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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Reformat results as: short short into frame detailed detailed into frame

_id	acadia19_392
id	acadia19_392
authors	Steinfeld, Kyle
year	2019
title	GAN Loci
doi	https://doi.org/10.52842/conf.acadia.2019.392
source	ACADIA 19:UBIQUITY AND AUTONOMY [Proceedings of the 39th Annual Conference of the Association for Computer Aided Design in Architecture (ACADIA) ISBN 978-0-578-59179-7] (The University of Texas at Austin School of Architecture, Austin, Texas 21-26 October, 2019) pp. 392-403
summary	This project applies techniques in machine learning, specifically generative adversarial networks (or GANs), to produce synthetic images intended to capture the predominant visual properties of urban places. We propose that imaging cities in this manner represents the first computational approach to documenting the Genius Loci of a city (Norberg-Schulz, 1980), which is understood to include those forms, textures, colors, and qualities of light that exemplify a particular urban location and that set it apart from similar places. Presented here are methods for the collection of urban image data, for the necessary processing and formatting of this data, and for the training of two known computational statistical models (StyleGAN (Karras et al., 2018) and Pix2Pix (Isola et al., 2016)) that identify visual patterns distinct to a given site and that reproduce these patterns to generate new images. These methods have been applied to image nine distinct urban contexts across six cities in the US and Europe, the results of which are presented here. While the product of this work is not a tool for the design of cities or building forms, but rather a method for the synthetic imaging of existing places, we nevertheless seek to situate the work in terms of computer-assisted design (CAD). In this regard, the project is demonstrative of a new approach to CAD tools. In contrast with existing tools that seek to capture the explicit intention of their user (Aish, Glynn, Sheil 2017), in applying computational statistical methods to the production of images that speak to the implicit qualities that constitute a place, this project demonstrates the unique advantages offered by such methods in capturing and expressing the tacit.
series	ACADIA
type	normal paper
email
last changed	2022/06/07 07:56

_id	acadia19_370
id	acadia19_370
authors	Mohammad, Ali; Beorkrem, Christopher; Ellinger, Jefferson
year	2019
title	Hybrid Elevations using GAN Networks
doi	https://doi.org/10.52842/conf.acadia.2019.370
source	ACADIA 19:UBIQUITY AND AUTONOMY [Proceedings of the 39th Annual Conference of the Association for Computer Aided Design in Architecture (ACADIA) ISBN 978-0-578-59179-7] (The University of Texas at Austin School of Architecture, Austin, Texas 21-26 October, 2019) pp. 370-379
summary	This project is an attempt to develop and test a method for generating one-sided hybrid exterior building elevations using designer’s base criteria and design rule sets as inputs in an advanced artificial intelligence network. Architects are using computational design to expedite the iteration process in an efficient manner. Optimization techniques utilizing genetic solvers allow designers to explore broad sets of iterations within a predefined subset. However, with the application of artificial intelligence networks these fields of exploration can be expanded upon to develop ranges of exploration which can explore iterations outside of typical ranges. This paper explores the use of Generative Adversarial Networks (GAN) to explore and demonstrate their possible capabilities to typical design problems. In this instance we are exploring their application in the development of architectural elevations.
series	ACADIA
type	normal paper
email
last changed	2022/06/07 07:58

_id	ecaadesigradi2019_605
id	ecaadesigradi2019_605
authors	Andrade Zandavali, Bárbara and Jiménez García, Manuel
year	2019
title	Automated Brick Pattern Generator for Robotic Assembly using Machine Learning and Images
doi	https://doi.org/10.52842/conf.ecaade.2019.3.217
source	Sousa, JP, Xavier, JP and Castro Henriques, G (eds.), Architecture in the Age of the 4th Industrial Revolution - Proceedings of the 37th eCAADe and 23rd SIGraDi Conference - Volume 3, University of Porto, Porto, Portugal, 11-13 September 2019, pp. 217-226
summary	Brickwork is the oldest construction method still in use. Digital technologies, in turn, enabled new methods of representation and automation for bricklaying. While automation explored different approaches, representation was limited to declarative methods, as parametric filling algorithms. Alternatively, this work proposes a framework for automated brickwork using a machine learning model based on image-to-image translation (Conditional Generative Adversarial Networks). The framework consists of creating a dataset, training a model for each bond, and converting the output images into vectorial data for robotic assembly. Criteria such as: reaching wall boundary accuracy, avoidance of unsupported bricks, and brick's position accuracy were individually evaluated for each bond. The results demonstrate that the proposed framework fulfils boundary filling and respects overall bonding structural rules. Size accuracy demonstrated inferior performance for the scale tested. The association of this method with 'self-calibrating' robots could overcome this problem and be easily implemented for on-site.
series	eCAADeSIGraDi
email
last changed	2022/06/07 07:54

