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	ascaad2021_022
id	ascaad2021_022
authors	Baºarir, Lale; Kutluhan Erol
year	2021
title	Briefing AI: From Architectural Design Brief Texts to Architectural Design Sketches
source	Abdelmohsen, S, El-Khouly, T, Mallasi, Z and Bennadji, A (eds.), Architecture in the Age of Disruptive Technologies: Transformations and Challenges [9th ASCAAD Conference Proceedings ISBN 978-1-907349-20-1] Cairo (Egypt) [Virtual Conference] 2-4 March 2021, pp. 23-31
summary	The main focus of this research is to uncover the underlying intuitive knowledge of architecture with the help of machine learning models. To achieve this, a generic architectural design process is considered and divided into iterative portions based on their output for each phase. This study looks into the initial portion of the architectural design process called “Briefing”. The authors search for the intuition that exists within the design process and how it can be learned by artificial intelligence (AI) that is currently gained through master-apprentice relationship and experience that builds up this knowledge. In this study, a way to enable users to attain an architectural design sketch while defining an architectural design problem with text is explored. This on-going research decomposes the components of the briefing and preliminary design sketching processes. Therefore the domain knowledge at each phase is considered for translating to constraints via natural language processing (NLP) and machine learning (ML) models such as Generative Adversarial Networks (GANs).
series	ASCAAD
type	normal paper
email
last changed	2021/08/09 13:11

_id	caadria2021_389
id	caadria2021_389
authors	del Campo, Matias
year	2021
title	Architecture,Language and AI - Language,Attentional Generative Adversarial Networks (AttnGAN) and Architecture Design
doi	https://doi.org/10.52842/conf.caadria.2021.1.211
source	A. Globa, J. van Ameijde, A. Fingrut, N. Kim, T.T.S. Lo (eds.), PROJECTIONS - Proceedings of the 26th CAADRIA Conference - Volume 1, The Chinese University of Hong Kong and Online, Hong Kong, 29 March - 1 April 2021, pp. 211-220
summary	The motivation to explore Attentional Generative Adversarial Networks (AttnGAN) as a design technique in architecture can be found in the desire to interrogate an alternative design methodology that does not rely on images as starting point for architecture design, but language. Traditionally architecture design relies on visual language to initiate a design process, wither this be a napkin sketch or a quick doodle in a 3D modeling environment. AttnGAN explores the information space present in programmatic needs, expressed in written form, and transforms them into a visual output. The key results of this research are shown in this paper with a proof-of-concept project: the competition entry for the 24 Highschool in Shenzhen, China. This award-winning project demonstrated the ability of GraphCNN to serve as a successful design methodology for a complex architecture program. In the area of Neural Architecture, this technique allows to interrogate shape through language. An alternative design method that creates its own unique sensibility.
keywords	Artificial Intelligence; Machine Learning; Artificial Neural Networks; Semiotics; Design Methodology
series	CAADRIA
email
last changed	2022/06/07 07:55

_id	caadria2021_446
id	caadria2021_446
authors	Zhou, Yifan and Park, Hyoung-June
year	2021
title	Sketch with Artificial Intelligence (AI) - A Multimodal AI Approach for Conceptual Design
doi	https://doi.org/10.52842/conf.caadria.2021.1.201
source	A. Globa, J. van Ameijde, A. Fingrut, N. Kim, T.T.S. Lo (eds.), PROJECTIONS - Proceedings of the 26th CAADRIA Conference - Volume 1, The Chinese University of Hong Kong and Online, Hong Kong, 29 March - 1 April 2021, pp. 201-210
summary	The goal of the research is to investigate an AI approach to assist architects with multimodal inputs (sketches and textual information) for conceptual design. With different textual inputs, the AI approach generates the architectural stylistic variations of a users initial sketch input as a design inspiration. A novel machine learning approach for the multimodal input system is introduced and compared to other approaches. The machine learning approach is performed through procedural training with the content curation of training data in order to control the fidelity of generated designs from the input and to manage their diversity. In this paper, the framework of the proposed AI approach is explained. Furthermore, the implementation of its prototype is demonstrated with various examples.
keywords	Artificial Intelligence; Stylistic Variations; Multimodal Input; Content Curation; Procedural Training
series	CAADRIA
email
last changed	2022/06/07 07:57

