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	cdrf2019_159
id	cdrf2019_159
authors	Hang Zhang and Ye Huang
year	2020
title	Machine Learning Aided 2D-3D Architectural Form Finding at High Resolution
doi	https://doi.org/https://doi.org/10.1007/978-981-33-4400-6_15
source	Proceedings of the 2020 DigitalFUTURES The 2nd International Conference on Computational Design and Robotic Fabrication (CDRF 2020)
summary	In the past few years, more architects and engineers start thinking about the application of machine learning algorithms in the architectural design field such as building facades generation or floor plans generation, etc. However, due to the relatively slow development of 3D machine learning algorithms, 3D architecture form exploration through machine learning is still a difficult issue for architects. As a result, most of these applications are confined to the level of 2D. Based on the state-of-the-art 2D image generation algorithm, also the method of spatial sequence rules, this article proposes a brand-new strategy of encoding, decoding, and form generation between 2D drawings and 3D models, which we name 2D-3D Form Encoding WorkFlow. This method could provide some innovative design possibilities that generate the latent 3D forms between several different architectural styles. Benefited from the 2D network advantages and the image amplification network nested outside the benchmark network, we have significantly expanded the resolution of training results when compared with the existing form-finding algorithm and related achievements in recent years
series	cdrf
email
last changed	2022/09/29 07:51

_id	acadia20_170
id	acadia20_170
authors	Li, Peiwen; Zhu, Wenbo
year	2020
title	Clustering and Morphological Analysis of Campus Context
doi	https://doi.org/10.52842/conf.acadia.2020.2.170
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. 170-177.
summary	“Figure-ground” is an indispensable and significant part of urban design and urban morphological research, especially for the study of the university, which exists as a unique product of the city development and also develops with the city. In the past few decades, methods adapted by scholars of analyzing the figure-ground relationship of university campuses have gradually turned from qualitative to quantitative. And with the widespread application of AI technology in various disciplines, emerging research tools such as machine learning/deep learning have also been used in the study of urban morphology. On this basis, this paper reports on a potential application of deep clustering and big-data methods for campus morphological analysis. It documents a new framework for compressing the customized diagrammatic images containing a campus and its surrounding city context into integrated feature vectors via a convolutional autoencoder model, and using the compressed feature vectors for clustering and quantitative analysis of campus morphology.
series	ACADIA
type	paper
email
last changed	2023/10/22 12:06

_id	ecaade2020_157
id	ecaade2020_157
authors	Vrouwe, Ivo, Dissaux, Thomas, Jancart, Sylvie and Stals, Adeline
year	2020
title	Concept Learning Through Parametric Design - A learning situation design for parametric design in architectural studio education
doi	https://doi.org/10.52842/conf.ecaade.2020.2.135
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. 135-144
summary	Over the past few decades, architectural practice and, consequently, the design studio have been increasingly challenged. Indeed, the development of digital tools and parametric design, in particular, has given rise to a new type of architectural knowledge. Among the IJAC publications over the past three years, we highlight the current diversity of vocabulary used to discuss this knowledge and develop why we focus our study on conceptual knowledge. We then report a learning situation through studio design education. This paper finally presents the steps developed to measure this knowledge and hypothesizes on the future work needed in order to have relevant quantitative results. The purpose of this paper is to observe the evolution of students' understanding when shifting from a traditional teacher-student relationship to an engaging learning environment, considering the specificities of parametric, and not to suggest a strict method to follow when learning parametric. This could guide teachers to adapt to their own situations.
keywords	Pedagogy; Learning; Parametric Design; Form Study
series	eCAADe
email
last changed	2022/06/07 07:58

