Cumincad : CUMINCAD Papers : Search Results

CumInCAD is a Cumulative Index about publications in Computer Aided Architectural Design
supported by the sibling associations ACADIA, CAADRIA, eCAADe, SIGraDi, ASCAAD and CAAD futures

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_id	acadia20_594
id	acadia20_594
authors	Farahbakhsh, Mehdi; Kalantar, Negar; Rybkowski, Zofia
year	2020
title	Impact of Robotic 3D Printing Process Parameters on Bond Strength
doi	https://doi.org/10.52842/conf.acadia.2020.1.594
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. 594-603.
summary	Additive manufacturing (AM), also known as 3D printing, offers advantages over traditional construction technologies, increasing material efficiency, fabrication precision, and speed. However, many AM projects in academia and industrial institutions do not comply with building codes. Consequently, they are not considered safe structures for public utilization and have languished as exhibition prototypes. While three discrete scales—micro, mezzo, and macro—are investigated for AM with paste in this paper, structural integrity has been tackled on the mezzo scale to investigate the impact of process parameters on the bond strength between layers in an AM process. Real-world material deposition in a robotic-assisted AM process is subject to environmental factors such as temperature, humidity, the load of upper layers, the pressure of the nozzle on printed layers, etc. Those factors add a secondary geometric characteristic to the printed objects that was missing in the initial digital model. This paper introduces a heuristic workflow for investigating the impacts of three selective process parameters on the bond strength between layers of paste in the robotic-assisted AM of large-scale structures. The workflow includes a method for adding the secondary geometrical characteristic to the initial 3D model by employing X-ray computerized tomography (CT) scanning, digital image processing, and 3D reconstruction. Ultimately, the proposed workflow offers a pattern library that can be used by an architect or artificial intelligence (AI) algorithms in automated AM processes to create robust architectural forms.
series	ACADIA
type	paper
email
last changed	2023/10/22 12:06

_id	acadia20_720
id	acadia20_720
authors	Farahi, Behnaz
year	2020
title	Can the subaltern speak?
doi	https://doi.org/10.52842/conf.acadia.2020.1.720
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. 720-729.
summary	How could design be used as a method of interrogation for addressing larger cultural, social, or political issues? How could we explore the possibility of using emerging technologies such as robotics and artificial intelligence in order to subvert the status quo? The project presented in this paper is inspired by the historical masks, known as Niqab, worn by the Bandari women from southern Iran. It has been said that these masks were developed during Portuguese colonial rule as a way to protect the wearer from the gaze of slave masters looking for pretty women. In this project two robotic masks seemingly begin to develop their own language to communicate with each other, blinking their eyelashes in rapid succession, using Morse code generated by artificial intelligence (AI). The project draws on a Facebook experiment where two AI bots began to develop their own language. It also draws on an incident when an American soldier used his eyes to blink the word “TORTURE” using Morse code during his captivity in Vietnam, and stories of women using code to report domestic abuse during the COVID-19 lockdown. Here the “wink” of the sexual predator is subverted into a language to protect women from the advances of a predator. Through the lens of the design methodology that is referred to as “critical making,” this project bridges AI, interactive design, and critical thinking. Moreover, while most feminist discourse takes a Eurocentric view, this project addresses feminism from a non-Western perspective.
series	ACADIA
type	paper
email
last changed	2023/10/22 12:06

_id	caadria2020_045
id	caadria2020_045
authors	Zheng, Hao and Ren, Yue
year	2020
title	Machine Learning Neural Networks Construction and Analysis in Vectorized Design Drawings
doi	https://doi.org/10.52842/conf.caadria.2020.2.707
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. 707-716
summary	Machine Learning, a recently prevalent research domain in data prediction and analysis, has been widely used in a variety of fields. In the design field, especially for architectural design, a machine learning method to learn and generate design data as pixelized images has been developed in previous researches. However, proceeding pixelized image data will cause the problems of precision loss and calculation waste, since the geometric architectural design data is efficiently stored and presented as vectorized CAD files. Thus, in this article, the author developed a specific machine learning neural network to learn and predict design drawings as vectorized data, speeding up the learning and predicting process, while improving the accuracy. First, two necessary geometric tests have been successfully done, which shows the central concept of neural network construct. Then, a design rule prediction model was built to demonstrate the methods to optimize the neural network and data structure. Lastly, a generation model based on human-made design data was constructed, which can be used to predict and generate the bedroom furniture positions by inputting the boundary data of the room, door, and window.
keywords	Machine Learning; Artificial Intelligence; Generative Design; Geometric Design
series	CAADRIA
email
last changed	2022/06/07 07:57