_id	sigradi2021_234
id	sigradi2021_234
authors	Al Nouri, Mhd Ziwar, Baghdadi, Bilal and Khateeb, Nairooz
year	2021
title	Re-coding Post-War Syria: The Role of Data Collection & Objective Investigations in PostWar Smart City
source	Gomez, P and Braida, F (eds.), Designing Possibilities - Proceedings of the XXV International Conference of the Ibero-American Society of Digital Graphics (SIGraDi 2021), Online, 8 - 12 November 2021, pp. 127–145
summary	Re-coding post-war Syria is an ongoing research and data platform, focused on innovation and collecting comprehensive, infrastructural and socioeconomic analytics, synchronization data, by using AI driven to give a more transparent image of innovating a new methodology to regenerate the future of post-war smart cities into advanced and sustainable urban environments in a smarter way (Fig. 1). The pressure to achieve a rapid Post-war smart city without clear strategy and comprehensive analysis of all aspects will cause a particularly catastrophic collapse in the interconnected social structure, services, education and health care system, leaving a long-term impact on the society. This paper presents the current status of the Research & Documentation methodology in the Data Collection phase by the objective investigations conducted through a series of local and international workshops species developed in this research called “Re-Coding“, offering consequent direct ground surveys, statistics and documentation study of the targeted areas, merging professionalism and youth power with local community to detect an open source data used as a tool to re-generate a precarious area towards a new methodology.
keywords	Post-War Smart cities, Collecting Data, Local community, Objective Investigations, Artificial intelligence
series	SIGraDi
email
last changed	2022/05/23 12:10

_id	ecaade2021_203
id	ecaade2021_203
authors	Arora, Hardik, Bielski, Jessica, Eisenstadt, Viktor, Langenhan, Christoph, Ziegler, Christoph, Althoff, Klaus-Dieter and Dengel, Andreas
year	2021
title	Consistency Checker - An automatic constraint-based evaluator for housing spatial configurations
doi	https://doi.org/10.52842/conf.ecaade.2021.2.351
source	Stojakovic, V and Tepavcevic, B (eds.), Towards a new, configurable architecture - Proceedings of the 39th eCAADe Conference - Volume 2, University of Novi Sad, Novi Sad, Serbia, 8-10 September 2021, pp. 351-358
summary	The gradual rise of artificial intelligence (AI) and its increasing visibility among many research disciplines affected Computer-Aided Architectural Design (CAAD). Architectural deep learning (DL) approaches are being developed and published on a regular basis, such as retrieval (Sharma et al. 2017) or design style manipulation (Newton 2019; Silvestre et al. 2016). However, there seems to be no method to evaluate highly constrained spatial configurations for specific architectural domains (such as housing or office buildings) based on basic architectural principles and everyday practices. This paper introduces an automatic constraint-based consistency checker to evaluate the coherency of semantic spatial configurations of housing construction using a small set of design principles to evaluate our DL approaches. The consistency checker informs about the overall performance of a spatial configuration followed by whether it is open/closed and the constraints it didn't satisfy. This paper deals with the relation of spaces processed as mathematically formalized graphs contrary to existing model checking software like Solibri.
keywords	model checking, building information modeling, deep learning, data quality
series	eCAADe
email
last changed	2022/06/07 07:54

