the stages involved in document creation during the architecture and design project phases, through the integration of the Briefing with the assistance of ChatGPT-3.5, with the purpose of assessing the relevance, correspondence, and coherence of data generated by Artificial Intelligence (AI). The research involved the use of two distinct prompts: one featuring a more generic nature, and the other defined by a higher level of detail. It was found that the generic prompt yielded innovative options – not foreseen in the reference sources –, however with lower correspondence. The detailed request was limited to the predefined structure, resulting in greater correspondence and partial consistency with the parameters established in the reference literature. It was possible to conclude that, while AI automates the briefing stages, the input and critical analysis of the generated text require substantial subject knowledge for consistent results.
keywords	Artificial Intelligence, ChatGPT, Briefing, Architecture Programme, Design Process.
series	SIGraDi
email
last changed	2024/03/08 14:07

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