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	ecaade2020_390
id	ecaade2020_390
authors	Ahmadzadeh Bazzaz, Siamak, Fioravanti, Antonio and Coraglia, Ugo Maria
year	2020
title	Depth and Distance Perceptions within Virtual Reality Environments - A Comparison between HMDs and CAVEs in Architectural Design
doi	https://doi.org/10.52842/conf.ecaade.2020.1.375
source	Werner, L and Koering, D (eds.), Anthropologic: Architecture and Fabrication in the cognitive age - Proceedings of the 38th eCAADe Conference - Volume 1, TU Berlin, Berlin, Germany, 16-18 September 2020, pp. 375-382
summary	The Perceptions of Depth and Distance are considered as two of the most important factors in Virtual Reality Environments, as these environments inevitability impact the perception of the virtual content compared with the one of real world. Many studies on depth and distance perceptions in a virtual environment exist. Most of them were conducted using Head-Mounted Displays (HMDs) and less with large screen displays such as those of Cave Automatic Virtual Environments (CAVEs). In this paper, we make a comparison between the different aspects of perception in the architectural environment between CAVE systems and HMD. This paper clarifies the Virtual Object as an entity in a VE and also the pros and cons of using CAVEs and HMDs are explained. Eventually, just a first survey of the planned case study of the artificial port of the Trajan emperor near Fiumicino has been done as for COVID-19 an on-field experimentation could not have been performed.
keywords	Visual Perception; Depth and Distance Perception; Virtual Reality; HMD; CAVE; Trajan’s port
series	eCAADe
email
last changed	2022/06/07 07:54

_id	ecaade2020_222
id	ecaade2020_222
authors	Ikeno, Kazunosuke, Fukuda, Tomohiro and Yabuki, Nobuyoshi
year	2020
title	Automatic Generation of Horizontal Building Mask Images by Using a 3D Model with Aerial Photographs for Deep Learning
doi	https://doi.org/10.52842/conf.ecaade.2020.2.271
source	Werner, L and Koering, D (eds.), Anthropologic: Architecture and Fabrication in the cognitive age - Proceedings of the 38th eCAADe Conference - Volume 2, TU Berlin, Berlin, Germany, 16-18 September 2020, pp. 271-278
summary	Information extracted from aerial photographs is widely used in urban planning and design. An effective method for detecting buildings in aerial photographs is to use deep learning for understanding the current state of a target region. However, the building mask images used to train the deep learning model are manually generated in many cases. To solve this challenge, a method has been proposed for automatically generating mask images by using virtual reality 3D models for deep learning. Because normal virtual models do not have the realism of a photograph, it is difficult to obtain highly accurate detection results in the real world even if the images are used for deep learning training. Therefore, the objective of this research is to propose a method for automatically generating building mask images by using 3D models with textured aerial photographs for deep learning. The model trained on datasets generated by the proposed method could detect buildings in aerial photographs with an accuracy of IoU = 0.622. Work left for the future includes changing the size and type of mask images, training the model, and evaluating the accuracy of the trained model.
keywords	Urban planning and design; Deep learning; Semantic segmentation; Mask image; Training data; Automatic design
series	eCAADe
email
last changed	2022/06/07 07:50

_id	sigradi2021_302
id	sigradi2021_302
authors	Bueno, Ernesto, Reis Balsini, André and Verde Zein, Ruth
year	2021
title	Analysis by Algorithmic Modeling of Historiographical Data on Modern and Contemporary Brazilian Architecture
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. 737–748
summary	Are historiographic diagrams valid instruments for gauging the main constituent aspects of historiographic documentation of a body of architectural production? The paper aims to discuss the results obtained by algorithmic modeling and three-dimensional visualization of historiographic data. The analysis method proposes a diagrammatic approach to the research object, established from the fundamentals originally described by Zein (2020). The diagrams were created using the algorithmic modeling software Grasshopper, which allowed us to combine a precise recording of data with an original approach to its interpretation. From the data collected, Cartesian coordinates were established for the generation of curves and interpolation surfaces representative of the computed aspects of certain historiographic narratives. With wide application possibilities, the resulting algorithmic diagrams establish a new model for data analysis and visualization, which stands as a consistent alternative to other more commonly used digital bibliometric tools.
keywords	Análise de dados, Big Data, Visualizaçao de dados, Historiografia, Arquitetura moderna brasileira
series	SIGraDi
email
last changed	2022/05/23 12:11