_id	ascaad2021_112
id	ascaad2021_112
authors	Hassab, Ahmed; Sherif Abdelmohsen, Mohamed Abdallah
year	2021
title	Generative Design Methodology for Double Curved Surfaces using AI
source	Abdelmohsen, S, El-Khouly, T, Mallasi, Z and Bennadji, A (eds.), Architecture in the Age of Disruptive Technologies: Transformations and Challenges [9th ASCAAD Conference Proceedings ISBN 978-1-907349-20-1] Cairo (Egypt) [Virtual Conference] 2-4 March 2021, pp. 622-635
summary	Despite recent approaches to generate unique surfaces using generative design algorithms, there are still challenges including teaching machines how to learn and manipulate surfaces, thus generating novel and unique versions, and exploring possible alternatives in producing unique surfaces using artificial intelligence. This paper proposes a generative design approach using Al. We propose a generative design methodology for producing novel and unique surfaces by faking input surfaces using artificial intelligence networks. This workflow is applied to two different artificial networks: (1) CycleGAN, (2) Pix2Pix and Augmentor. This experimentation is introduced to apply two real surfaces generating two fake surfaces as a unique version through the networks. Upon running the CycleGANs, Pix2Pix, and a Grasshopper script, the experiment results demonstrated how the proposed generative design methodology using AI produced a unique surface version with a higher level of manipulation and result control.
series	ASCAAD
email
last changed	2021/08/09 13:13

_id	cdrf2021_3
id	cdrf2021_3
authors	Jean Jaminet, Gabriel Esquivel, and Shane Bugni
year	2021
title	Serlio and Artificial Intelligence: Problematizing the Image-to-Object Workflow
doi	https://doi.org/https://doi.org/10.1007/978-981-16-5983-6_1
source	Proceedings of the 2021 DigitalFUTURES The 3rd International Conference on Computational Design and Robotic Fabrication (CDRF 2021)
summary	Virtual design production demands that information be increasingly encoded and decoded with image compression technologies. Since the Renaissance, the discourses of language and drawing and their actuation by the classical disciplinary treatise have been fundamental to the production of knowledge within the building arts. These early forms of data compression provoke reflection on theory and technology as critical counterparts to perception and imagination unique to the discipline of architecture. This research examines the illustrated expositions of Sebastiano Serlio through the lens of artificial intelligence (AI). The mimetic powers of technological data storage and retrieval and Serlio’s coded operations of orthographic projection drawing disclose other aesthetic and formal logics for architecture and its image that exist outside human perception. Examination of aesthetic communication theory provides a conceptual dimension of how architecture and artificial intelligent systems integrate both analog and digital modes of information processing. Tools and methods are reconsidered to propose alternative AI workflows that complicate normative and predictable linear design processes. The operative model presented demonstrates how augmenting and interpreting layered generative adversarial networks drive an integrated parametric process of three-dimensionalization. Concluding remarks contemplate the role of human design agency within these emerging modes of creative digital production.
series	cdrf
email
last changed	2022/09/29 07:53

_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	architectural_intelligence2023_16
id	architectural_intelligence2023_16
authors	Philip F. Yuan
year	2023
title	Toward a generative AI-augmented design era
doi	https://doi.org/https://doi.org/10.1007/s44223-023-00038-9
source	Architectural Intelligence Journal
summary	With the rapid development of Artificial Intelligence (AI), the relationship between humans and machines has become a significant concern. One view suggests that AI will possess subjectivity: Matias del Campo emphasises that, unlike traditional tools that teach machines how to perform, artificial intelligence teaches machines how to learn (Campo, 2022). According to him, AI has the capability and awareness to recognise the world; Neil Leach et al. argue that AI will replace the majority of architects, resulting in widespread unemployment (Leach, 2021). Other opinions hold that AI is unconscious, incapable of thought, and identical to tools such as cellular automata machines, parameterisation, etc. According to Mario Carpo, the data-driven AI employs iterative optimisation to solve problems, which must be quantifiable and amenable to optimisation. Therefore, AI’s role as a tool is limited to measurable phenomena and factors (Carpo, 2023).
series	Architectural Intelligence
email
last changed	2025/01/09 15:03