_id	caadria2021_053
id	caadria2021_053
authors	Rhee, Jinmo and Veloso, Pedro
year	2021
title	Generative Design of Urban Fabrics Using Deep Learning
doi	https://doi.org/10.52842/conf.caadria.2021.1.031
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. 31-40
summary	This paper describes the Urban Structure Synthesizer (USS), a research prototype based on deep learning that generates diagrams of morphologically consistent urban fabrics from context-rich urban datasets. This work is part of a larger research on computational analysis of the relationship between urban context and morphology. USS relies on a data collection method that extracts GIS data and converts it to diagrams with context information (Rhee et al., 2019). The resulting dataset with context-rich diagrams is used to train a Wasserstein GAN (WGAN) model, which learns how to synthesize novel urban fabric diagrams with the morphological and contextual qualities present in the dataset. The model is also trained with a random vector in the input, which is later used to enable parametric control and variation for the urban fabric diagram. Finally, the resulting diagrams are translated to 3D geometric entities using computer vision techniques and geometric modeling. The diagrams generated by USS suggest that a learning-based method can be an alternative to methods that rely on experts to build rule sets or parametric models to grasp the morphological qualities of the urban fabric.
keywords	Deep Learning; Urban Fabric; Generative Design; Artificial Intelligence; Urban Morphology
series	CAADRIA
email
last changed	2022/06/07 07:56

_id	ecaadesigradi2019_514
id	ecaadesigradi2019_514
authors	de Miguel, Jaime, Villafa?e, Maria Eugenia, Piškorec, Luka and Sancho-Caparrini, Fernando
year	2019
title	Deep Form Finding - Using Variational Autoencoders for deep form finding of structural typologies
doi	https://doi.org/10.52842/conf.ecaade.2019.1.071
source	Sousa, JP, Xavier, JP and Castro Henriques, G (eds.), Architecture in the Age of the 4th Industrial Revolution - Proceedings of the 37th eCAADe and 23rd SIGraDi Conference - Volume 1, University of Porto, Porto, Portugal, 11-13 September 2019, pp. 71-80
summary	In this paper, we are aiming to present a methodology for generation, manipulation and form finding of structural typologies using variational autoencoders, a machine learning model based on neural networks. We are giving a detailed description of the neural network architecture used as well as the data representation based on the concept of a 3D-canvas with voxelized wireframes. In this 3D-canvas, the input geometry of the building typologies is represented through their connectivity map and subsequently augmented to increase the size of the training set. Our variational autoencoder model then learns a continuous latent distribution of the input data from which we can sample to generate new geometry instances, essentially hybrids of the initial input geometries. Finally, we present the results of these computational experiments and lay out the conclusions as well as outlook for future research in this field.
keywords	artificial intelligence; deep neural networks; variational autoencoders; generative design; form finding; structural design
series	eCAADeSIGraDi
email
last changed	2022/06/07 07:55

_id	acadia19_412
id	acadia19_412
authors	Del Campo, Matias; Manninger, Sandra; Carlson, Alexandra
year	2019
title	Imaginary Plans
doi	https://doi.org/10.52842/conf.acadia.2019.412
source	ACADIA 19:UBIQUITY AND AUTONOMY [Proceedings of the 39th Annual Conference of the Association for Computer Aided Design in Architecture (ACADIA) ISBN 978-0-578-59179-7] (The University of Texas at Austin School of Architecture, Austin, Texas 21-26 October, 2019) pp. 412-418
summary	Artificial Neural Networks (NN) have become ubiquitous across disciplines due to their high performance in modeling the real world to execute complex tasks in the wild. This paper presents a computational design approach that uses the internal representations of deep vision neural networks to generate and transfer stylistic form edits to both 2D floor plans and building sections. The main aim of this paper is to demonstrate and interrogate a design technique based on deep learning. The discussion includes aspects of machine learning, 2D to 2D style transfers, and generative adversarial processes. The paper examines the meaning of agency in a world where decision making processes are defined by human/machine collaborations (Figure 1), and their relationship to aspects of a Posthuman design ecology. Taking cues from the language used by experts in AI, such as Hallucinations, Dreaming, Style Transfer, and Vision, the paper strives to clarify the position and role of Artificial Intelligence in the discipline of Architecture.
series	ACADIA
type	normal paper
email
last changed	2022/06/07 07:55

