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	caadria2022_208
id	caadria2022_208
authors	Bielski, Jessica, Langenhan, Christoph, Ziegler, Christoph, Eisenstadt, Viktor, Petzold, Frank, Dengel, Andreas and Althoff, Klaus-Dieter
year	2022
title	The What, Why, What-If and How-To for Designing Architecture, Explainability for Auto-Completion of Computer-Aided Architectural Design of Floor Plan Layouting During the Early Design Stages
doi	https://doi.org/10.52842/conf.caadria.2022.2.435
source	Jeroen van Ameijde, Nicole Gardner, Kyung Hoon Hyun, Dan Luo, Urvi Sheth (eds.), POST-CARBON - Proceedings of the 27th CAADRIA Conference, Sydney, 9-15 April 2022, pp. 435-444
summary	In the next thirty years, the world's population is expected to increase to ten billion people, posing major challenges for the construction industry. To meet the growing demands for residential housing in the future, architects need to work faster, more efficiently, and more sustainably, while increasing architectural quality. The hypothetical intelligent design assistant WHITE BRIDGE, based on the methods of the 'metis' projects, suggests further design steps to support the architectural design decision-making processes of the early design phases. This facilitates faster and better decisions early in the process for a more responsible resource consumption, better mental well-being, and ultimately economic growth. Through a case study we investigate if additional information supports the understanding of these suggestions to reduce the cognitive workload of architectural design decisions on the backdrop of their respective representation. The paper contributes an approach for visualising explanations of an intelligent design assistant, their integration into paper prototypes for case studies, and a workflow for data collection and analysis. The results suggest that the cognitive horizon of the architects is broadened by the explanations, while the visualisation methods significantly influence the usefulness and use of the conveyed information within the explanations.
keywords	Explainability, Artificial intelligence, XAI, SDG 3, SDG 8, SDG 12
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	ascaad2022_004
id	ascaad2022_004
authors	Falih, Zahraa; Mahdavinejad, Mohammadjavad; Tarawneh, Deyala; Al-Mamaniori, Hamza
year	2022
title	Solar Energy Control Strategy using Interactive Modules
source	Hybrid Spaces of the Metaverse - Architecture in the Age of the Metaverse: Opportunities and Potentials [10th ASCAAD Conference Proceedings] Debbieh (Lebanon) [Virtual Conference] 12-13 October 2022, pp. 117-138
summary	The concept of interactive canopy emerged as a notable manifestation of smart buildings in architectural endeavors, using artificial intelligence applications in computational architecture, interactive canopies came as a potential response for living organisms to combat external environmental changes as well as reduce energy consumption in buildings. This research aims to explore architecture with higher efficiency through the impact of environmentally technological factors on the design form by introducing solar energy into the design process through the implementation of interactive curtains that interact with the sun in the form of an umbrella. The main objective of the umbrellas is to protect the users from the sun's harmful rays. After designing an interactive cell using Grasshopper, the methodology follows an analytical and experimental approach, the analytical section is summarized by conducting a case study of multiple models and analyzing the techniques used in these models to discover the significant advantages and disadvantages of the design. While the experimental section demonstrates the mechanism for implementing the interactive modules. The research suggests that by designing an interactive canopy that responds to external changes and senses solar radiation in ways that when the intensity of solar radiation increases and the sun is perpendicular to the dynamic units, will lead to maintaining a more balanced level of illumination. The work efficiency is studied by simulating it by Climate Studio.
series	ASCAAD
email
last changed	2024/02/16 13:24