_id	cdrf2019_179
id	cdrf2019_179
authors	Yuzhe Pan, Jin Qian, and Yingdong Hu
year	2020
title	A Preliminary Study on the Formation of the General Layouts on the Northern Neighborhood Community Based on GauGAN Diversity Output Generator
doi	https://doi.org/https://doi.org/10.1007/978-981-33-4400-6_17
source	Proceedings of the 2020 DigitalFUTURES The 2nd International Conference on Computational Design and Robotic Fabrication (CDRF 2020)
summary	Recently, the mainstream gradually has become replacing neighborhood-style communities with high-density residences. The original pleasant scale and enclosed residential spaces have been broken, and the traditional neighborhood relations are going away. This research uses machine learning to train the model to generate a new plan, which is used in today’s residential design. First, in order to obtain a better generation effect, this study extracts the transcendental information of the neighborhood community in north of China, using roads, buildings etc. as morphological representations; GauGAN, compared to the pix2pix and pix2pixHD, used by predecessors, can achieve a clearer and a more diversified output and also fit irregular contours more realistically. ANN model trained by 167 general layout samples of a neighborhood community in north of China from 1950s to 1970s can generate various general layouts in different shapes and scales. The experiment proves that GauGAN is more suitable for general layout generation than pix2pix (pix2pixHD); Distributed training can improve the clarity of the generation and allow later vectorization to be more convenient.
series	cdrf
email
last changed	2022/09/29 07:51

_id	ecaade2020_018
id	ecaade2020_018
authors	Sato, Gen, Ishizawa, Tsukasa, Iseda, Hajime and Kitahara, Hideo
year	2020
title	Automatic Generation of the Schematic Mechanical System Drawing by Generative Adversarial Network
doi	https://doi.org/10.52842/conf.ecaade.2020.1.403
source	Werner, L and Koering, D (eds.), Anthropologic: Architecture and Fabrication in the cognitive age - Proceedings of the 38th eCAADe Conference - Volume 1, TU Berlin, Berlin, Germany, 16-18 September 2020, pp. 403-410
summary	In the front-loaded project workflow, mechanical, electrical, and plumbing (MEP) design requires precision from the beginning of the design phase. Leveraging insights from as-built drawings during the early design stage can be beneficial to design enhancement. This study proposes a GAN (Generative Adversarial Networks)-based system which populates the fire extinguishing (FE) system onto the architectural drawing image as its input. An algorithm called Pix2Pix with the improved loss function enabled such generation. The algorithm was trained by the dataset, which includes pairs of as-built building plans with and without FE equipment. A novel index termed Piping Coverage Rate was jointly proposed to evaluate the obtained results. The system produces the output within 45 seconds, which is drastically faster than the conventional manual workflow. The system realizes the prompt engineering study learned from past as-built information, which contributes to further the data-driven decision making.
keywords	Generative Adversarial Network; MEP; as-built drawing; automated design; data-driven design
series	eCAADe
email
last changed	2022/06/07 07:57

_id	acadia20_94
id	acadia20_94
authors	Yoo, Wonjae; Kim, Hyoungsub; Shin, Minjae; J.Clayton, Mark
year	2020
title	BIM-Based Automatic Contact Tracing System Using Wi-Fi
doi	https://doi.org/10.52842/conf.acadia.2020.1.094
source	ACADIA 2020: Distributed Proximities / Volume I: Technical Papers [Proceedings of the 40th Annual Conference of the Association of Computer Aided Design in Architecture (ACADIA) ISBN 978-0-578-95213-0]. Online and Global. 24-30 October 2020. edited by B. Slocum, V. Ago, S. Doyle, A. Marcus, M. Yablonina, and M. del Campo. 94-101.
summary	This study presents a BIM-based automatic contact tracing method using a stations-oriented indoor localization (SOIL) system. The SOIL system integrates BIM models and existing network infrastructure (i.e., Wi-Fi), using a clustering method to generate roomlevel occupancy schedules. In this study, we improve the accuracy of the SOIL system by including more detailed Wi-Fi signal travel sources, such as reflection, refraction, and diffraction. The results of field measurements in an educational building show that the SOIL system was able to produce room-level occupant location information with a 95.6% level of accuracy. This outcome is 2.6% more accurate than what was found in a previous study. We also describe an implementation of the SOIL system for conducting contact tracing in large buildings. When an individual is confirmed to have COVID-19, public health professionals can use this system to quickly generate information regarding possible contacts. The greatest strength of this SOIL implementation is that it has wide applicability in largescale buildings, without the need for additional sensing devices. Additional tests using buildings with multiple floors are required to further explore the robustness of the system.
series	ACADIA
type	paper
email
last changed	2023/10/22 12:06