_id	ecaadesigradi2019_648
id	ecaadesigradi2019_648
authors	Eisenstadt, Viktor, Langenhan, Christoph and Althoff, Klaus-Dieter
year	2019
title	Generation of Floor Plan Variations with Convolutional Neural Networks and Case-based Reasoning - An approach for transformative adaptation of room configurations within a framework for support of early conceptual design phases
doi	https://doi.org/10.52842/conf.ecaade.2019.2.079
source	Sousa, JP, Xavier, JP and Castro Henriques, G (eds.), Architecture in the Age of the 4th Industrial Revolution - Proceedings of the 37th eCAADe and 23rd SIGraDi Conference - Volume 2, University of Porto, Porto, Portugal, 11-13 September 2019, pp. 79-84
summary	We present an approach for computer-aided generation of different variations of floor plans during the early phases of conceptual design in architecture. The early design phases are mostly characterized by the processes of inspiration gaining and search for contextual help in order to improve the building design at hand. The generation method described in this work uses the novel as well as established artificial intelligence methods, namely, generative adversarial nets and case-based reasoning, for creation of possible evolutions of the current design based on the most similar previous designs. The main goal of this approach is to provide the designer with information on how the current floor plan can evolve over time in order to influence the direction of the design process. The work described in this paper is part of the methodology FLEA (Find, Learn, Explain, Adapt) whose task is to provide a holistic structure for support of the early conceptual phases in architecture. The approach is implemented as the adaptation component of the framework MetisCBR that is based on FLEA.
keywords	room configuration; adaptation; case-based reasoning; convolutional neural networks; conceptual design
series	eCAADeSIGraDi
email
last changed	2022/06/07 07:55

_id	ecaadesigradi2019_135
id	ecaadesigradi2019_135
authors	Newton, David
year	2019
title	Deep Generative Learning for the Generation and Analysis of Architectural Plans with Small Datasets
doi	https://doi.org/10.52842/conf.ecaade.2019.2.021
source	Sousa, JP, Xavier, JP and Castro Henriques, G (eds.), Architecture in the Age of the 4th Industrial Revolution - Proceedings of the 37th eCAADe and 23rd SIGraDi Conference - Volume 2, University of Porto, Porto, Portugal, 11-13 September 2019, pp. 21-28
summary	The field of generative architectural design has explored a wide range of approaches in the automation of design production, but these approaches have demonstrated limited artificial intelligence. Generative Adversarial Networks (GANs) are a leading deep generative model that use deep neural networks (DNNs) to learn from a set of training examples in order to create new design instances with a degree of flexibility and fidelity that outperform competing generative approaches. Their application to generative tasks in architecture, however, has been limited. This research contributes new knowledge on the use of GANs for architectural plan generation and analysis in relation to the work of specific architects. Specifically, GANs are trained to synthesize architectural plans from the work of the architect Le Corbusier and are used to provide analytic insight. Experiments demonstrate the efficacy of different augmentation techniques that architects can use when working with small datasets.
keywords	generative design; deep learning; artificial intelligence; generative adversarial networks
series	eCAADeSIGraDi
email
last changed	2022/06/07 07:58