_id	acadia21_572
id	acadia21_572
authors	Rodrigues, Ricardo Cesar; Alzate-Martinez, Fábio A.; Escobar, Daniel; Mistry, Mayur
year	2021
title	Rendering Conceptual Design Ideas with Artificial Intelligence
doi	https://doi.org/10.52842/conf.acadia.2021.572
source	ACADIA 2021: Realignments: Toward Critical Computation [Proceedings of the 41st Annual Conference of the Association of Computer Aided Design in Architecture (ACADIA) ISBN 979-8-986-08056-7]. Online and Global. 3-6 November 2021. edited by S. Parascho, J. Scott, and K. Dörfler. 572-575.
summary	This paper documents a data-driven approach to a conceptual rendering workflow with Artificial Intelligence (AI) models. This work originates from the workshop ‘Intro to AI for Architectural Design Explorations’ lectured by the authors Mayur Mistry and Daniel Escobar, during the event ‘Inclusive FUTURES 2021’ at the Digital Futures platform. The observations reflect about the applicability of machine-augmented conceptual design. As a common practice in the fi eld, architects start designing their buildings by sketching their ideas, this is a process that attempts to translate a concept into a spatial and aesthetic solution. Nevertheless, the design process is an iterative and time-consuming task. For this reason, we must experiment new methods that can potentially enhance architectural practice.
series	ACADIA
type	field note
email
last changed	2023/10/22 12:06

_id	sigradi2023_234
id	sigradi2023_234
authors	Santos, Ítalo, Andrade, Max, Zanchettin, Cleber and Rolim, Adriana
year	2023
title	Machine learning applied in the evaluation of airport projects in Brazil based on BIM models
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. 875–887
summary	In a country with continental dimensions like Brazil, air transport plays a strategic role in the development of the country. In recent years, initiatives have been promoted to boost the development of air transport, among which the BIM BR strategy stands out, instituted by decree n-9.983 (2019), decree n-10.306 (2020) and more recently, the publication of the airport design manual (SAC, 2021). In this context, this work presents partial results of a doctoral research based on the Design Science Research (DSR) method for the application of Machine Learning (ML) techniques in the Artificial Intelligence (AI) subarea, aiming to support SAC airport project analysts in the phase of project evaluation. Based on a set of training and test data corresponding to airport projects, two ML algorithms were trained. Preliminary results indicate that the use of ML algorithms enables a new scenario to be explored by teams of airport design analysts in Brazil.
keywords	Airports, Artificial intelligence, BIM, Evaluation, Machine learning.
series	SIGraDi
email
last changed	2024/03/08 14:07

_id	ecaade2021_237
id	ecaade2021_237
authors	Sönmez, Ayça and Gönenç Sorguç, Arzu
year	2021
title	Computer-Aided Fabrication Technologies as Computational Design Mediators
doi	https://doi.org/10.52842/conf.ecaade.2021.1.465
source	Stojakovic, V and Tepavcevic, B (eds.), Towards a new, configurable architecture - Proceedings of the 39th eCAADe Conference - Volume 1, University of Novi Sad, Novi Sad, Serbia, 8-10 September 2021, pp. 465-474
summary	The developments in recent technologies through Industry 4.0 lead to the integration of digital design and manufacturing processes. Albeit manufacturing continues to increase its importance as design input, it is generally considered at the last stages of the design process. This misconception results in a gap between digital design and fabrication, leading to differences between the initial design and the fabricated outcome in the context of architectural tectonics. Here, we present an artificial intelligence (AI)-based approach that aims to provide a basis to bridge the gap between computation and fabrication. We considered a case study of a 3D model in two stages. In the first stage, an intuitive and top-down design process is adopted, and in the second stage, an AI-based exploration is conducted with three cases derived from the same 3D model. The outcomes of the two stages provided a dataset including different design parameters to be used in a decision tree classifier algorithm which selects the manufacturing method for a given 3D model. Our results show that generative design simulations based on manufacturing constraints can provide a significant variety of manufacturable design alternatives, and minimizes the difference between design alternatives. Using our proposed approach, the time spent in form-finding and fabrication can be reduced significantly. Additionally, the implementation of decision tree classifier learning algorithm shows that AI can serve designers to make accurate predictions for manufacturing method.
keywords	Generative Design; Computer-Aided Fabrication; Arcihtecture 4.0; Artificial Intelligence; Digital Tectonics
series	eCAADe
email
last changed	2022/06/07 07:56