_id	acadia23_v3_195
id	acadia23_v3_195
authors	Gandia, Augusto; Iverson, Aileen
year	2023
title	Hybrid Making: Physical Explorations with Computational Matter
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	This publication introduces hybrid making as the subject of a workshop conducted at the ACADIA Conference 2023 (See Fig. 1). We contextualize hybrid making in today’s design digitalization marked by the opening of Artificial Intelligence (AI), wherein AI is seen as an accelerant in the ongoing digital evolution. In design-related practice and research, digital design is increasingly dominant (See Fig. 2); as shown in a quick survey of ACADIA 2022 wherein 10 out of 14 workshops focused on topics related to digitalization. Given this context, the subject of our workshop, hybrid making, highlights that which is excluded in purely digital processes, namely a richness of designing associated with the qualities of materials and fabrication (See Fig. 3). Hybrid making seeks to influence digital evolution with aspects of analogue processes such as the integration of constraints related to actual physical materials and their context. The task of hybrid making, therefore, is to introduce actual constraints into digital ones (See Fig. 4).
series	ACADIA
type	workshop
email
last changed	2024/04/17 14:00

_id	caadria2022_231
id	caadria2022_231
authors	Kim, Frederick Chando and Huang, Jeffrey
year	2022
title	Deep Architectural Archiving (DAA), Towards a Machine Understanding of Architectural Form
doi	https://doi.org/10.52842/conf.caadria.2022.1.727
source	Jeroen van Ameijde, Nicole Gardner, Kyung Hoon Hyun, Dan Luo, Urvi Sheth (eds.), POST-CARBON - Proceedings of the 27th CAADRIA Conference, Sydney, 9-15 April 2022, pp. 727-736
summary	With the ‚digital turn‚, machines now have the intrinsic capacity to learn from big data in order to understand the intricacies of architectural form. This paper explores the research question: how can architectural form become machine computable? The research objective is to develop "Deep Architectural Archiving‚ (DAA), a new method devised to address this question. DAA consists of the combination of four distinct steps: (1) Data mining, (2) 3D Point cloud extraction, (3) Deep form learning, as well as (4) Form mapping and clustering. The paper discusses the DAA method using an extensive dataset of architecture competitions in Switzerland (with over 360+ architectural projects) as a case study resource. Machines learn the particularities of forms using 'architectural' point clouds as an opportune machine-learnable format. The result of this procedure is a multidimensional, spatialized, and machine-enabled clustering of forms that allows for the visualization of comparative relationships among form-correlated datasets that exceeds what the human eye can generally perceive. Such work is necessary to create a dedicated digital archive for enhancing the formal knowledge of architecture and enabling a better understanding of innovation, both of which provide architects a basis for developing effective architectural form in a post-carbon world.
keywords	artificial intelligence, deep learning, architectural form, architectural competitions, architectural archive, 3D dataset, SDG 11
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	sigradi2022_168
id	sigradi2022_168
authors	Koh, Immanuel
year	2022
title	Palette2Interior Architecture: From Syntactic and Semantic Colour Palettes to Generative Interiors with Deep Learning
source	Herrera, PC, Dreifuss-Serrano, C, Gómez, P, Arris-Calderon, LF, Critical Appropriations - Proceedings of the XXVI Conference of the Iberoamerican Society of Digital Graphics (SIGraDi 2022), Universidad Peruana de Ciencias Aplicadas, Lima, 7-11 November 2022 , pp. 187–198
summary	Colour palettes have long played a significant role in not only capturing design ambience (e.g., as mood boards), but more significantly, in translating an abstract intuition into an explicit ordering mechanism for design representation and synthesis, whether it is in the discipline of graphic design, interior design or architectural design. Might this difficult process of design synthesis from a low-dimensional colour input domain to a high-dimensional spatial design output domain be computationally mapped? Using today’s generative adversarial networks (GANs), the paper aims to investigate this plausibility, and in doing so, hoping to envision an AI-augmented design workflow and tooling. Newly-created datasets are made procedurally and used to train three different types of deep learning models in the specific context of generating living room interior layouts. The results suggest that a combination of syntactic and semantic generative processes is necessary for a critical appropriation of such AI models
keywords	Machine Learning, Artificial Intelligence, Deep Neural Networks, Colour Palette, Interior Design
series	SIGraDi
email
last changed	2023/05/16 16:55