_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	acadia19_404
id	acadia19_404
authors	Liu, Henan; Liao, Longtai; Srivastava, Akshay
year	2019
title	AN ANONYMOUS COMPOSITION
doi	https://doi.org/10.52842/conf.acadia.2019.404
source	ACADIA 19:UBIQUITY AND AUTONOMY [Proceedings of the 39th Annual Conference of the Association for Computer Aided Design in Architecture (ACADIA) ISBN 978-0-578-59179-7] (The University of Texas at Austin School of Architecture, Austin, Texas 21-26 October, 2019) pp. 404-411
summary	Within the context of continuous technology transformations, the way scientists and designers process data is changing dramatically from simplification and explicit defined rules to searching and retrieving. Ideally, such a trending method can eliminate issues including deviation and ambiguity with the help of hypothetically unlimited computational power. To process data in this manner, artificial intelligence is necessary and needs to be integrated into the design process. An experiment of a design process that consists of a generative model, a data library, and a machine learning system (GAN) is introduced to demonstrate its effectiveness. The methodology is further evaluated by comparing its output with its input targets, which proves the possibility of employing machine learning systems to aggressively process data and automate the design process. Further improvement of such methodology, including judging criteria and possible applications, and the sensibility of the machine is also discussed at the end.
keywords	Machine Learning, Automation, Variables, Data Processing, Sensibility, Generative Design
series	ACADIA
type	normal paper
email
last changed	2022/06/07 07:59

_id	acadia20_202p
id	acadia20_202p
authors	Battaglia, Christopher A.; Verian, Kho; Miller, Martin F.
year	2020
title	DE:Stress Pavilion
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. 202-207
summary	Print-Cast Concrete investigates concrete 3D printing utilizing robotically fabricated recyclable green sand molds for the fabrication of thin shell architecture. The presented process expedites the production of doubly curved concrete geometries by replacing traditional formwork casting or horizontal corbeling with spatial concrete arching by developing a three-dimensional extrusion path for deposition. Creating robust non-zero Gaussian curvature in concrete, this method increases fabrication speed for mass customized elements eliminating two-part mold casting by combining robotic 3D printing and extrusion casting. Through the casting component of this method, concrete 3D prints have greater resolution along the edge condition resulting in tighter assembly tolerances between multiple aggregated components. Print-Cast Concrete was developed to produce a full-scale architectural installation commissioned for Exhibit Columbus 2019. The concrete 3D printed compression shell spanned 12 meters in length, 5 meters in width, and 3 meters in height and consisted of 110 bespoke panels ranging in weight of 45 kg to 160 kg per panel. Geometrical constraints were determined by the bounding box of compressed sand mold blanks and tooling parameters of both CNC milling and concrete extrusion. Using this construction method, the project was able to be assembled and disassembled within the timeframe of the temporary outdoor exhibit, produce <1% of waste mortar material in fabrication, and utilize 60% less material to construct than cast-in-place construction. Using the sand mold to contain geometric edge conditions, the Print-Cast technique allows for precise aggregation tolerances. To increase the pavilions resistance to shear forces, interlocking nesting geometries are integrated into each edge condition of the panels with .785 radians of the undercut. Over extruding strategically during the printing process casts the undulating surface with accuracy. When nested together, the edge condition informs both the construction logic of the panel’s placement and orientation for the concrete panelized shell.
series	ACADIA
type	project
email
last changed	2021/10/26 08:08

_id	acadia19_156
id	acadia19_156
authors	Dahy, Hanaa; Baszyñski, Piotr; Petrš, Jan
year	2019
title	Experimental Biocomposite Pavilion
doi	https://doi.org/10.52842/conf.acadia.2019.156
source	ACADIA 19:UBIQUITY AND AUTONOMY [Proceedings of the 39th Annual Conference of the Association for Computer Aided Design in Architecture (ACADIA) ISBN 978-0-578-59179-7] (The University of Texas at Austin School of Architecture, Austin, Texas 21-26 October, 2019) pp. 156-165
summary	Excessive use of aggregate materials and metals in construction should be balanced by increasing use of construction materials from annually renewable resources based on natural lignocellulosic fibers. Parametric design tools gave here a possibility of using an alternative newly developed biocomposite material, for realization of complex geometries. Contemporary digital fabrication tools have enabled precise manufacturing possibilities and sophisticated geometry-making to take place that helped in obtaining high structural behavior of the overall global geometry of the discussed project. This paper presents a process of realizing an experimental structure made from Natural Fiber-Reinforced Polymers (NFRP)- also referred to as biocomposites, which were synthesized from lignocellulosic flexible core reinforced by 3D-veneer layers in a closed-moulding vacuum-assisted process. The biocomposite sandwich panels parameters were developed and defined before the final properties were imbedded in the parametric model. This paper showcases the multi-disciplinarity work between architects, structural engineers and material developers. It allowed the architects to work on the material development themselves and enabled to apply a new created design philosophy by the first author, namely applying ‘Materials as a Design-Tool’. The erected biocomposite segmented shell construction allowed a 1:1 validation for the whole design process, material development and the digital fabrication processes applied. The whole development has been reached after merging an ongoing industrial research project results with academic education at the school of architecture in Stuttgart-Germany.
series	ACADIA
type	normal paper
email
last changed	2022/06/07 07:56