_id	cdrf2019_309
id	cdrf2019_309
authors	Yuliya Sinke, Sebastian Gatz, Martin Tamke, and Mette Ramsgaard Thomsen
year	2020
title	Machine Learning for Fabrication of Graded Knitted Membranes
doi	https://doi.org/https://doi.org/10.1007/978-981-33-4400-6_29
source	Proceedings of the 2020 DigitalFUTURES The 2nd International Conference on Computational Design and Robotic Fabrication (CDRF 2020)
summary	This paper examines the use of machine learning in creating digitally integrated design-to-fabrication workflows. As computational design allows for new methods of material specification and fabrication, it enables direct functional grading of material at high detail thereby tuning the design performance in response to performance criteria. However, the generation of fabrication data is often cumbersome and relies on in-depth knowledge of the fabrication processes. Parametric models that set up for automatic detailing of incremental changes, unfortunately, do not accommodate the larger topological changes to the material set up. The paper presents the speculative case study KnitVault. Based on earlier research projects Isoropia and Ombre, the study examines the use of machine learning to train models for fabrication data generation in response to desired performance criteria. KnitVault demonstrates and validates methods for shortcutting parametric interfacing and explores how the trained model can be employed in design cases that exceed the topology of the training examples.
series	cdrf
email
last changed	2022/09/29 07:51

_id	cdrf2019_134
id	cdrf2019_134
authors	Zhen Han, Wei Yan, and Gang Liu
year	2020
title	A Performance-Based Urban Block Generative Design Using Deep Reinforcement Learning and Computer Vision
doi	https://doi.org/https://doi.org/10.1007/978-981-33-4400-6_13
source	Proceedings of the 2020 DigitalFUTURES The 2nd International Conference on Computational Design and Robotic Fabrication (CDRF 2020)
summary	In recent years, generative design methods are widely used to guide urban or architectural design. Some performance-based generative design methods also combine simulation and optimization algorithms to obtain optimal solutions. In this paper, a performance-based automatic generative design method was proposed to incorporate deep reinforcement learning (DRL) and computer vision for urban planning through a case study to generate an urban block based on its direct sunlight hours, solar heat gains as well as the aesthetics of the layout. The method was tested on the redesign of an old industrial district located in Shenyang, Liaoning Province, China. A DRL agent - deep deterministic policy gradient (DDPG) agent - was trained to guide the generation of the schemes. The agent arranges one building in the site at one time in a training episode according to the observation. Rhino/Grasshopper and a computer vision algorithm, Hough Transform, were used to evaluate the performance and aesthetics, respectively. After about 150 h of training, the proposed method generated 2179 satisfactory design solutions. Episode 1936 which had the highest reward has been chosen as the final solution after manual adjustment. The test results have proven that the method is a potentially effective way for assisting urban design.
series	cdrf
email
last changed	2022/09/29 07:51

