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	sigradi2022_270
id	sigradi2022_270
authors	Arenas, Felipe; Banda, Pablo
year	2022
title	Ludo faber alumni: playful experiences of digital manufacturing for the appropriation of educational spaces
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. 503–514
summary	The present study is inserted in the learning context of the Architecture career of 40 students in a course of Applied Digital Fabrication. It seeks to explore the design possibilities that are produced by permeating game design features with digital architectural design and digital fabrication with each other. What spatial design potentials appear when introducing and intermingling the notions of Homo Ludens and Homo Faber in architectural generative design systems?
keywords	Digital fabrication, Gamification, Generative design, rule-based design
series	SIGraDi
email
last changed	2023/05/16 16:56

_id	ijac202220101
id	ijac202220101
authors	Bao, Ding Wen; Xin Yan, Yi Min Xie
year	2022
title	Encoding topological optimisation logical structure rules into multi-agent system for architectural design and robotic fabrication
source	International Journal of Architectural Computing 2022, Vol. 20 - no. 1, pp. 7–17
summary	Natural phenomena have been explored as a source of architectural and structural design inspiration with different approaches undertaken within architecture and engineering. The research proposes a connection between two dichotomous principles: architectural complexity and structural efficiency through a hybrid of natural phenomena, topology optimisation and generative design. Both Bi-directional Evolutionary Structural Optimisation (BESO) and multi-agent algorithms are emerging technologies developed into new approaches that transform architectural and structural design, respectively, from the logic of topology optimisation and swarm intelligence. This research aims to explore a structural behaviour feedback loop in designing intricate functional forms through encoding BESO logical structure rules into the multi-agent algorithm. This research intends to study and evaluate the application of topology optimisation and multi-agent system in form-finding and later robotic fabrication through a series of prototypes. It reveals a supposition that the structural behaviour-based design method matches the beauty and function of natural appearance and structure. Thus, a new exploration of architectural design and fabrication strategy is introduced, which benefits the collab- oration among architects, engineers and manufacturers. There is the potential to seek the ornamental complexities in architectural forms and the most efficient use of material based on structural performance in the process of generating complex geometry of the building and its various elements.
keywords	Swarm intelligence, multi-agent, bi-directional evolutionary structural optimisation (BESO), intricate architectural form, efficient structure
series	journal
last changed	2024/04/17 14:29

_id	acadia22_224
id	acadia22_224
authors	Coersmeier, Jonas; Nanasca, James; Man Hin, Ivan Yan; Blasetti, Ezio
year	2022
title	Nanotectonica SEM-GAN
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. 224-243.
summary	The present study, Nanotectonica SEM-GAN, focuses on two processes for image production, one based in the field of nanotechnology and the other in machine learning: Scanning Electron Microscopy (SEM) and Generative Adversarial Networks (GAN). It establishes commonalities of these routines as they pertain to aesthetics and design methodology, and it explores methods of spatializing and materializing images produced in their interaction. The study of transposing rich image material to three-dimensional geometry and material artifact is considered relevant not only to the particular study at hand, but also to the general problem of image-based machine learning techniques when applied in the spatial design disciplines. A third process, Robotic Incremental Metal Forming (RIMF), advances the aesthetic language of SEM-GAN through the sculptural method of the relief.
series	ACADIA
type	paper
email
last changed	2024/02/06 14:00

_id	ecaade2022_175
id	ecaade2022_175
authors	Di Carlo, Raffaele, Mittal, Divyae and Vesely, Ondrej
year	2022
title	Generating 3D Building Volumes for a Given Urban Context using Pix2Pix GAN
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. 287–295
doi	https://doi.org/10.52842/conf.ecaade.2022.2.287
summary	Our ability to delegate the most intellectually demanding tasks to machines improves with each passing day. Even in the fields of architecture and design, which were previously thought to be exclusive domain of human creativity and flare, we are moving the first steps towards developing models that can capture the patterns, invisible to the naked eye, embedded in the creative process. These patterns reflect ideas and traditions, imprinted in the collective mind over the course of history, that can be improved upon or serve as a cautionary tale for the new generation of designers in their work of designing an equitable, more inclusive future. Generative Adversarial Networks (GANs) give us the opportunity to turn style and design into learnable features that can be used to automatically generate blueprints and layouts. In this study, we attempt to apply this technology to urban design and to the task of generating a building footprint and volume that fits within the surrounding built environment. We do so by developing a Pix2Pix model composed of a ResNet-6 generator and a Patch discriminator, applying it to satellite views of neighborhoods from across the Netherlands, and then turning the resulting 2D generated building footprint into a reusable 3D model. The model is trained using the national cadastral data and TU Delft 3D BAG dataset. The results show that it is possible to predict a building shape compatible in style and height with the surroundings. Although the model can be used for different applications, we use it as an evaluation tool to compare the design alternatives fitting the desired contextual patterns.
keywords	Generative Adversarial Networks, Urban Design, Pix2Pix, Raster Vectorization, 3D Rendering
series	eCAADe
email
last changed	2024/04/22 07:10