_id	cdrf2021_26
id	cdrf2021_26
authors	Yuqian Li and Weiguo Xu
year	2021
title	Using CycleGAN to Achieve the Sketch Recognition Process of Sketch-Based Modeling
doi	https://doi.org/https://doi.org/10.1007/978-981-16-5983-6_3
source	Proceedings of the 2021 DigitalFUTURES The 3rd International Conference on Computational Design and Robotic Fabrication (CDRF 2021)
summary	Architects usually design ideation and conception by hand-sketching. Sketching is a direct expression of the architect’s creativity. But 2D sketches are often vague, intentional and even ambiguous. In the research of sketch-based modeling, it is the most difficult part to make the computer to recognize the sketches. Because of the development of artificial intelligence, especially deep learning technology, Convolutional Neural Networks (CNNs) have shown obvious advantages in the field of extracting features and matching, and Generative Adversarial Neural Networks (GANs) have made great breakthroughs in the field of architectural generation which make the image-to-image translation become more and more popular. As the building images are gradually developed from the original sketches, in this research, we try to develop a system from the sketches to the images of buildings using CycleGAN algorithm. The experiment demonstrates that this method could achieve the mapping process from the sketches to images, and the results show that the sketches’ features could be recognised in the process.By the learning and training process of the sketches’ reconstruction, the features of the images are also mapped to the sketches, which strengthen the architectural relationship in the sketch, so that the original sketch can gradually approach the building images, and then it is possible to achieve the sketch-based modeling technology.
series	cdrf
email
last changed	2022/09/29 07:53

_id	sigradi2021_304
id	sigradi2021_304
authors	Andia, Alfredo
year	2021
title	Synthetic Biology Imaginations for the Biscayne Bay, Florida
source	Gomez, P and Braida, F (eds.), Designing Possibilities - Proceedings of the XXV International Conference of the Ibero-American Society of Digital Graphics (SIGraDi 2021), Online, 8 - 12 November 2021, pp. 1487–1497
summary	This project attempts to reimagine Miami and coastal communities with the advent of climate change and the rise of biotechnology. We develop a speculative vision/plan for the Biscayne Bay estuary that envisions infrastructures that grow by themselves using synthetic biology. In this paper, we elaborate on how Synthetic Biology has evolved to become the fastest growing technology in human history, its potential in the development of large-scale infrastructures, and its impact on the future imaginations of Architecture.
keywords	Automated Workflows, Synthetic Biology, Artificial Intelligence, Architecture, Sea-level Rise
series	SIGraDi
email
last changed	2022/05/23 12:11

_id	caadria2021_088
id	caadria2021_088
authors	Batalle Garcia, Anna, Cebeci, Irem Yagmur, Vargas Calvo, Roberto and Gordon, Matthew
year	2021
title	Material (data) Intelligence - Towards a Circular Building Environment
doi	https://doi.org/10.52842/conf.caadria.2021.1.361
source	A. Globa, J. van Ameijde, A. Fingrut, N. Kim, T.T.S. Lo (eds.), PROJECTIONS - Proceedings of the 26th CAADRIA Conference - Volume 1, The Chinese University of Hong Kong and Online, Hong Kong, 29 March - 1 April 2021, pp. 361-370
summary	The integration of repurposed material in new construction products generates resiliency strategies that diminish the dependency on raw resources and reduce the CO2 emissions produced by their extraction, transportation, and manufacturing. This research emphasizes the need to expand preliminary data collation from pre-demolition sites to inform early design decisions. Material (data) Intelligence investigates how the merging of artificial intelligence and data analysis could have a crucial impact on achieving widespread material reuse. The first step consists of automating the process of detecting materials and construction elements from pre-demolition sites through drone photography and computer vision. The second part of the research links the resulting database with a computational design tool that can be integrated into construction software. This paper strengthens the potential of circular material flows in a digital paradigm and exposes the capability for constructing big data sets of reusable materials, digitally available, for sharing and organizing material harvesting.
keywords	computer vision; material database; automation; reclaimed material; digitalization
series	CAADRIA
email
last changed	2022/06/07 07:54