_id	caadria2020_015
id	caadria2020_015
authors	Zheng, Hao, An, Keyao, Wei, Jingxuan and Ren, Yue
year	2020
title	Apartment Floor Plans Generation via Generative Adversarial Networks
doi	https://doi.org/10.52842/conf.caadria.2020.2.599
source	D. Holzer, W. Nakapan, A. Globa, I. Koh (eds.), RE: Anthropocene, Design in the Age of Humans - Proceedings of the 25th CAADRIA Conference - Volume 2, Chulalongkorn University, Bangkok, Thailand, 5-6 August 2020, pp. 599-608
summary	When drawing architectural plans, designers should always define every detail, so the images can contain enough information to support design. This process usually costs much time in the early design stage when the design boundary has not been finally determined. Thus the designers spend a lot of time working forward and backward drawing sketches for different site conditions. Meanwhile, Machine Learning, as a decision-making tool, has been widely used in many fields. Generative Adversarial Network (GAN) is a model frame in machine learning, specially designed to learn and generate image data. Therefore, this research aims to apply GAN in creating architectural plan drawings, helping designers automatically generate the predicted details of apartment floor plans with given boundaries. Through the machine learning of image pairs that show the boundary and the details of plan drawings, the learning program will build a model to learn the connections between two given images, and then the evaluation program will generate architectural drawings according to the inputted boundary images. This automatic design tool can help release the heavy load of architects in the early design stage, quickly providing a preview of design solutions for architectural plans.
keywords	Machine Learning; Artificial Intelligence; Architectural Design; Interior Design
series	CAADRIA
email
last changed	2022/06/07 07:57

_id	caadria2020_306
id	caadria2020_306
authors	Akizuki, Yuta, Bernhard, Mathias, Kakooee, Reza, Kladeftira, Marirena and Dillenburger, Benjamin
year	2020
title	Generative Modelling with Design Constraints - Reinforcement Learning for Object Generation
doi	https://doi.org/10.52842/conf.caadria.2020.1.445
source	D. Holzer, W. Nakapan, A. Globa, I. Koh (eds.), RE: Anthropocene, Design in the Age of Humans - Proceedings of the 25th CAADRIA Conference - Volume 1, Chulalongkorn University, Bangkok, Thailand, 5-6 August 2020, pp. 445-454
summary	Generative design has been explored to produce unprecedented geometries, nevertheless design constraints are, in most cases, second-graded in the computational process. In this paper, reinforcement learning is deployed in order to explore the potential of generative design satisfying design objectives. The aim is to overcome the three issues identified in the state of the art: topological inconsistency, less variations in style and unpredictability in design. The goal of this paper is to develop a machine learning framework, which works as an intellectual design interpreter capable of codifying an input geometry to form a new geometry. Experiments demonstrate that the proposed method can generate a family of tables of unique aesthetics, satisfying topological consistency under given constraints.
keywords	generative design; computational design; data-driven design; reinforcement learning; machine learning
series	CAADRIA
email
last changed	2022/06/07 07:54

_id	caadria2020_032
id	caadria2020_032
authors	Gu, Zhuoxing and Yang, Chunxia
year	2020
title	Generation of Public Space Structure Based on Digital Multi-agent System - Taking the interaction between self-consensus "Stigmergy" particles and the old city area as an example
doi	https://doi.org/10.52842/conf.caadria.2020.1.285
source	D. Holzer, W. Nakapan, A. Globa, I. Koh (eds.), RE: Anthropocene, Design in the Age of Humans - Proceedings of the 25th CAADRIA Conference - Volume 1, Chulalongkorn University, Bangkok, Thailand, 5-6 August 2020, pp. 285-294
summary	In the study, the ant colony behavior was simulated to establish a parametric multi-agent system with independent consensus "Stigmergy" for interaction with the site. In the experiment, the initial points of the particles correspond to the key historical buildings, and the target points correspond to the important public space nodes. Edit and adjust the motion characteristics, search features, generation and disappearance characteristics of the simulated particles to obtain the main consensus particle swarm distribution and the distributed consensus particle swarm distribution. This form has a compliant or conflicting relationship with the existing urban environment. Using the contours of the self-consensus spatial form, the particle swarm density, and the pointing relationship between the particles and the building can provide a basis for the transformation and renewal of the existing urban environment, thus forming a spatial transformation strategy that more closely matches the user behavior in the space.
keywords	Multi-agent system; Particle property construction; Stigmergy; Self-consensus particles; Public space structure
series	CAADRIA
email
last changed	2022/06/07 07:51