_id	caadria2019_478
id	caadria2019_478
authors	Fingrut, Adam, Crolla, Kristof and Lau, Darwin
year	2019
title	Automation Complexity - Brick By Brick
doi	https://doi.org/10.52842/conf.caadria.2019.1.093
source	M. Haeusler, M. A. Schnabel, T. Fukuda (eds.), Intelligent & Informed - Proceedings of the 24th CAADRIA Conference - Volume 1, Victoria University of Wellington, Wellington, New Zealand, 15-18 April 2019, pp. 93-102
summary	This paper discusses the assembly of brick structures with a Cable Driven Parallel Robot (CDPR). Explored is the impact of using computational design tools and the deployment of robotic equipment for the creation of an expanded architectural design space, based on the limits of material and equipment in place of a skilled labor force.
keywords	Cable-Robot; Construction Automation; Digital Fabrication; Construction Complexity; Non-Standard Architecture
series	CAADRIA
email
last changed	2022/06/07 07:50

_id	caadria2019_211
id	caadria2019_211
authors	Globa, Anastasia, Wang, Rui and Beza, Beau B.
year	2019
title	Sensory Urbanism and Placemaking - Exploring Virtual Reality and the Creation of Place
doi	https://doi.org/10.52842/conf.caadria.2019.2.737
source	M. Haeusler, M. A. Schnabel, T. Fukuda (eds.), Intelligent & Informed - Proceedings of the 24th CAADRIA Conference - Volume 2, Victoria University of Wellington, Wellington, New Zealand, 15-18 April 2019, pp. 737-746
summary	Sensory Urbanism is an experimental prototyping project exploring the potential of immersive Virtual Reality (VR) environments to support the incorporation of sensory and intangible aspects of place. The study investigates how sensory exploration of urban places can be integrated into decision making regarding the future of cities. In the past, numerous studies reported various sophisticated 'livability' measures, deeming to determine what makes a city a great place to live in. While a part of these measures can be quantified and be represented as text, graphs or images, most of the qualitative aspects of place are inherently abstract and sensory. These aspects have to be experienced to be understood and therefore they are extremely difficult to communicate using conventional representation means. The proposition explored in this study is that the increasing ubiquity of VR and Augmented Reality (AR) technologies can provide new opportunities to engage with the multi-sensory and temporal aspects of urban place. A mixed media approach was adopted, tapping into a temporal dimension as well as visual, aural and kinesthetic range of human senses. The paper reports on the development of the VR sensory urbanism prototype and the initial pilot study that demonstrated the proof-of-concept.
keywords	Sensory Urbanism; Immersive Environments; Virtual Reality; Design Evaluation; Placemaking
series	CAADRIA
email
last changed	2022/06/07 07:51

_id	ijac201917204
id	ijac201917204
authors	Karaoglan Füsun Cemre and Sema Alaçam
year	2019
title	Design of a post-disaster shelter through soft computing
source	International Journal of Architectural Computing vol. 17 - no. 2, 185-205
summary	Temporary shelters become a more critical subject of architectural design as the increasing number of natural disasters taking place each year result in a larger number of people in need of urgent sheltering. Therefore, this project focuses on designing a temporary living space that can respond to the needs of different post-disaster scenarios and form a modular system through differentiation of units. When designing temporary shelters, it is a necessity to deal with the provision of materials, low-cost production and the time limit in the emergency as well as the needs of the users and the experiential quality of the space. Although computational approaches might lead to much more efficient and resilient design solutions, they have been utilized in very few examples. For that reason and due to their suitability to work with architectural design problems, soft computing methods shape the core of the methodology of the study. Initially, a digital model is generated through a set of rules that define a growth algorithm. Then, Multi-Objective Genetic Algorithms alter this growth algorithm while evaluating different configurations through the objective functions constructed within a Fuzzy Neural Tree. The struggle to represent design goals in the form of Fuzzy Neural Tree holds potential for the further use of it for architectural design problems centred on resilience. Resilience in this context is defined as a measure of how agile a design is when dealing with a major sheltering need in a post-disaster environment. Different from the previous studies, this article aims to focus on the design of a temporary shelter that can respond to different user types and disaster scenarios through mass customization, using Fuzzy Neural Tree as a novel approach. While serving as a temporary space, the design outcomes are expected to create a more neighbourhood-like pattern with a stronger sense of community for the users compared to the previous examples.
keywords	Humanitarian design, emergency architecture, computational design, Fuzzy Neural Tree, Multi-Objective Genetic Algorithms
series	journal
email
last changed	2019/08/07 14:04