_id	ascaad2021_142
id	ascaad2021_142
authors	Bakir, Ramy; Sara Alsaadani, Sherif Abdelmohsen
year	2021
title	Student Experiences of Online Design Education Post COVID-19: A Mixed Methods Study
source	Abdelmohsen, S, El-Khouly, T, Mallasi, Z and Bennadji, A (eds.), Architecture in the Age of Disruptive Technologies: Transformations and Challenges [9th ASCAAD Conference Proceedings ISBN 978-1-907349-20-1] Cairo (Egypt) [Virtual Conference] 2-4 March 2021, pp. 142-155
summary	This paper presents findings of a survey conducted to assess students’ experiences within the online instruction stage of their architectural education during the lockdown period caused by the COVID-19 pandemic between March and June 2020. The study was conducted in two departments of architecture in both Cairo branches of the Arab Academy for Science, Technology & Maritime Transport (AASTMT), Egypt, with special focus on courses involving a CAAD component. The objective of this exploratory study was to understand students’ learning experiences within the online period, and to investigate challenges facing architectural education. A mixed methods study was used, where a questionnaire-based survey was developed to gather qualitative and quantitative data based on the opinions of a sample of students from both departments. Findings focus on the qualitative component to describe students’ experiences, with quantitative data used for triangulation purposes. Results underline students’ positive learning experiences and challenges faced. Insights regarding digital tool preferences were also revealed. Findings are not only significant in understanding an important event that caused remote architectural education in Egypt but may also serve as an important stepping-stone towards the future of design education in light of newly-introduced disruptive online learning technologies made necessary in response to lockdowns worldwide
series	ASCAAD
email
last changed	2021/08/09 13:13

_id	acadia20_226p
id	acadia20_226p
authors	Borhani, Alireza; Kalantar, Negar
year	2020
title	Interlocking Shell
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. 226-231
summary	With a specific focus on robotic stereotomy, two full-scale vault structures were designed to explore the potential of self-standing building structures made from interlocking components; these structures were fabricated with a track-mounted industrial-scale robot (ABB 4600). To respond to the economic affordances of robotic subtractive cutting, all uniquely shaped structural modules came from one block of material (48"" x96"" x36""). Through the discretization of curvilinear tessellated vault surfaces into a limited number of uniquely shaped modules with embedded form-fitting connectors, the project exhibited the potential for programming a robot to cut ruled surfaces to produce freeform shells of any kind. Representing nearly zero-waste construction, the developed technology can potentially be used for self-supporting emergency shelters and field medical clinics, facilitating easy shipping and speedy assembly. Without using any scaffolding, a few people can erect and dismantle an entire mortar-free structure at the construction site. The disassembled structure occupies minimal space in storage, and the structure’s pieces can be transported to the site in stacks. Robot milling is a common technique for removing material to transform a block into a sculptural shape. Unlike milling techniques that produce significant waste, we used a hotwire that sliced through a Geofoam block to create almost no waste pieces. Since the front side of every module was concurrent with the backside of the next one, such a decision allowed to operate just one cut per front side of each module. In this case, by having three cuts, two neighboring modules were fabricated. The form of the structure and its modules emerged from the constraints of the fabrication technique, aiming to establish a feedback loop between geometry, material, simulation, and tool. By cross-referencing geometric data across Grasshopper, a customized tessellation script was made to breakdown a vault into its modular ruled surface constructs.
series	ACADIA
type	project
email
last changed	2021/10/26 08:08

_id	ijac202018403
id	ijac202018403
authors	Dagmar Reinhardt, Matthias Hank Haeusler, Kerry London, Lian Loke, Yingbin Feng, Eduardo De Oliveira Barata, Charlotte Firth, Kate Dunn, Nariddh Khean, Alessandra Fabbri, Dylan Wozniak-O’Connor and Rin Masuda
year	2020
title	CoBuilt 4.0: Investigating the potential of collaborative robotics for subject matter experts
source	International Journal of Architectural Computing vol. 18 - no. 4, 353–370
summary	Human-robot interactions can offer alternatives and new pathways for construction industries, industrial growth and skilled labour, particularly in a context of industry 4.0. This research investigates the potential of collaborative robots (CoBots) for the construction industry and subject matter experts; by surveying industry requirements and assessments of CoBot acceptance; by investing processes and sequences of work protocols for standard architecture robots; and by exploring motion capture and tracking systems for a collaborative framework between human and robot co-workers. The research investigates CoBots as a labour and collaborative resource for construction processes that require precision, adaptability and variability.Thus, this paper reports on a joint industry, government and academic research investigation in an Australian construction context. In section 1, we introduce background data to architecture robotics in the context of construction industries and reports on three sections. Section 2 reports on current industry applications and survey results from industry and trade feedback for the adoption of robots specifically to task complexity, perceived safety, and risk awareness. Section 3, as a result of research conducted in Section 2, introduces a pilot study for carpentry task sequences with capture of computable actions. Section 4 provides a discussion of results and preliminary findings. Section 5 concludes with an outlook on how the capture of computable actions provide the foundation to future research for capturing motion and machine learning.
keywords	Industry 4.0, collaborative robotics, on-site robotic fabrication, industry research, machine learning
series	journal
email
last changed	2021/06/03 23:29