_id	sigradi2022_156
id	sigradi2022_156
authors	Dornelas, Wallace; Martinez, Andressa
year	2022
title	Towards a Parametric Variation of Floor Plans: a Preliminary Approach for Vertical Residential Buildings
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. 151–162
summary	In the context of the housing demands that respond to several family profiles, allied with the potential of the algorithmic approaches to Architecture, this paper aims to describe an exploratory process of possible solutions toward a generative system of housing distribution in vertical multifamily buildings. As a method, this work presents a parametric design process of a multifamily building, simulating a variety of shape solutions for apartment buildings, in a Grasshopper definition. The work also discusses the data transmission between the parametric modeling using Grasshopper in the Rhinoceros interface and the connection of the final design to Graphisoft’s Archicad BIM-based software. As a result, the parametric model allows several design solutions for several building shapes and contexts. For this study, to fully explore the design possibilities, we applied the method in the context of a Brazilian metropolitan city.
keywords	Generative design, Visual algorithmic design, Parametric architecture, Housing
series	SIGraDi
email
last changed	2023/05/16 16:55

_id	ecaade2022_367
id	ecaade2022_367
authors	Doumpioti, Christina and Huang, Jeffrey
year	2022
title	Field Condition - Environmental sensibility of spatial configurations with the use of machine intelligence
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. 67–74
doi	https://doi.org/10.52842/conf.ecaade.2022.2.067
summary	Within computational environmental design (CED), different Machine Learning (ML) models are gaining ground. They aim for time efficiency by automating simulation and speeding up environmental performance feedback. This study suggests an approach that enhances not the optimization but the generative aspect of environmentally driven ML processes in architectural design. We follow Stan Allen's (2009) idea of 'field conditions' as a bottom-up phenomenon according to which form and space emerge from local invisible and dynamic connections. By employing parametric modeling, environmental analysis data, and conditional Generative Adversarial Networks [cGAN] we introduce a generative approach in design that reverses the typical design process of going from formal interpretation to analysis and encourages the emergence of spatial configurations with embedded environmental intelligence. We call it Intensive-driven Environmental Design Computation [IEDC], and we employ it in a case study on a residential building typology encountered in the Mediterranean. The paper describes the process, emphasizing dataset preparation as the stage where the logic of field conditions is established. The proposed research differentiates from cGAN models that offer automatic environmental performance predictions to one that spatial predictions stem from dynamic fields.
keywords	Field Architecture, Environmental Design, Generative Design, Machine Learning, Residential Typologies
series	eCAADe
email
last changed	2024/04/22 07:10

_id	ecaade2022_44
id	ecaade2022_44
authors	Güzelci, Orkan Zeynel
year	2022
title	Machine Learning in Predicting Section Drawings - Case of Anatolian Seljuk Kümbets
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. 169–176
doi	https://doi.org/10.52842/conf.ecaade.2022.2.169
summary	Funerary structures called kümbet emerged as a unique typology during the Anatolian Seljuk period (1077-1307). This study introduces a machine learning (ML) based model to predict sections of kümbets to complete their missing parts. The proposed ML-based model employs the Pix2Pix method, which is a subset of conditional Generative Adversarial Networks (cGAN).The model is trained over a coupled dataset (interior space and exterior shell) of section drawings. Then, the model is validated by predicting overall shape (exterior shell) for a given input (interior space). The outcomes of the validation phase are evaluated objectively by using structural similarity method (SSIM). Initial findings of the implementation show that the proposed ML-based model has the potential to be used as a design decision support tool for further restitution and renovation works.
keywords	Anatolian Seljuk Architecture, Kümbet, Pix2Pix, Machine Learning, Section
series	eCAADe
email
last changed	2024/04/22 07:10

_id	acadia22_366
id	acadia22_366
authors	Hauptman, Jonas; Haghnazar, Ramtin; Moghaddam, Sara Saghafi
year	2022
title	Developing a Digital Design Workflow for Nexorade Bamboo Structure
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. 366-377.
summary	This paper presents a case study integrating generative design and bamboo culm geometries. Our goal is to improve the application of biological materials in a responsive Computer-Aided Design (CAD) process. While employing eccentric biological materials such as bamboo imposes an added layer of complexity on the design-to-fabrication process, it may also offer more sustainable material application and expand the frontiers of design and fabrication research methods. The methods explored in this paper are deployed to realize freeform Nexorade structures (FNS) that are explicitly tailored to individual bamboo culms (BC); each of these has been measured to explore the potential that material eccentricity may be a district benefit rather than a detriment to the quality and efficiency of a design.
series	ACADIA
type	paper
email
last changed	2024/02/06 14:00