_id	architectural_intelligence2022_12
id	architectural_intelligence2022_12
authors	Matias del Campo
year	2022
title	Deep House - datasets, estrangement, and the problem of the new
doi	https://doi.org/https://doi.org/10.1007/s44223-022-00013-w
source	Architectural Intelligence Journal
summary	The purpose of this article is to discuss the application of artificial intelligence (AI) in the design of the Deep House project (Fig. 1), an attempt to use estrangement as a method to emancipate a house from a canonical approach to the progressive design of a one-family house project. The main argument in this text is that the results created by Artificial Neural Networks (ANNs), whether in the form of GANs, CNNs, or other networks, generate results that fall into the category of Estranged objects. In this article, I would like to offer a possible definition of what architecture in this plateau of thinking represents and how it differentiates from previous attempts to use estrangement to explain the phenomena observed when working with NNs in architecture design. A potpourri of thoughts that demonstrate the intellectual tradition of exploring estrangement, especially in theater and literature, that ultimately circles back to its implications for architecture, particularly in light of the application of AI.
series	Architectural Intelligence
email
last changed	2025/01/09 15:00

_id	ascaad2022_060
id	ascaad2022_060
authors	Senem, Mehmet; Koc, Mustafa; Tuncay, Hayriye; As, Imdat
year	2022
title	Using Deep Learning to Generate Front and Backyards in Landscape Architecture
source	Hybrid Spaces of the Metaverse - Architecture in the Age of the Metaverse: Opportunities and Potentials [10th ASCAAD Conference Proceedings] Debbieh (Lebanon) [Virtual Conference] 12-13 October 2022, pp. 2-16
summary	The use of artificial intelligence (AI) engines in the design disciplines is a nascent field of research, which became very popular over the last decade. In particular, deep learning (DL) and related generative adversarial networks (GANs) proved to be very promising. While there are many research projects exploring AI in architecture and urban planning, e.g., in order to generate optimal floor layouts, massing models, evaluate image quality, etc., there are not many research projects in the area of landscape architecture - in particular the design of two-dimensional garden layouts. In this paper, we present our work using GANs to generate optimal front- and backyard layouts. We are exploring various GAN engines, e.g., DCGAN, that have been successfully used in other design disciplines. We used supervised and unsupervised learning utilizing a massive dataset of about 100,000 images of front- and backyard layouts, with qualitative and quantitative attributes, e.g., idea and beauty scores, as well as functional and structural evaluation scores. We present the results of our work, i.e., the generation of garden layouts, and their evaluation, and speculate on how this approach may help landscape architects in developing their designs. The outcome of the study may also be relevant to other design disciplines.
series	ASCAAD
email
last changed	2024/02/16 13:29

_id	artificial_intellicence2019_117
id	artificial_intellicence2019_117
authors	Stanislas Chaillou
year	2020
title	ArchiGAN: Artificial Intelligence x Architecture
doi	https://doi.org/https://doi.org/10.1007/978-981-15-6568-7_8
source	Architectural Intelligence Selected Papers from the 1st International Conference on Computational Design and Robotic Fabrication (CDRF 2022)
summary	AI will soon massively empower architects in their day-to-day practice. This article provides a proof of concept. The framework used here offers a springboard for discussion, inviting architects to start engaging with AI, and data scientists to consider Architecture as a field of investigation. In this article, we summarize a part of our thesis, submitted at Harvard in May 2019, where Generative Adversarial Neural Networks (or GANs) get leveraged to design floor plans and entire buildings .
series	Architectural Intelligence
email
last changed	2022/09/29 07:28