_id	ecaade2020_478
id	ecaade2020_478
authors	Han, Yoon J. and Kotnik, Toni
year	2020
title	A Tomographic computation of Spatial Dynamics
doi	https://doi.org/10.52842/conf.ecaade.2020.2.089
source	Werner, L and Koering, D (eds.), Anthropologic: Architecture and Fabrication in the cognitive age - Proceedings of the 38th eCAADe Conference - Volume 2, TU Berlin, Berlin, Germany, 16-18 September 2020, pp. 89-94
summary	Waning of vigorous discourses about the idea of space as essence in architectural design concurred with the emergence of digital architecture. The notion of space was replaced with the underlying notion of form facilitating optimization of performances and form-generation in digital design ever since. Within the context of digital architecture, the current research investigates a formal method to reintroduce spatial aspects, based on dynamics of architectural space in relation to form, into digital design processes. Accordingly, a computational framework is devised employing the idea of space as dynamic field conditions, in order to capture dynamic interrelation of architectural space with architectural form. That is, spatial dynamics are regarded as data embedded in architectural space, that can imply operational aspects of spatial experiences and / or stimulate corporeal engagements with experiential space, as concepts as action potentials and affordances do (Rasmussen 1964). As a result, the research aims to contribute to the body of knowledge that endeavour to systematize architectural sensibilities that are implicit in design processes by externalization utilizing computation.
keywords	spatial dynamics; dynamic field conditions; dynamic displacement
series	eCAADe
email
last changed	2022/06/07 07:50

_id	sigradi2020_863
id	sigradi2020_863
authors	Jalkh, Heidi
year	2020
title	Morpho-Active Materials: Fabricating auxetic structures with bioinspired behavior
source	SIGraDi 2020 [Proceedings of the 24th Conference of the Iberoamerican Society of Digital Graphics - ISSN: 2318-6968] Online Conference 18 - 20 November 2020, pp. 863-869
summary	This practice-led research lies at the intersection of design, craft, materials science, and biology. Inspired by the responsive mechanism of plant’s biological actuators, and Nature's outstanding capacity of attaining maximal performances while using minimum resources. This thesis explores how to achieve a higher level of integration between the generation of form and behavior with its materialization and fabrication.This research proposes to endow a conventional laminar elastic material with unconventional behavior. Taking as inspiration plants biological actuators, which allows them to sense and adapt according to different environmental stimuli. We explored, developed, and fabricated a range of cellular structures (and in particular auxetics) that have out of the plane shape morphing capabilities, displaying a distinctive behavior in response to a design pattern (spatial cell arrangement) and an actuating force.The final design is a material/geometry-based actuator with reversible behavior, an active material with integrated tunable and responsive capacity which provides the capabilities to sense, adapt and respond to external stimuli within the structure of the material.
keywords	Bioinspired, Auxetic Materials, Shape-shifting, Active matter, Soft matter
series	SIGraDi
email
last changed	2021/07/16 11:53

_id	ijac202018103
id	ijac202018103
authors	Kimm, Geoff
year	2020
title	Actual and experiential shadow origin tagging: A 2.5D algorithm for efficient precinct-scale modelling
source	International Journal of Architectural Computing vol. 18 - no. 1, 41-52
summary	This article describes a novel algorithm for built environment 2.5D digital model shadow generation that allows identities of shadowing sources to be efficiently precalculated. For any point on the ground, all sources of shadowing can be identified and are classified as actual or experiential obstructions to sunlight. The article justifies a 2.5D raster approach in the context of modelling of architectural and urban environments that has in recent times shifted from 2D to 3D, and describes in detail the algorithm which builds on precedents for 2.5D raster calculation of shadows. The algorithm is efficient and is applicable at even precinct scale in low-end computing environments. The simplicity of this new technique, and its independence of GPU coding, facilitates its easy use in research, prototyping and civic engagement contexts. Two research software applications are presented with technical details to demonstrate the algorithm’s use for participatory built environment simulation and generative modelling applications. The algorithm and its shadow origin tagging can be applied to many digital workflows in architectural and urban design, including those using big data, artificial intelligence or community participative processes.
keywords	2.5D raster, actual and experiential shadow origins, generative techniques, participatory built environment simulation, reactive scripting for design
series	journal
email
last changed	2020/11/02 13:34