_id	caadria2020_190
id	caadria2020_190
authors	Karakiewicz, Justyna, Holguin, Jose Rafael and Kvan, Thomas
year	2020
title	Hope in Perturbanism
doi	https://doi.org/10.52842/conf.caadria.2020.2.041
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. 41-50
summary	A fundamental assumption in this conference is that human actions in creating and modifying our constructed environments can be rethought and made better for the environment. There are few laboratories in which to conduct research; an isolated island system offers one such opportunity. This paper reflects on work that carried out in the past five years in the Galapagos Islands by a collaborative of researchers from five institutions. The research examines potential positive changes in urban settlements and their impact on a fragile ecology of the islands. In this work, we illustrate how small perturbations (disturbances) within urban systems can lead to changes not only within urban form but also in the citizen's environmental awareness and how these, in turn, can lead to positive changes in the environment. The paper discusses applications of models we developed using Python scripting, GIS, and agent-based modelling, as we applied them to design strategies, built outcomes and community awareness.
keywords	complex adaptive systems; urban design; CAS; panarchy
series	CAADRIA
email
last changed	2022/06/07 07:52

_id	ecaade2020_130
id	ecaade2020_130
authors	Markusiewicz, Jacek and Gortazar Balerdi, Ander
year	2020
title	LOTI - Using Machine Learning to simulate subjective opinions in design.
doi	https://doi.org/10.52842/conf.ecaade.2020.1.439
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. 439-448
summary	The objective of the workshop described in the article was to redesign a chair called Loti. In a subjective opinion shared by the authors and the participants of the workshop, the chair seems plagiarism of a famous chair by Ray and Charles Eames. The authors centralised the workshop on the use of computational tools for assessing subjective opinions. The authors and the participants created a method for detecting plagiarism and implemented it in the process of design. They created a parametric model of the chair that allowed changing the chair's components with variables. Using this model, the participants generated multiple variations and surveyed other students to assess which of the versions seemed plagiarism. With the information obtained from the survey, we trained a neural network to relate the variables with the level of plagiarism. We linked the parametric model with the neural network to create a tool that informs the user about the probability of committing plagiarism in real-time. The participants used the tool for designing new chairs to evaluate the efficiency of the method.
keywords	parametric design; machine learning; interfaces
series	eCAADe
email
last changed	2022/06/07 07:59

_id	ecaade2020_167
id	ecaade2020_167
authors	Newton, David, Piatkowski, Dan, Marshall, Wesley and Tendle, Atharva
year	2020
title	Deep Learning Methods for Urban Analysis and Health Estimation of Obesity
doi	https://doi.org/10.52842/conf.ecaade.2020.1.297
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. 297-304
summary	In the 20th and 21st centuries, urban populations have increased dramatically with a whole host of impacts to human health that remain unknown. Research has shown significant correlations between design features in the built environment and human health, but this research has remained limited. A better understanding of this relationship could allow urban planners and architects to design healthier cities and buildings for an increasingly urbanized population. This research addresses this problem by using discriminative deep learning in combination with satellite imagery of census tracts to estimate rates of obesity. Data from the California Health Interview Survey is used to train a Convolutional Neural Network that uses satellite imagery of selected census tracts to estimate rates of obesity. This research contributes knowledge on methods for applying deep learning to urban health estimation, as well as, methods for identifying correlations between urban morphology and human health.
keywords	Deep Learning; Artificial Intelligence; Urban Planning; Health; Remote Sensing
series	eCAADe
email
last changed	2022/06/07 07:58