_id	architectural_intelligence2022_6
id	architectural_intelligence2022_6
authors	Achim Menges, Fabian Kannenberg & Christoph Zechmeister
year	2022
title	Computational co-design of fibrous architecture
doi	https://doi.org/https://doi.org/10.1007/s44223-022-00004-x
source	Architectural Intelligence Journal
summary	Fibrous architecture constitutes an alternative approach to conventional building systems and established construction methods. It shows the potential to converge architectural concerns such as spatial expression and structural elegance, with urgently required resource effectiveness and material efficiency, in a genuinely computational approach. Fundamental characteristics of fibre composite are shared with fibre structures in the natural world, enabling the transfer of design principles and providing a vast repertoire of inspiration. Robotic fabrication based on coreless filament winding, a technique to deposit resin impregnated fibre filaments with only minimal formwork, as well as integrative computational design methods are imperative to the development of complex fibrous building systems. Two projects, the BUGA Fibre Pavilion as an example for long-span structures, and Maison Fibre as an example of multi-storey architecture, showcase the application of those techniques in an architectural context and highlight areas of further research opportunities. The highly interrelated aesthetic, structural and fabrication characteristics of fibre nets are difficult to understand and go beyond a designer’s comprehension and intuition. An AI powered, self-learning agent system aims to extend and thoroughly explore the design space of fibre structures to unlock the full design potential coreless filament winding offers. In order to ensure feedback between all relevant design and performance criteria and enable interdisciplinary convergence, these novel design methods are embedded in a larger co-design framework. It formalizes the interaction of involved interdisciplinary domains and allows for interactive collaboration based on a central data model, serving as a base for design optimisation and exploration. To further advance research on fibre composites in architecture, bio-based materials are considered, continuing the journey of discovery of fibrous architecture to fundamentally rethinking design and construction towards a novel, computational material culture in architecture.
series	Architectural Intelligence
email
last changed	2025/01/09 15:00

_id	ijac202220204
id	ijac202220204
authors	BuHamdan, Samer; Aladdin Alwisy; Thomas Danel; Ahmed Bouferguene; Zoubeir Lafhaj
year	2022
title	The use of reinforced learning to support multidisciplinary design in the AEC industry: Assessing the utilization of Markov Decision Process
source	International Journal of Architectural Computing 2022, Vol. 20 - no. 2, pp. 216–237
summary	While the design practice in the architecture, engineering, and construction (AEC) industry continues to be acreative activity, approaching the design problem from a perspective of the decision-making science hasremarkable potentials that manifest in the delivery of high-performing sustainable structures. These possiblegains can be attributed to the myriad of decision-making tools and technologies that can be implemented toassist design efforts, such as artificial intelligence (AI) that combines computational power and data wisdom.Such combination comes to extreme importance amid the mounting pressure on the AEC industry players todeliver economic, environmentally friendly, and socially considerate structures. Despite the promisingpotentials, the utilization of AI, particularly reinforced learning (RL), to support multidisciplinary designendeavours in the AEC industry is still in its infancy. Thus, the present research discusses developing andapplying a Markov Decision Process (MDP) model, an RL application, to assist the preliminary multidisciplinary design efforts in the AEC industry. The experimental work shows that MDP models can expediteidentifying viable design alternatives within the solutions space in multidisciplinary design while maximizingthe likelihood of finding the optimal design
keywords	Design evaluation, multidisciplinary design, reinforced learning, Markov Decision Process, social impact,architecture, engineering, and construction industry
series	journal
last changed	2024/04/17 14:29