_id	acadia22_638
id	acadia22_638
authors	Hosmer, Tyson; Wang, Jiaqi; Jiang, Wanzhu; He, Ziming
year	2022
title	Integrated Reconfigurable Autonomous Architecture System
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. 638-651.
summary	Integrated Reconfigurable Autonomous Architecture System (IRAAS) is composed of three components: 1) an interactive platform for user and environmental data input, 2) an agent-based generative space planning algorithm with deep reinforcement learning for continuous spatial adaptation, 3) a distributed robotic material system with bidirectional cyber-physical control protocols for simultaneous state alignment.
series	ACADIA
type	paper
email
last changed	2024/02/06 14:04

_id	ecaade2022_399
id	ecaade2022_399
authors	Johanes, Mikhael and Huang, Jeffrey
year	2022
title	Deep Learning Spatial Signature - Inverted GANs for Isovist representation in architectural floorplan
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. 621–629
doi	https://doi.org/10.52842/conf.ecaade.2022.2.621
summary	The advances of Generative Adversarial Networks (GANs) have provided a new experimental ground for creative architecture processes. However, the analytical potential of the latent representation of GANs is yet to be explored for architectural spatial analysis. Furthermore, most research on GANs for floorplan learning in architecture uses images as its main representation medium. This paper presents an experimental framework that uses one-dimensional periodic isovist samples and GANs inversion to recover its latent representation. Access to GANs’ latent space will open up a possibility for discriminative tasks such as classification and clustering analysis. The resulting latent representation will be investigated to discover its analytical capacity in extracting isovist spatial patterns from thousands of floorplans data. In this experiment, we hypothetically conclude that the spatial signature of the architectural floor plan could be derived from the degree of regularity of isovist samples in the latent space structure. The finding of this research will enable a new data-driven strategy to measure spatial quality using isovist and provide a new way for indexing architectural floorplan.
keywords	Machine Learning, Isovist, Latent Representation, GANs Inversion, Spatial Signature
series	eCAADe
email
last changed	2024/04/22 07:10

_id	acadia22_672
id	acadia22_672
authors	Johanes, Mikhael; Huang, Jeffrey
year	2022
title	Latent Isovist
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. 672-683.
summary	This research leverages the development in deep learning research to develop an experimental framework for discovering machine-human interpretable spatial properties from latent isovist, a reduced dimensionality isovist representation obtained from generative adversarial networks (GANs). GAN latent space contains a wide range of semantically interpretable directions, potentially being used to quantify the spatial properties encoded in isovist representation.
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
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
doi	https://doi.org/10.52842/conf.ecaade.2022.1.341
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	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	acadia22_90
id	acadia22_90
authors	Li, Chenxiao; Yuan, Mingyang; Han, Zilong; Faircloth, Billie; Anderson, Jeffrey S.; King, Nathan; Stuart-Smith, Robert
year	2022
title	Smart Branching
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. 90-97.
summary	Through the design and fabrication of a 1.3m-high physical prototype sampled from our facade proposal, we developed a relatively automated project pipeline. It aims to achieve the generative and evolutionary design and a non-planar clay deposition method for tubular branching components.
series	ACADIA
type	paper
email
last changed	2024/02/06 14:00

_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
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
doi	https://doi.org/10.52842/conf.caadria.2022.1.343
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	ecaade2022_217
id	ecaade2022_217
authors	Panagiotidou, Vasiliki and Koerner, Andreas
year	2022
title	From Intricate to Coarse and Back - A voxel-based workflow to approximate high-res geometries for digital environmental simulations
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. 491–500
doi	https://doi.org/10.52842/conf.ecaade.2022.1.491
summary	Digital environmental simulations can present a computational bottleneck concerning the complexity of geometry. Therefore, a series of workarounds, ranging from cloud-based solutions to machine learning simulations as surrogate simulations are conventionally applied in practice. Concurrently, contemporary advances in procedural modelling in architecture result in design concepts with high polygon counts. This leads to an ever- increasing resolution discrepancy between design and analysis models. Responding to this problem, this research presents a step-by-step approximation workflow for handling and transferring high-resolution geometries between procedural modelling and environmental simulation software. The workflow is intended to allow designers to quickly assess a design’s interaction with environmental parameters such as airflow and solar radiation and further articulate them. A controllable voxelization procedure is applied to approximate the original geometry and therefore reduce the resolution. Controllable in this context refers to the user’s ability to locally adjust the voxel resolution to fit design needs. After export and simulation, 3d results are imported back into the design environment. The colour properties are re-mapped onto the original high- resolution geometry following a weighted proximity technique. The developed data transfer pipeline allows designers to integrate environmental analysis during initial design steps, which is essential for accessibility in the design profession. This can help to environmentally inform generative designs as well as to make simulation workflows more accessible when working with a wider range of geometries. In this, it reduces the perceived discrepancy between the concept and simulation model. This eases the use and allows a wider audience of users to develop co-creation processes between computation, architecture, and environment.
keywords	Simulation, Accessibility, Computation, Environmental Data, Workflow
series	eCAADe
email
last changed	2024/04/22 07:10