_id	acadia20_114p
id	acadia20_114p
authors	Zivkovic, Sasa; Havener, Brian; Battaglia, Christopher
year	2020
title	Log Knot
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. 114-119.
summary	Log Knot, developed by the Robotic Construction Laboratory (RCL) at Cornell University, is a robotically fabricated architectural installation that establishes a method for variable compound timber curvature creation utilizing both regular and irregular roundwood geometries. Moreover, the project develops methods for minimal formwork assembly and moment force optimization of customized mortise and tenon joints. Following the logic of a figure-8 knot, the project consists of an infinite loop of roundwood, curving three-dimensionally along its length. There are a variety of techniques to generate single curvature in wood structures – such as steam bending (Wright et al., 2013) or glue lamination (Issa and Kmeid, 2005) – but only a few techniques to generate complex curvature from raw material within a single wooden structural element exist. To construct complex curvature, the research team developed a simple method that can easily be replicated. First, the log is compartmentalized, establishing a series of discrete parts. Second, the parts are reconfigured into a complex curvature “whole” by carefully manipulating the assembly angles and joints between the logs. Timber components reconfigured in such a manner can either follow planar curvature profiles or spatial compound curvature profiles. Based on knowledge gained from the initial joinery tests, the research team developed a custom tri-fold mortise and tenon joint, which is self-supportive during assembly and able to resist bending in multiple directions. Using the tri-fold mortise and tenon joint, a number of full-scale prototypes were created to test the structural capacity of the overall assembly. Various structural optimization protocols are deployed in the Log Knot project. While the global knot form is derived from spatial considerations – albeit within the structurally sound framework of a closed-loop knot structure – the project is structurally optimized at a local level, closely calibrating structural cross-sections, joinery details, and joint rotation in relation to prevailing load conditions.
series	ACADIA
type	project
email
last changed	2021/10/26 08:03

_id	acadia20_228
id	acadia20_228
authors	Alawadhi, Mohammad; Yan, Wei
year	2020
title	BIM Hyperreality
doi	https://doi.org/10.52842/conf.acadia.2020.1.228
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. 228-236.
summary	Deep learning is expected to offer new opportunities and a new paradigm for the field of architecture. One such opportunity is teaching neural networks to visually understand architectural elements from the built environment. However, the availability of large training datasets is one of the biggest limitations of neural networks. Also, the vast majority of training data for visual recognition tasks is annotated by humans. In order to resolve this bottleneck, we present a concept of a hybrid system—using both building information modeling (BIM) and hyperrealistic (photorealistic) rendering—to synthesize datasets for training a neural network for building object recognition in photos. For generating our training dataset, BIMrAI, we used an existing BIM model and a corresponding photorealistically rendered model of the same building. We created methods for using renderings to train a deep learning model, trained a generative adversarial network (GAN) model using these methods, and tested the output model on real-world photos. For the specific case study presented in this paper, our results show that a neural network trained with synthetic data (i.e., photorealistic renderings and BIM-based semantic labels) can be used to identify building objects from photos without using photos in the training data. Future work can enhance the presented methods using available BIM models and renderings for more generalized mapping and description of photographed built environments.
series	ACADIA
type	paper
email
last changed	2023/10/22 12:06

_id	ecaade2020_499
id	ecaade2020_499
authors	Ashour, Ziad and Yan, Wei
year	2020
title	BIM-Powered Augmented Reality for Advancing Human-Building Interaction
doi	https://doi.org/10.52842/conf.ecaade.2020.1.169
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. 169-178
summary	The shift from computer-aided design (CAD) to building information modeling (BIM) has made the adoption of augmented reality (AR) promising in the field of architecture, engineering and construction. Despite the potential of AR in this field, the industry and professionals have still not fully adopted it due to registration and tracking limitations and visual occlusions in dynamic environments. We propose our first prototype (BIMxAR), which utilizes existing buildings' semantically rich BIM models and contextually aligns geometrical and non-geometrical information with the physical buildings. The proposed prototype aims to solve registration and tracking issues in dynamic environments by utilizing tracking and motion sensors already available in many mobile phones and tablets. The experiment results indicate that the system can support BIM and physical building registration in outdoor and part of indoor environments, but cannot maintain accurate alignment indoor when relying only on a device's motion sensors. Therefore, additional computer vision and AI (deep learning) functions need to be integrated into the system to enhance AR model registration in the future.
keywords	Augmented Reality; BIM; BIM-enabled AR; GPS; Human-Building Interactions; Education
series	eCAADe
email
last changed	2022/06/07 07:54

_id	sigradi2020_60
id	sigradi2020_60
authors	Asmar, Karen El; Sareen, Harpreet
year	2020
title	Machinic Interpolations: A GAN Pipeline for Integrating Lateral Thinking in Computational Tools of Architecture
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. 60-66
summary	In this paper, we discuss a new tool pipeline that aims to re-integrate lateral thinking strategies in computational tools of architecture. We present a 4-step AI-driven pipeline, based on Generative Adversarial Networks (GANs), that draws from the ability to access the latent space of a machine and use this space as a digital design environment. We demonstrate examples of navigating in this space using vector arithmetic and interpolations as a method to generate a series of images that are then translated to 3D voxel structures. Through a gallery of forms, we show how this series of techniques could result in unexpected spaces and outputs beyond what could be produced by human capability alone.
keywords	Latent space, GANs, Lateral thinking, Computational tools, Artificial intelligence
series	SIGraDi
email
last changed	2021/07/16 11:48