_id	ascaad2021_074
id	ascaad2021_074
authors	Belkaid, Alia; Abdelkader Ben Saci, Ines Hassoumi
year	2021
title	Human-Computer Interaction for Urban Rules Optimization
source	Abdelmohsen, S, El-Khouly, T, Mallasi, Z and Bennadji, A (eds.), Architecture in the Age of Disruptive Technologies: Transformations and Challenges [9th ASCAAD Conference Proceedings ISBN 978-1-907349-20-1] Cairo (Egypt) [Virtual Conference] 2-4 March 2021, pp. 603-613
summary	Faced with the complexity of manual and intuitive management of urban rules in architectural and urban design, this paper offers a collaborative and digital human-computer approach. It aims to have an Authorized Bounding Volume (ABV) which uses the best target values of urban rules. It is a distributed constraint optimization problem. The ABV Generative Model uses multi-agent systems. It offers an intelligent system of urban morphology able to transform the urban rules, on a given plot, into a morphological delimitation permitted by the planning regulations of a city. The overall functioning of this system is based on two approaches: construction and supervision. The first is conducted entirely by the machine and the second requires the intervention of the designer to collaborate with the machine. The morphological translation of urban rules is sometimes contradictory and may require additional external relevance to urban rules. Designer arbitration assists the artificial intelligence in accomplishing this task and solving the problem. The Human-Computer collaboration is achieved at the appropriate time and relies on the degree of constraint satisfaction with fitness function. The resolution of the distributed constraint optimization problem is not limited to an automatic generation of urban rules, but involves also the production of multiple optimal-ABV conditioned both by urban constraints as well as relevance, chosen by the designer.
series	ASCAAD
email
last changed	2021/08/09 13:13

_id	ijac202119106
id	ijac202119106
authors	Del Campo, Matias; Alexandra Carlson, and Sandra Manninger
year	2021
title	Towards Hallucinating Machines - Designing with Computational Vision
source	International Journal of Architectural Computing 2021, Vol. 19 - no. 1, 88–103
summary	There are particular similarities in how machines learn about the nature of their environment, and how humans learn to process visual stimuli. Machine Learning (ML), more specifically Deep Neural network algorithms rely on expansive image databases and various training methods (supervised, unsupervised) to “make sense” out of the content of an image. Take for example how students of architecture learn to differentiate various architectural styles. Whether this be to differentiate between Gothic, Baroque or Modern Architecture, students are exposed to hundreds, or even thousands of images of the respective styles, while being trained by faculty to be able to differentiate between those styles. A reversal of the process, striving to produce imagery, instead of reading it and understanding its content, allows machine vision techniques to be utilized as a design methodology that profoundly interrogates aspects of agency and authorship in the presence of Artificial Intelligence in architecture design. This notion forms part of a larger conversation on the nature of human ingenuity operating within a posthuman design ecology. The inherent ability of Neural Networks to process large databases opens up the opportunity to sift through the enormous repositories of imagery generated by the architecture discipline through the ages in order to find novel and bespoke solutions to architectural problems. This article strives to demystify the romantic idea of individual artistic design choices in architecture by providing a glimpse under the hood of the inner workings of Neural Network processes, and thus the extent of their ability to inform architectural design.The approach takes cues from the language and methods employed by experts in Deep Learning such as Hallucinations, Dreaming, Style Transfer and Vision. The presented approach is the base for an in-depth exploration of its meaning as a cultural technique within the discipline. Culture in the extent of this article pertains to ideas such as the differentiation between symbolic and material cultures, in which symbols are defined as the common denominator of a specific group of people.1 The understanding and exchange of symbolic values is inherently connected to language and code, which ultimately form the ingrained texture of any form of coded environment, including the coded structure of Neural Networks.A first proof of concept project was devised by the authors in the form of the Robot Garden. What makes the Robot Garden a distinctively novel project is the motion from a purely two dimensional approach to designing with the aid of Neural Networks, to the exploration of 2D to 3D Neural Style Transfer methods in the design process.
keywords	Artificial intelligence, design agency, neural networks, machine learning, machine vision
series	journal
email
last changed	2021/06/03 23:29