_id	acadia19_470
id	acadia19_470
authors	Meyboom, AnnaLisa; Correa, David; Krieg, Oliver David
year	2019
title	Stressed Skin Wood Surface Structure
doi	https://doi.org/10.52842/conf.acadia.2019.470
source	ACADIA 19:UBIQUITY AND AUTONOMY [Proceedings of the 39th Annual Conference of the Association for Computer Aided Design in Architecture (ACADIA) ISBN 978-0-578-59179-7] (The University of Texas at Austin School of Architecture, Austin, Texas 21-26 October, 2019) pp. 470-477
summary	Innovation in parametric design and robotic fabrication is in reciprocal relationship with the investigation of new structural types that facilitated by this technology. The stressed skin structure has historically been used to create lightweight curved structures, mainly in engineering applications such as naval vessels, aircraft, and space shuttles. Stressed skin structures were first referred to by Fairbairn in 1849. In England, the first use of the structure was in the Mosquito night bomber of World War II. In the United States, stressed skin structures were used at the same time, when the Wright Patterson Air Force Base designed and fabricated the Vultee BT-15 fuselage using fiberglass-reinforced polyester as the face material and both glass-fabric honeycomb and balsa wood core. With the renewed interest in wood as a structural building material, due to its sustainable characteristics, new potentials for the use of stressed skin structures made from wood on building scales are emerging. The authors present a material informed system that is characterized by its adaptability to freeform curvature on exterior surfaces. A stressed skin system can employ thinner materials that can be bent in their elastic bending range and then fixed into place, leading to the ability to be architecturally malleable, structurally highly efficient, as well as easily buildable. The interstitial space can also be used for services. Advanced digital fabrication and robotic manufacturing methods further enhance this capability by enabling precisely fabricated tolerances and embedded assembly instructions; these are essential to fabricate complex, multi-component forms. Through a prototypical installation, the authors demonstrate and discuss the technology of the stressed skin structure in wood considering current digital design and fabrication technologies.
series	ACADIA
type	normal paper
email
last changed	2022/06/07 07:58

_id	acadia19_616
id	acadia19_616
authors	Sitnikov, Vasily; Eigenraam, Peter; Papanastasis, Panagiotis; Wassermann-Fry, Stephan
year	2019
title	IceFormwork for Cast HPFRC Elements
doi	https://doi.org/10.52842/conf.acadia.2019.616
source	ACADIA 19:UBIQUITY AND AUTONOMY [Proceedings of the 39th Annual Conference of the Association for Computer Aided Design in Architecture (ACADIA) ISBN 978-0-578-59179-7] (The University of Texas at Austin School of Architecture, Austin, Texas 21-26 October, 2019) pp. 616-627
summary	The following paper introduces a design implementation of an innovative fabrication method that aims at enabling an environmental and automated production of geometrically challenging cast concrete elements. The fabrication method is based on the use of ice as the molding material for cast concrete. Empirical testing of ice CNC-processing, and a concrete mix capable of hardening at subzero temperatures was undertaken during previous research stages. The current paper illustrates a practical application of ice formwork. A façade rain screen has been developed using algorithmic modeling to illustrate a common case in which a non-repetitive geometrical pattern requires individual formwork to be produced for each element. Existing industrial methods capable of delivering such a project for formidable costs are based on CNC-processed expanded polystyrene (EPS), wood-based materials, or industrial wax formwork. These materials have been found to be either difficult to recycle, expensive, insufficiently strong, energy- or labor-intensive to produce. Preliminary evaluation has shown that ice, used in their place, facilitates a much cleaner, economic, and an even more energy-efficient process. Moreover, a very gentle demolding process through ice-thawing eliminates any shock stresses exposed on newly cast concrete and provides optimal curing conditions. As a result, the thickness of façade elements can be reduced while still fulfilling all structural requirements.
series	ACADIA
type	normal paper
email
last changed	2022/06/07 07:56