_id	ascaad2022_085
id	ascaad2022_085
authors	Cicek, Selen; Koc, Mustafa; Korukcu, Berfin
year	2022
title	Urban Map Generation in Artist's Style using Generative Adversarial Networks (GAN)
source	Hybrid Spaces of the Metaverse - Architecture in the Age of the Metaverse: Opportunities and Potentials [10th ASCAAD Conference Proceedings] Debbieh (Lebanon) [Virtual Conference] 12-13 October 2022, pp. 264-282
summary	Artificial Intelligence is a field that is able to learn from existing data to synthesize new ones using deep learning methods. Using Artificial Neural Networks that process big datasets, complex tasks and challenges become easily resolved. As the zeitgeist suggests, it is possible to produce novel outcomes for future projections by applying various machine learning algorithms on the generated data sets. In that context, the focus of this research is exploring the reinterpretation of 21st century urban plans with familiar artist styles using different subtypes of deep-learning-based generative adversarial networks (GAN) algorithms. In order to explore the capabilities of urban map transformation with machine learning approaches, two different GAN algorithms which are cycleGAN and styleGAN have been applied on the two main data sets. First data set, the urban data set, contains 50 cities urban plans in .jpeg format collected according to the diversity of the urban morphologies. Whereas the second data set is composed of four well-known artist’s paintings, that belong to various artistic movements. As a result of training the same data sets with different GAN algorithms and epoch values were compared and evaluated. In this respect, the study not only investigates the reinterpretation of stylistic urban maps and shows the discoverability of new representation techniques, but also offers a comparison of the use of different image to image translation GAN algorithms.
series	ASCAAD
email
last changed	2024/02/16 13:29

_id	caadria2022_166
id	caadria2022_166
authors	Eisenstadt, Viktor, Bielski, Jessica, Mete, Burak, Langenhan, Christoph, Althoff, Klaus-Dieter and Dengel, Andreas
year	2022
title	Autocompletion of Floor Plans for the Early Design Phase in Architecture: Foundations, Existing Methods, and Research Outlook
doi	https://doi.org/10.52842/conf.caadria.2022.1.323
source	Jeroen van Ameijde, Nicole Gardner, Kyung Hoon Hyun, Dan Luo, Urvi Sheth (eds.), POST-CARBON - Proceedings of the 27th CAADRIA Conference, Sydney, 9-15 April 2022, pp. 323-332
summary	This paper contributes the current research state and possible future developments of AI-based autocompletion of architectural floor plans and shows demand for its establishment in computer-aided architectural design to facilitate decent work, economic growth through accelerating the design process to meet the future workload. Foundations of data representations together with the autocompletion contexts are defined, existing methods described and evaluated in the integrated literature review, and criteria for qualitative and sustainable autocompletion are proposed. Subsequently, we contribute three unique deep learning-based autocompletion methods currently in development for the research project metis-II. They are described in detail from a technical point of view on the backdrop of how they adhere to the proposed criteria for creating our novel AI.
keywords	Artificial Intelligence, Architectural Design, Floor Plan, Autocompletion, SDG 8, SDG 9
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	ascaad2022_000
id	ascaad2022_000
authors	El-Bastawissi, Ibtihal Y.; Abdelmohsen, Sherif
year	2022
title	ASCAAD 2022: Hybrid Spaces of the Metaverse
source	Hybrid Spaces of the Metaverse - Architecture in the Age of the Metaverse: Opportunities and Potentials [10th ASCAAD Conference Proceedings] Debbieh (Lebanon) [Virtual Conference] 12-13 October 2022, 743 p.
summary	The ASCAAD 2022 theme focuses on Hybrid Spaces of the Metaverse, with the aim of unraveling the opportunities and potentials of architecture in the age of the Metaverse. Historically space was always the container of people’s activities and memories; it is the collective reflection of their life styles. Walls, floors and ceilings of architectural spaces witnessed the moments of joy and happiness, as well as moments of misery that changed human history, from the signing of the United Nations Declaration post WWII, to the first I-phone sold in the Apple store; history is written inside architectural spaces. The new era of the 4th industrial revolution, which is associated with digital transformation, will unlock new opportunities for architects, interior designers and whoever will enter the domain of the metaverse. The metaverse will not only serve as a portal to a new world, but also as an extension to new activities such as commercial, social, educational and business activities that will thrive in the new virtual realm. The metaverse will act as the natural transcendence of technological advancements carrying new potentials to the architectural profession. Active Worlds, Second Life, Roblox and Fortnite are all early versions of what we will witness in the next few years, shifting from entertainment to full commercial, official and governmental activities; all will be hosted inside virtual and hybrid spaces. A new era will start inside virtual realms; real economy will rise inside virtual architecture but without the multiple physical or structural constraints that limit physicality anymore such as gravity, and day and night cycles; no oxygen is needed anymore. But this time, human activities will not only be recorded and saved but also attended and lived in real time. Computational design will continue to thrive and even evolve into new forms aligning with new changes and challenges of the metaverse. Hybrid spaces are the spaces that will be built as a virtual extension of real spaces. They will be in connection to real spaces and reflecting their activities on a real time basis. On the other hand, pure virtual spaces will occur, trespassing time zones and geographical barriers. The importance of hybrid experiences was most realized after the pandemic lockdowns; and now is the time to invent new design methodologies and new theories as a natural transcendence of architecture profession. Hyperlinks portals replacing staircases and elevators, physically impossible structures, open budget interiors, teleportation are all new notions emerging with the new domain. Today, virtual spaces are hosted on various cloud services and registered as Non-Fungible Tokens (NFTs). They are experienced as immersed spaces using headsets or semi immersed spaces presented through laptops and/or mobile screens. With the new accelerating pace of technology, there is high possibility for integration within our neural networks to be experienced in our minds with just closing our eyes in the near future.
series	ASCAAD
email
last changed	2024/02/16 13:24