_id	caadria2023_446
id	caadria2023_446
authors	Guida, George
year	2023
title	Multimodal Architecture: Applications of Language in a Machine Learning Aided Design Process
doi	https://doi.org/10.52842/conf.caadria.2023.2.561
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
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	ecaade2021_158
id	ecaade2021_158
authors	Joyce, Sam Conrad and Nazim, Ibrahim
year	2021
title	Limits to Applied ML in Planning and Architecture - Understanding and defining extents and capabilities
doi	https://doi.org/10.52842/conf.ecaade.2021.1.243
source	Stojakovic, V and Tepavcevic, B (eds.), Towards a new, configurable architecture - Proceedings of the 39th eCAADe Conference - Volume 1, University of Novi Sad, Novi Sad, Serbia, 8-10 September 2021, pp. 243-252
summary	There has been an exponential increase in Machine Learning (ML) research in design. Specifically, with Deep Learning becoming more accessible, frameworks like Generative Adversarial Networks (GANs), which are able to synthesise novel images are being used in the classification and generation of designs in architecture. While much of these explorations successfully demonstrate the 'magic' and potential of these techniques, their limits remain unclear, with only a few, but crucial, discussions on underlying fundamental limits and sensitivities of ML. This is a gap in our understanding of these tools especially within the complex context of planning and architecture. This paper seeks to discuss what limits ML in design as it exists today, by examining the state-of-the-art and mechanics of ML models relevant to design tasks. Aiming to help researchers to focus on productive uses of ML and avoid areas of over-promise.
keywords	Machine Learning; Artificial Intelligence; Creativity
series	eCAADe
email
last changed	2022/06/07 07:52

_id	sigradi2021_200
id	sigradi2021_200
authors	Karabagli, Kaan, Koc, Mustafa, Basu, Prithwish and As, Imdat
year	2021
title	A Machine Learning Approach to Translate Graph Representations into Conceptual Massing Models
source	Gomez, P and Braida, F (eds.), Designing Possibilities - Proceedings of the XXV International Conference of the Ibero-American Society of Digital Graphics (SIGraDi 2021), Online, 8 - 12 November 2021, pp. 191–202
summary	Machine learning (ML) has popular applications in domains involving image, video, text and voice. However, in architecture, image-based ML systems face challenges capturing the complexity of three-dimensional space. In this paper, we leverage a graph-based ML system that can capture the inherent topology of architectural conceptual designs and identify high-performing latent patterns within such designs. In particular, our goal is to translate architectural graph data into three-dimensional massing models. We are building on our prior ML work, where we, a. discovered latent topological features, b. composed building blocks into new designs, c. evaluated their feasibility, and d. explored Generative Adversarial (Neural) Networks (GAN)-generated design variations. We trained the ML system with architectural design data that we gathered from an online architectural design competition platform, translated them into machine-readable graph representations, and identified their essential subgraphs to develop novel compositions. In this paper, we explore how these novel designs (outputted in graph form), can be translated into three-dimensional architectural form. We present an ML approach to turn graph representations into functional volumetric massing models. The ultimate goal of the study is to develop an end-to-end pipeline to generate architectural design - from a graph representation to a fully developed conceptual proxy of a designed product. The research question is promising in automating conceptual design, and we believe the outcome can be relevant to other design disciplines as well.
keywords	Architectural design, machine learning, conceptual design, deep learning, artificial intelligence
series	SIGraDi
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last changed	2022/05/23 12:10

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