_id	sigradi2022_38
id	sigradi2022_38
authors	Porley, Natalia
year	2022
title	Experimenting with generative algorithmic design using bamboo
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. 1137–1148
summary	This article arises as an analysis of the practical group work carried out in the course within the framework of our Master's Degree in Architecture, and seeks to comprehend and expand on the logic and dynamics that underlie the digital paradigm and the incorporation of algorithmic practices as possible design generators (Centro de Integración Digital (CID), 2022). By quantitatively and experimentally delving into the knowledge acquired in the course, we integrate the experience of parametric design from a scientific point of view through a process of searching for and solving problems. Our team explored the possibilities that generative algorithmic design offers us when working with a natural material namely bamboo, as a contribution to the mix between digital and bio-sustainable materials. Exploring the approach that digitalization offers us as designers and the role that we are called to play as architects of this time.
keywords	Sustainable Design, Digital architecture with bamboo, Sustainable construction, Experimentation with digital design
series	SIGraDi
email
last changed	2023/05/16 16:57

_id	sigradi2022_164
id	sigradi2022_164
authors	Rodriguez Cortez, Fernando Hernan; Gatica Laurie, Braulio Alfonso; Garcia-Alvarado, Rodrigo
year	2022
title	Hydrological recovery of the landscape through generative design.
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. 1113–1122
summary	The climate crisis confronts us with global scenarios of water deficiency, which leads us to optimize rainwater harvesting (RWH) methods applied to landscapes. These are sophisticated projects with high technical investment per hectare designed and executed. Therefore, to balance the cost of engineering, through advanced digital design tools, it has become an objective to use landscape architecture as a resource for mitigating water deficit and climate change. In order to restore the relationship between the natural and built environment through virtual processing. This work exposes a new geometric analysis methodology, verified in a case study, which applies generative parametric programming to increase the water capacity of a natural landscape. This work demonstrates the potential of digital design for the ecological recovery of the territory.
keywords	Smart Cities and Environments, Sustainable Design, Generative Design, Mixed realities, hydrological design
series	SIGraDi
email
last changed	2023/05/16 16:57

_id	ascaad2022_099
id	ascaad2022_099
authors	Sencan, Inanc
year	2022
title	Progeny: A Grasshopper Plug-in that Augments Cellular Automata Algorithms for 3D Form Explorations
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. 377-391
summary	Cellular automata (CA) is a well-known computation method introduced by John von Neumann and Stanislaw Ulam in the 1940s. Since then, it has been studied in various fields such as computer science, biology, physics, chemistry, and art. The Classic CA algorithm is a calculation of a grid of cells' binary states based on neighboring cells and a set of rules. With the variation of these parameters, the CA algorithm has evolved into alternative versions such as 3D CA, Multiple neighborhood CA, Multiple rules CA, and Stochastic CA (Url-1). As a rule-based generative algorithm, CA has been used as a bottom-up design approach in the architectural design process in the search for form (Frazer,1995; Dinçer et al., 2014), in simulating the displacement of individuals in space, and in revealing complex relations at the urban scale (Güzelci, 2013). There are implementations of CA tools in 3D design software for designers as additional scripts or plug-ins. However, these often have limited ability to create customized CA algorithms by the designer. This study aims to create a customizable framework for 3D CA algorithms to be used in 3D form explorations by designers. Grasshopper3D, which is a visual scripting environment in Rhinoceros 3D, is used to implement the framework. The main difference between this work and the current Grasshopper3D plug-ins for CA simulation is the customizability and the real-time control of the framework. The parameters that allow the CA algorithm to be customized are; the initial state of the 3D grid, neighborhood conditions, cell states and rules. CA algorithms are created for each customizable parameter using the framework. Those algorithms are evaluated based on the ability to generate form. A voxel-based approach is used to generate geometry from the points created by the 3D cellular automata. In future, forms generated using this framework can be used as a form generating tool for digital environments.
series	ASCAAD
email
last changed	2024/02/16 13:38

_id	artificial_intellicence2019_117
id	artificial_intellicence2019_117
authors	Stanislas Chaillou
year	2020
title	ArchiGAN: Artificial Intelligence x Architecture
source	Architectural Intelligence Selected Papers from the 1st International Conference on Computational Design and Robotic Fabrication (CDRF 2022)
doi	https://doi.org/https://doi.org/10.1007/978-981-15-6568-7_8
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

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