_id	acadia22_662
id	acadia22_662
authors	Furgiuele, Antonio; Ergezer, Mehmet; Zaman, Cagri Hakan
year	2022
title	Towards an Adversarial Architecture
source	ACADIA 2022: Hybrids and Haecceities [Proceedings of the 42nd Annual Conference of the Association of Computer Aided Design in Architecture (ACADIA) ISBN 979-8-9860805-8-1]. University of Pennsylvania Stuart Weitzman School of Design. 27-29 October 2022. edited by M. Akbarzadeh, D. Aviv, H. Jamelle, and R. Stuart-Smith. 662-671.
summary	A key technological weakness of artificial intelligence (AI) is adversarial images, a constructed form of image-noise added to an image that can manipulate machine learning algorithms but is imperceptible to humans. Adversarial Architecture explores the application of adversarial images to the built environment and develops a new method of design agency to directly engage artificial intelligence. Embedding a layer of information to physical surfaces that is only perceptible to machines has many potential applications, such as uniquely identifying and tracking objects, embedding accessibility features directly to surfaces, and counter-surveillance systems in different scales.
series	ACADIA
type	paper
email
last changed	2024/02/06 14:04

_id	ecaade2022_203
id	ecaade2022_203
authors	Kim, Frederick Chando and Huang, Jeffrey
year	2022
title	Perspectival GAN - Architectural form-making through dimensional transformation
doi	https://doi.org/10.52842/conf.ecaade.2022.1.341
source	Pak, B, Wurzer, G and Stouffs, R (eds.), Co-creating the Future: Inclusion in and through Design - Proceedings of the 40th Conference on Education and Research in Computer Aided Architectural Design in Europe (eCAADe 2022) - Volume 1, Ghent, 13-16 September 2022, pp. 341–350
summary	With the ascendance of Generative Adversarial Networks (GAN), promising prospects have arisen from the abilities of machines to learn and recognize patterns in 2D datasets and generate new results as an inspirational tool in architectural design. Insofar as the majority of ML experiments in architecture are conducted with imagery based on readily available 2D data, architects and designers are faced with the challenge of transforming machine-generated images into 3D. On the other hand, GAN-generated images are found to be able to learn the 3D information out of 2D perspectival images. To facilitate such transformation from 2D and 3D data in the framework of deep learning in architecture, this paper explores making new architectural forms from flat GAN images by employing traditional tools of projective geometry. The experiments draw on Brook Taylor’s 19th- century theorem of inverse projection system for creating architectural form from perspectival information learned from GAN images of Swiss alpine architecture. The research develops a parametric tool that automates the dimensional transformation of 2D images into 3D architectural forms. This research identifies potential synergic interactions between traditional tools and techniques of architects and deep learning algorithms to achieve collective intelligence in designing and representing creative architecture forms between humans and machines.
keywords	Machine Learning, GAN, Architectural Form, Perspective Projection, Inverse Perspective, Digital Representation
series	eCAADe
email
last changed	2024/04/22 07:10