_id	ijac202018402
id	ijac202018402
authors	Mette Ramsgaard Thomsen, Paul Nicholas, Martin Tamke, Sebastian Gatz, Yuliya Sinke and Gabriella Rossi
year	2020
title	Towards machine learning for architectural fabrication in the age of industry 4.0
source	International Journal of Architectural Computing vol. 18 - no. 4, 335–352
summary	Machine Learning (ML) is opening new perspectives for architectural fabrication, as it holds the potential for the profession to shortcut the currently tedious and costly setup of digital integrated design to fabrication workflows and make these more adaptable. To establish and alter these workflows rapidly becomes a main concern with the advent of Industry 4.0 in building industry. In this article we present two projects, which presents how ML can lead to radical changes in generation of fabrication data and linking these directly to design intent. We investigate two different moments of implementation: linking performance to the generation of fabrication data (KnitCone) and integrating the ability to adapt fabrication data in realtime as response to fabrication processes (Neural-Network Steered Robotic Fabrication). Together they examine how models can employ design information as training data and be trained to by step processes within the digital chain. We detail the advantages and limitations of each experiment, we reflect on core questions and perspectives of ML for architectural fabrication: the nature of data to be used, the capacity of these algorithms to encode complexity and generalize results, their task-specificness versus their adaptability and the tradeoffs of using them with respect to conventional explicit analytical modelling.
keywords	Machine learning, architectural design, industry 4.0, digital fabrication, robotic fabrication, CNC knit, neural networks
series	journal
email
last changed	2021/06/03 23:29

_id	sigradi2020_734
id	sigradi2020_734
authors	Nope Bernal, Alberto; Ramírez, Anna Gabriela; García Alvarado, Rodrigo; Forcael Durán, Eric
year	2020
title	DESIGN OF A NEARLY ZERO-ENERGY HOME WITH EXTREME COLLABORATION IN BIM
source	SIGraDi 2020 [Proceedings of the 24th Conference of the Iberoamerican Society of Digital Graphics - ISSN: 2318-6968] Online Conference 18 - 20 November 2020, pp. 734-741
summary	The global request of energy politics and actions against climate change, reiterate the importance of promoting the nearly zero-energy buildings (nZEB), taking into account environmental habitability and comfort; therefore, this type of buildings has to include a process, design, and construction, intelligent. Accordingly, the present research shows a methodology for the design of almost zero-energy housing, by using BIM under an environment of extreme collaboration; evaluating energy consumption and active solar generation. Thus, the proposed methodology allows optimizing the processes related to design time, level of geometric development, and the application and evaluation of sustainability strategies, to achieve nearly zero-energy housing within the city Concepcion Chile.
keywords	Nearly Zero-energy Buildings, Building Information Modeling, Extreme Collaboration, Sustainability
series	SIGraDi
email
last changed	2021/07/16 11:52

_id	sigradi2021_51
id	sigradi2021_51
authors	Poustinchi, Ebrahim
year	2021
title	PX01-Switch: A Hardware Extension for KUKA Robot Controller Enabling Realtime Safe Operation
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. 1223–1234
summary	PX01-Switch is a research investigation based on an internationally filed patent by the author, focused on human-robot interaction and robotic control/motion in the field of design (Poustinchi, E. 2020). Using hardware solutions, PX01-Switch enables users—with limited or no programming background, to convert any simple or complicated “offline” developed robotic operations for KUKA robots, to realtime online operational strategies without needing any additional software package or coding knowledge. Operating as a hardware plug-in, PX01-Switch—as a device, can be added to any KUKA robot with the 4th generation controller—KRC4, regardless of the robot's type and payload. PX01-Switch aims to make realtime robotic interaction more accessible to general users, by simplifying some of the advanced programming aspects of the process, at the cost of reducing the operation/interaction resolution.
keywords	Interface design, Human Robot Interaction, Robotics, Design, Digital fabrication
series	SIGraDi
email
last changed	2022/05/23 12:11

