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	acadia20_120p
id	acadia20_120p
authors	Hirth, Kevin
year	2020
title	Short Stack
source	ACADIA 2020: Distributed Proximities / Volume II: Projects [Proceedings of the 40th Annual Conference of the Association of Computer Aided Design in Architecture (ACADIA) ISBN 978-0-578-95253-6]. Online and Global. 24-30 October 2020. edited by M. Yablonina, A. Marcus, S. Doyle, M. del Campo, V. Ago, B. Slocum. 120-123
summary	Short Stack is a bare minimal structure using only laminated sheets of structural metal decking for all elements of its structure and enclosure. The project operates under a simple principle. Structural metal decking is a one-way system that resists loads well in one direction, but not in the other. When this decking is stacked into rotated sections and tensioned together, the resultant sandwich of corrugated metal is resistant to loading in every direction. These sandwiches become walls, floors, and roofs to a temporary structure. The compounded effect at the edges of the rotated and cropped decking is one of filigree or an ornamental articulation. The sandwich, which is mostly hollow due to the section of the decking, provides a sense of airy lightness that is at odds with its bulky mass. The structure, therefore, teeters between being unexpectedly open and at once heavy. The economy of the project is in its uniformity and persistent singularity. By maintaining a single palette of material and using a plasma cutting CNC bed to cut each section of the decking, the structure is simply assembled. The digital intelligence that lies underneath the apparent formal simplicity of the project is two-fold. Firstly, each sheet of metal decking is different from the next. Because of the locations of bolt-holes and constant variability of rotation and cropping of each sheet, it is a project that expresses uniformity rather than articulation through discretization. Secondly, the project appears solid and monolithic but is hollowed structurally to minimize the weight of the assembly. Parametric tools are implemented to maximize material efficiencies by hollowing the interior of each sandwich for load optimization. The project is presently in prototyping and documentation and will go into construction in Spring 2021 on a site in downtown Denver.
series	ACADIA
type	project
email
last changed	2021/10/26 08:03

_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
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
doi	https://doi.org/10.52842/conf.caadria.2021.1.211
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	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	acadia21_122
id	acadia21_122
authors	Velikov, Kathy; Hasan, Kazi Najeeb; del Campo, Matias; Xie, Ruxin; Denit, Lucas; Boyce, Brent
year	2021
title	Design Engine
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 B. Bogosian, K. Dörfler, B. Farahi, J. Garcia del Castillo y López, J. Grant, V. Noel, S. Parascho, and J. Scott. 122-133.
doi	https://doi.org/10.52842/conf.acadia.2021.122
summary	Generative design offers the possibility to heuristically explore data-driven design iterations during the design process. This enables performance-informed feedback and the possibility for exploring viable options with stakeholders earlier in the design process. Since architectural design is a complex, nonlinear process that requires trade-offs and compromises among multiple requirements, many of which are in conflict with each other, a multi-objective solver provides a spectrum of possible solutions without converging on a single optimized individual. This enables a more informed design possibility space that is open to collaborative decision-making. This paper describes the development of a custom multi-objective generative design workflow to visualize families of possible future building typologies with a focus on the impact of site, form, envelope performance, and glazing. Three future design scenarios are generated for three urban U.S. locations projected to grow and where progressive environmental performance stretch codes have been adopted. Drivers such as plausible site, procurement, financing, value chain, and construction typology inform possibilities for built form, envelope technologies, and performance in relation to local codes, environment, and occupant health, are transformed into design inputs through urban, spatial and environmental simulation tools for a "building design generator," or a multi-objective optimizer tool that produces an array of possible building massing and schematic envelope design options. The paper concludes with pointing out some of the gaps in data of current evaluation tools, the need for interoperability across platforms, and this points to multiple trajectories of future research in this area.
series	ACADIA
type	paper
email
last changed	2023/10/22 12:06

_id	acadia21_410
id	acadia21_410
authors	Meibodi, Mania Aghaei; Craney, Ryan; McGee, Wes
year	2021
title	Robotic Pellet Extrusion: 3D Printing and Integral Computational Design
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 B. Bogosian, K. Dörfler, B. Farahi, J. Garcia del Castillo y López, J. Grant, V. Noel, S. Parascho, and J. Scott. 410-419.
doi	https://doi.org/10.52842/conf.acadia.2021.410
summary	3D printing offers significant geometric freedom and allows the fabrication of integral parts. This research showcases how robotic fused deposition modeling (FDM) enables the prefabrication of large-scale, lightweight, and ready-to-cast freeform formwork to minimize material waste, labor, and errors in the construction process while increasing the speed of production and economic viability of casting non-standard concrete elements. This is achieved through the development of a digital design-to-production workflow for concrete formwork. All functions that are needed in the final product, an integrally insulated steel-reinforced concrete wall, and the process for a successful cast, are fully integrated into the formwork system. A parametric model for integrated structural ribbing is developed and verified using finite element analysis. A case study is presented which showcases the fully integrated system in the production of a 2.4 m tall x 2.0 m curved concrete wall. This research demonstrates the potential for large-scale additive manufacturing to enable the efficient production of non-standard concrete formwork.
series	ACADIA
type	paper
email
last changed	2023/10/22 12:06

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