_id	ecaade2022_176
id	ecaade2022_176
authors	Kotov, Anatolii, Starke, Rolf and Vukorep, Ilija
year	2022
title	Spatial Agent-based Architecture Design Simulation Systems
doi	https://doi.org/10.52842/conf.ecaade.2022.2.105
source	Pak, B, Wurzer, G and Stouffs, R (eds.), Co-creating the Future: Inclusion in and through Design - Proceedings of the 40th Conference on Education and Research in Computer Aided Architectural Design in Europe (eCAADe 2022) - Volume 2, Ghent, 13-16 September 2022, pp. 105–112
summary	This paper presents case studies and analysis of agent-based reinforcement learning (RL) systems towards practical applications for specific architecture/engineering tasks using Unity 3D-based simulation methods. Finding and implementing sufficient abstraction for architecture and engineering problems to be solved by agent-based systems requires broad architectural knowledge and the ability to break down complex problems. Modern artificial intelligence (AI) and machine learning (ML) systems based on artificial neural networks can solve complex problems in different domains such as computer vision, language processing, and predictive maintenance. The paper will give a theoretical overview, such as more theoretical abstractions like zero-sum games, and a comparison of presented games. The application section describes a possible categorization of practical usages. From more general applications to more narrowed ones, we explore current possibilities of RL application in the field of relatable problems. We use the Unity 3D engine as the basis of a robust simulation environment.
keywords	AI Aided Architecture, Reinforcement Learning, Agent Simulation
series	eCAADe
email
last changed	2024/04/22 07:10

_id	caadria2022_120
id	caadria2022_120
authors	Lin, Yuxin
year	2022
title	Rhetoric, Writing, and Anexact Architecture: The Experiment of Natural Language Processing (NLP) and Computer Vision (CV) in Architectural Design
doi	https://doi.org/10.52842/conf.caadria.2022.1.343
source	Jeroen van Ameijde, Nicole Gardner, Kyung Hoon Hyun, Dan Luo, Urvi Sheth (eds.), POST-CARBON - Proceedings of the 27th CAADRIA Conference, Sydney, 9-15 April 2022, pp. 343-352
summary	This paper presents a novel language-driven and artificial intelligence-based architectural design method. This new method demonstrates the ability of neural networks to integrate the language of form through written texts and has the potential to interpret the texts into sustainable architecture under the topic of the coexistence between technologies and humans. The research merges natural language processing, computer vision, and human-machine interaction into a machine learning-to-design workflow. This article encompasses the following topics: 1) an experiment of rethinking writing in architecture through anexact form as rhetoric; 2) an integrative machine learning design method incorporating Generative Pre-trained Transformer 2 model and Attentional Generative Adversarial Networks for sustainable architectural production with unique spatial feeling; 3) a human-machine interaction framework for model generation and detailed design. The whole process is from inexact to exact, then finally anexact, and the key result is a proof-of-concept project: Anexact Building, a mixed-use building that promotes sustainability and multifunctionality under the theme of post-carbon. This paper is of value to the discipline since it applies current and up-to-date digital tools research into a practical project.
keywords	Rhetoric and writing, Natural Language Processing, Computer Vision, GPT-2, AttnGAN, Human-computer Interaction, Architectural Design, Post-carbon, SDG3, SDG11
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	architectural_intelligence2022_13
id	architectural_intelligence2022_13
authors	Mette Ramsgaard Thomsen
year	2022
title	Computational design logics for bio-based design
doi	https://doi.org/https://doi.org/10.1007/s44223-022-00015-8
source	Architectural Intelligence Journal
summary	This paper examines how the central contributions of the computational design field can be understood as central steppingstones into an age of sustainability to engage with new renewable, regenerative and restorative material systems. By taking departure in the conceptualisation of an extended digital chain by which architecture can address fabrication at the low scales of the material, this paper asks how these methodological innovations can be transferred to new questions arising from a bio-based material paradigm. The paper outlines the three central contributions of the computational design field: advanced information modelling, functional grading and integrated sensing, and suggests how these can be extended to allow new means of instrumentation for bio-based materials characterised by the heterogeneous, the behaving and the living.
series	Architectural Intelligence
email
last changed	2025/01/09 15:00