_id	ecaade2020_180
id	ecaade2020_180
authors	Bolshakova, Veronika, Besançon, Franck, Guerriero, Annie and Halin, Gilles
year	2020
title	Use of a Digital Collaboration Tool for Project Review - A pedagogical experiment with multidisciplinary teams
doi	https://doi.org/10.52842/conf.ecaade.2020.2.651
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. 651-660
summary	This paper emphasizes feedback from a pedagogical experiment in the context of teaching collaboration and design to multidisciplinary teams. A digital collaboration tool, a multi-touch table and collaboration software, was used as a support for discussion and decision-making for weekly project review meetings. The experiment participants' feedback on the use and usability of the digital collaboration tool highlights the potential for the use of synchronous collaboration technology and project-based learning for higher-level education. It also highlights the need for a transition towards implementation of digital tools at project review sessions.
keywords	: Synchronous collaboration; Pedagogical experiment; Project-based learning; CSCW; NUI; BIM
series	eCAADe
email
last changed	2022/06/07 07:54

_id	ecaade2020_047
id	ecaade2020_047
authors	Brown, Lachlan, Yip, Michael, Gardner, Nicole, Haeusler, M. Hank, Khean, Nariddh, Zavoleas, Yannis and Ramos, Cristina
year	2020
title	Drawing Recognition - Integrating Machine Learning Systems into Architectural Design Workflows
doi	https://doi.org/10.52842/conf.ecaade.2020.2.289
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. 289-298
summary	Machine Learning (ML) has valuable applications that are yet to be proliferated in the AEC industry. Yet, ML offers arguably significant new ways to produce and assist design. However, ML tools are too often out of the reach of designers, severely limiting opportunities to improve the methods by which designers design. To address this and to optimise the practices of designers, the research aims to create a ML tool that can be integrated into architectural design workflows. Thus, this research investigates how ML can be used to universally move BIM data across various design platforms through the development of a convolutional neural network (CNN) for the recognition and labelling of rooms within floor plan images of multi-residential apartments. The effects of this computation and thinking shift will have meaningful impacts on future practices enveloping all major aspects of our built environment from designing, to construction to management.
keywords	machine learning; convolutional neural networks; labelling and classification; design recognition
series	eCAADe
email
last changed	2022/06/07 07:54

_id	sigradi2020_668
id	sigradi2020_668
authors	Cenci, Laline Elisangela; Pires, Júlio César Pinheiro; Vieira, Stéphane Soares
year	2020
title	Measuring the experience of algorithmic thought digital analogue design in architecture teaching
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. 668-675
summary	Due to constant technological developments, society’s priorities and cultural perspectives have changed, requiring a redefinition of experiences in education. In the field of architecture teaching, the transition from CAD (Computer-Aided Design) to the design systems in other digital media, such as the parametric design, can be observed. This article aims to demonstrate two analog-digital experiences in an architecture school. The methodology consisted of dividing the activities into three stages: analog, logical, and digital. The results are described through quantitative and qualitative data acquired in the experiences. The data allowed toreflect on the strategies adopted, lessons learned, and futures challenges.
keywords	Teaching-learning, Parametric Design, Design Script, Dynamo Studio
series	SIGraDi
email
last changed	2021/07/16 11:52