_id	cf2019_003
id	cf2019_003
authors	Steinfeld, Kyle; Katherine Park, Adam Menges and Samantha Walker
year	2019
title	Fresh Eyes A framework for the application of machine learning to generative architectural design, and a report of activities at Smartgeometry 2018
source	Ji-Hyun Lee (Eds.) "Hello, Culture!" [18th International Conference, CAAD Futures 2019, Proceedings / ISBN 978-89-89453-05-5] Daejeon, Korea, p. 22
summary	This paper presents a framework for the application of Machine Learning (ML) to Generative Architectural Design (GAD), and illustrates this framework through a description of a series of projects completed at the Smart Geometry conference in May of 2018 (SG 2018) in Toronto. Proposed here is a modest modification of a 3-step process that is well-known in generative architectural design, and that proceeds as: generate, evaluate, iterate. In place of the typical approaches to the evaluation step, we propose to employ a machine learning process: a neural net trained to perform image classification. This modified process is different enough from traditional methods as to warrant an adjustment of the terms of GAD. Through the development of this framework, we seek to demonstrate that generative evaluation may be seen as a new locus of subjectivity in design.
keywords	Machine Learning, Generative Design, Design Methods
series	CAAD Futures
email
last changed	2019/07/29 14:08

_id	caadria2019_117
id	caadria2019_117
authors	Deniz Kiraz, Leyla and Kocaturk, Tuba
year	2019
title	Integrating User-Behaviour as Performance Criteria in Conceptual Parametric Design
doi	https://doi.org/10.52842/conf.caadria.2019.1.215
source	M. Haeusler, M. A. Schnabel, T. Fukuda (eds.), Intelligent & Informed - Proceedings of the 24th CAADRIA Conference - Volume 1, Victoria University of Wellington, Wellington, New Zealand, 15-18 April 2019, pp. 215-224
summary	Prediction of user behaviour has always been problematic in architectural design. Several methods have already been developed and explored to model human behaviour in architecture. However, the majority of these methods are implemented during post-design evaluation where the insights obtained can only be implemented in a limited capacity. There is an apparent gap and opportunity, in current research and practice, to embed behaviour simulations directly into the conceptual design process. The proposed paper (research) aims to fill this gap. This paper will report on the results of a recently completed research exploring the integration process of Agent Based Modelling into the conceptual design process, using a parametric design approach. The research resulted in the development of a methodological framework for the integration of behavioural parameters into the explorative stages of the early design process. This paper also offers a categorisation and critical evaluation of existing Agent Based Modelling applications in current research and practice, which leads to the formulation of possible pathways for future implementation.
keywords	Performance Based Design; Generative Design; Behaviour Modelling; Agent Based Modelling; Parametric Design
series	CAADRIA
email
last changed	2022/06/07 07:55

_id	ecaadesigradi2019_239
id	ecaadesigradi2019_239
authors	Garrido, Federico and Meyer, Joost
year	2019
title	Dexterity-controlled Design Procedures
doi	https://doi.org/10.52842/conf.ecaade.2019.1.659
source	Sousa, JP, Xavier, JP and Castro Henriques, G (eds.), Architecture in the Age of the 4th Industrial Revolution - Proceedings of the 37th eCAADe and 23rd SIGraDi Conference - Volume 1, University of Porto, Porto, Portugal, 11-13 September 2019, pp. 659-668
summary	This paper explores the development of design procedures in relationship to their digital proceedings, in order to interface human movement and parametric design procedures. The research studied the use of Leap Motion controller, a gesture recognition device using infrared sensors combined with time-based generative tools in Rhinoceros Grasshopper. A physical, artistic procedure was used as a reference to model a digital design procedure, including a series of parametric definitions combined with them in an attempt to produce complex three-dimensional designs in real time. In a later stage of this research, a modular, open source, digitizing arm was developed to capture hand movement and interact with an autonomous parametric definition, augmenting even more the range of applications of dexterity-based digital design. The challenge of this experimental investigation lies in the negotiation of the designer's needs for a complex yet open design process and the possibilities of defined soft- and hardware solutions.
keywords	digital design; dexterity; parametric design; motion detection
series	eCAADeSIGraDi
email
last changed	2022/06/07 07:51

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