_id	acadia20_160p
id	acadia20_160p
authors	Scelsa, Jonathan A.; Birkeland, Jennifer
year	2020
title	The Collective Perspective Machine
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. 160-163
summary	Since the age of humanism, both on the easel and our screens, the production of the architectural image has been conventionally governed by one individual, whom we might refer to as the drafter. As the primary author sitting in the chair of the vantage point, the drafter occupies the privileged position, for whom the translation between the second and third dimensions establishes an approximate realism. The viewers, or secondary participants, by contrast, are relegated to a subordinate position, subject to the residual distortions of the drafter’s vision, based on their relative vantage points. While perhaps cynical, our current condition does not share the same philosophical positivistic optimism of the Renaissance, nor the ideal faith in humanity that empowered the democratic universalisms of modernity. Rather, it is formed from an ambiguous inquiry into creating a new sense of truth, brought forth by the proliferation and amplification of multiple individual ‘perspectives.’ In his conclusion to The Projective Cast, Evans illustrates ten ‘transitive spaces’ of geometric projection towards the generation and representation of a designed object. The fifth “transitive space” describes the space between a building or object and its defined perspectival representations. Evans observes that this path typically follows the progression from the object to a photo or a drawing and is rarely reversed. This project and machine designed for an exhibition seeks to establish a new procedure for generating design, neither subjectively from a personal static individual point nor objectively in the round for all to experience equally. Instead, a new machine establishes form as the hybrid of multiple responsive perspectives wherein all viewers are simultaneously the generator of projective form and the receiver of distorted images.
series	ACADIA
type	project
email
last changed	2021/10/26 08:03

_id	cdrf2019_169
id	cdrf2019_169
authors	Yubo Liu, Yihua Luo, Qiaoming Deng, and Xuanxing Zhou
year	2020
title	Exploration of Campus Layout Based on Generative Adversarial Network Discussing the Significance of Small Amount Sample Learning for Architecture
doi	https://doi.org/https://doi.org/10.1007/978-981-33-4400-6_16
source	Proceedings of the 2020 DigitalFUTURES The 2nd International Conference on Computational Design and Robotic Fabrication (CDRF 2020)
summary	This paper aims to explore the idea and method of using deep learning with a small amount sample to realize campus layout generation. From the perspective of the architect, we construct two small amount sample campus layout data sets through artificial screening with the preference of the specific architects. These data sets are used to train the ability of Pix2Pix model to automatically generate the campus layout under the condition of the given campus boundary and surrounding roads. Through the analysis of the experimental results, this paper finds that under the premise of effective screening of the collected samples, even using a small amount sample data set for deep learning can achieve a good result.
series	cdrf
email
last changed	2022/09/29 07:51

_id	ijac202018304
id	ijac202018304
authors	Aagaard, Anders Kruse and Niels Martin Larsen
year	2020
title	Developing a fabrication workflow for irregular sawlogs
source	International Journal of Architectural Computing vol. 18 - no. 3, 270-283
summary	In this article, we suggest using contemporary manufacturing technologies to integrate material properties with architectural design tools, revealing new possibilities for the use of wood in architecture. Through an investigative approach, material capacities and fabrication methods are explored and combined towards establishing new workflows and architectural expressions, where material, fabrication and result are closely interlinked. The experimentation revolves around discarded, crooked oak logs, doomed to be used as firewood due to their irregularity. This project treats their diverging shapes differently by offering unique processing to each log informed by its particularities. We suggest here a way to use the natural forms and properties of sawlogs to generate new structures and spatial conditions. In this article, we discuss the scope of this approach and provide an example of a workflow for handling the discrete shapes of natural sawlogs in a system that involve the collection of material, scanning/digitisation, handling of a stockpile, computer analysis, design and robotic manufacturing. The creation of this specific method comes from a combination of investigation of wood as a material, review of existing research in the field, studies of the production lines in the current wood industry and experimentation through our in-house laboratory facilities. As such, the workflow features several solutions for handling the complex and different shapes and data of natural wood logs in a highly digitised machining and fabrication environment. This up-cycling of discarded wood supply establishes a non-standard workflow that utilises non-standard material stock and leads to a critical articulation of today’s linear material economy. The project becomes part of an ambition to reach sustainable development goals and technological innovation in global and resource-intensive architecture and building industry.
keywords	Natural wood, robotic fabrication, computation, fabrication, research by design
series	journal
email
last changed	2020/11/02 13:34

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