_id	cdrf2019_17
id	cdrf2019_17
authors	Chuan Liu, Jiaqi Shen, Yue Ren, and Hao Zheng
year	2020
title	Pipes of AI – Machine Learning Assisted 3D Modeling Design
doi	https://doi.org/https://doi.org/10.1007/978-981-33-4400-6_2
source	Proceedings of the 2020 DigitalFUTURES The 2nd International Conference on Computational Design and Robotic Fabrication (CDRF 2020)
summary	Style transfer is a design technique that is based on Artificial Intelligence and Machine Learning, which is an innovative way to generate new images with the intervention of style images. The output image will carry the characteristic of style image and maintain the content of the input image. However, the design technique is employed in generating 2D images, which has a limited range in practical use. Thus, the goal of the project is to utilize style transfer as a toolset for architectural design and find out the possibility for a 3D modeling design. To implement style transfer into the research, floor plans of different heights are selected from a given design boundary and set as the content images, while a framework of a truss structure is set as the style image. Transferred images are obtained after processing the style transfer neural network, then the geometric images are translated into floor plans for new structure design. After the selection of the tilt angle and the degree of density, vertical components that connecting two adjacent layers are generated to be the pillars of the structure. At this stage, 2D style transferred images are successfully transformed into 3D geometries, which can be applied to the architectural design processes. Generally speaking, style transfer is an intelligent design tool that provides architects with a variety of choices of idea-generating. It has the potential to inspire architects at an early stage of design with not only 2D but also 3D format.
series	cdrf
email
last changed	2022/09/29 07:51

_id	cdrf2019_36
id	cdrf2019_36
authors	Dan Luo, Joseph M. Gattas, and Poah Shiun Shawn Tan
year	2020
title	Real-Time Defect Recognition and Optimized Decision Making for Structural Timber Jointing
doi	https://doi.org/https://doi.org/10.1007/978-981-33-4400-6_4
source	Proceedings of the 2020 DigitalFUTURES The 2nd International Conference on Computational Design and Robotic Fabrication (CDRF 2020)
summary	Non-structural or out-of-grade timber framing material contains a large proportion of visual and natural defects. A common strategy to recover usable material from these timbers is the marking and removing of defects, with the generated intermediate lengths of clear wood then joined into a single piece of fulllength structural timber. This paper presents a novel workflow that uses machine learning based image recognition and a computational decision-making algorithm to enhance the automation and efficiency of current defect identification and rejoining processes. The proposed workflow allows the knowledge of worker to be translated into a classifier that automatically recognizes and removes areas of defects based on image capture. In addition, a real-time optimization algorithm in decision making is developed to assign a joining sequence of fragmented timber from a dynamic inventory, creating a single piece of targeted length with a significant reduction in material waste. In addition to an industrial application, this workflow also allows for future inventory-constrained customizable fabrication, for example in production of non-standard architectural components or adaptive reuse or defect-avoidance in out-of-grade timber construction.
series	cdrf
email
last changed	2022/09/29 07:51

_id	acadia20_272
id	acadia20_272
authors	del Campo, Matias; Carlson, Alexandra; Manninger, Sandra
year	2020
title	How Machines Learn to Plan
doi	https://doi.org/10.52842/conf.acadia.2020.1.272
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. 272-281.
summary	This paper strives to interrogate the abilities of machine vision techniques based on a family of deep neural networks, called generative adversarial neural networks (GANs), to devise alternative planning solutions. The basis for these processes is a large database of existing planning solutions. For the experimental setup of this paper, these plans were divided into two separate learning classes: Modern and Baroque. The proposed algorithmic technique leverages the large amount of structural and symbolic information that is inherent to the design of planning solutions throughout history to generate novel unseen plans. In this area of inquiry, aspects of culture such as creativity, agency, and authorship are discussed, as neural networks can conceive solutions currently alien to designers. These can range from alien morphologies to advanced programmatic solutions. This paper is primarily interested in interrogating the second existing but uncharted territory.
series	ACADIA
type	paper
email
last changed	2023/10/22 12:06

_id	sigradi2021_354
id	sigradi2021_354
authors	Ferreira, Julio César and Ferreira, Claudio Lima
year	2021
title	Emotion, Cognition, and The Practice of Teaching Architectural Design
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. 435–450
summary	From the view of concepts related to emotions and feelings treated in the field of cognitive - behavioral neuroscience and its relation with the teaching-learning processes, this paper searches to analyze educational strategies that can contribute to the field of emergency synchronous remote teaching architectural design. Methodologically, the bibliographical research of exploratory nature is related to an experience of investigation about pedagogical methods of teaching architectural design in a postgraduate course, developed, in the second semester of 2020, during the period of emergency synchronous remote teaching due to the SARS-Cov-2 coronavirus.We seek to comprehend the benefits and limits of remote emergency teaching practices of architectural design, looking at factors such as emotions and feelings as important mediation tools on teaching-learning processes.
keywords	Neurociencias, Fatores emocionais, Cogniçao em projeto, Ensino-aprendizado de projeto de arquitetura, Pensamento complexo.
series	SIGraDi
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last changed	2022/05/23 12:11

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