_id	acadia23_v1_242
id	acadia23_v1_242
authors	Noel, Vernelle A.
year	2023
title	Carnival + AI: Heritage, Immersive virtual spaces, and Machine Learning
source	ACADIA 2023: Habits of the Anthropocene: Scarcity and Abundance in a Post-Material Economy [Volume 1: Projects Catalog of the 43rd Annual Conference of the Association of Computer Aided Design in Architecture (ACADIA) ISBN 979-8-9860805-8-1]. Denver. 26-28 October 2023. edited by A. Crawford, N. Diniz, R. Beckett, J. Vanucchi, M. Swackhamer 242-245.
summary	Built on a Situated Computations framework, this project explores preservation, reconfiguration, and presentation of heritage through immersive virtual experiences, and machine learning for new understandings and possibilities (Noel 2020; 2017; Leach and Campo 2022; Leach 2021). Using the Trinidad and Tobago Carnival - hereinafter referred to as Carnival - as a case study, Carnival + AI is a series of immersive experiences in design, culture, and artificial intelligence (AI). These virtual spaces create new digital modes of engaging with cultural heritage and reimagined designs of traditional sculptures in the Carnival (Noel 2021). The project includes three virtual events that draw on real events in the Carnival: (1) the Virtual Gallery, which builds on dancing sculptures in the Carnival and showcases AI-generated designs; (2) Virtual J’ouvert built on J’ouvert in Carnival with AI-generated J’ouvert characters specific; and (3) Virtual Mas which builds on the masquerade.
series	ACADIA
type	project
email
last changed	2024/04/17 13:58

_id	ascaad2022_063
id	ascaad2022_063
authors	Ozman, Gizem; Selcuk, Semra
year	2022
title	Generating Mass Housing Plans through GANs: A case in TOKI, Turkey
source	Hybrid Spaces of the Metaverse - Architecture in the Age of the Metaverse: Opportunities and Potentials [10th ASCAAD Conference Proceedings] Debbieh (Lebanon) [Virtual Conference] 12-13 October 2022, pp. 17-29
summary	Nowadays, Machine Learning (ML) is frequently used in almost all disciplines having an intersection with technology. Recently, architects are using existing plan data sets in architecture through Deep Learning (DL) algorithms of big data to achieve generative and non-existent plan models by using ML. Especially, Generative Adversarial Neural Networks (GANs), one of the deep learning algorithms, have been in use in the creation of generative models for architectural studies. Within the scope of this paper, architectural drawings were generated by using GANs. This generation method allows for the training of spatial layout planning to networks and for the generation of plans that do not exist in the dataset. Architectural drawings of TOKI (Housing Development Administration of the Republic of Türkiye) mass housing projects were used as datasets. In line with studies already carried out, this study attempts to create a method for further processing of the research. In this study, the differences between the plan typologies generated with raster images and the reality relations in visual productions between graph-based plan layout productions were evaluated. In this context, 157 plan datasets were obtained by multiplying plans which were spatially correlated with the RGB settings of 21 plan typologies. As a result of this research, it has been determined that the spatial layout planning of the HouseGAN algorithm provides TOK?'s current plan typologies of generation together with bubble diagrams. HouseGAN was trained using its dataset and the outputs obtained were realistic background images.
series	ASCAAD
email
last changed	2024/02/16 13:29

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