_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	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	acadia20_110
id	acadia20_110
authors	Zhang, Mengni; Dewey, Clara; Kalantari, Saleh
year	2020
title	Dynamic Anthropometric Modeling Interface
doi	https://doi.org/10.52842/conf.acadia.2020.1.110
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. 110-119.
summary	In this paper, we propose a Kinect-based Dynamic Anthropometric Modeling Interface (DAMI), built in Rhinoceros with Grasshopper for patient room layout optimization and nurse posture evaluations. Anthropometry is an important field that studies human body measurements to help designers improve product ergonomics and reduce negative health consequences such as musculoskeletal disorders (MSDs). Unlike existing anthropometric tools, which rely on generic human body datasets and static posture models, DAMI tracks and records user postures in real time, creating custom 3D body movement models that are typically absent in current space-planning practices. A generic hospital patient room, which contains complex and ergonomically demanding activities for nurses, was selected as an initial testing environment. We will explain the project background, the methods used to develop DAMI, and demonstrate its capabilities. There are two main goals DAMI aims to achieve. First, as a generative tool, it will reconstruct dynamic body point cloud models, which will be used as input for optimizing room layout during a project’s schematic design phase. Second, as an evaluation tool, by encoding and visualizing the Rapid Entire Body Assessment (REBA) scores, DAMI will illustrate the spatiotemporal relationship between nurse postures and the built environment during a project’s construction phase or post occupancy evaluation. We envision a distributed system of Kinect sensors to be embedded in various hospital rooms to help architects, planners, and facility managers improve nurse work experiences through better space planning.
series	ACADIA
type	paper
email
last changed	2023/10/22 12:06

_id	ecaade2020_227
id	ecaade2020_227
authors	Bielski, Jessica, Langenhan, Christoph, Weyand, Babara, Neuber, Markus, Eisenstadt, Viktor and Althoff, Klaus-Dieter
year	2020
title	Topological Queries and Analysis of School Buildings Based on Building Information Modeling (BIM) Using Parametric Design Tools and Visual Programming to Develop New Building Typologies
doi	https://doi.org/10.52842/conf.ecaade.2020.2.279
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. 279-288
summary	School buildings are currently one of the largest portions of planning and building projects in Germany. In order to reflect the continuous developments in school building construction with constantly changing spatial requirements, an approach to analyse, derive and combine patterns of schools is proposed to adapt school typologies accordingly. Therefore, the topology is analysed, concerning interconnection methods, such as adjacency, accessibility, depth, and flow. The geometric analysis of e.g. room sizes or spatial proportions is enhanced by including grouping of rooms, estimated room clusters, or room shapes. Furthermore, text-matching is used to determine e.g. room program fulfilment, or assigning functional room descriptions to predefined room types, revealing huge differences of terms throughout time and architects. First results of the analyses show a relevant correlation between spatial proportion and room types.
keywords	school building typologies; building information modeling (BIM); artificial intelligence (AI); topology; spatial analysis; digital semantic model
series	eCAADe
email
last changed	2022/06/07 07:52

_id	ecaade2020_017
id	ecaade2020_017
authors	Chan, Yick Hin Edwin and Spaeth, A. Benjamin
year	2020
title	Architectural Visualisation with Conditional Generative Adversarial Networks (cGAN). - What machines read in architectural sketches.
doi	https://doi.org/10.52842/conf.ecaade.2020.2.299
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. 299-308
summary	As a form of visual reasoning, sketching is a human cognitive activity instrumental to architectural design. In the process of sketching, abstract sketches invoke new mental imageries and subsequently lead to new sketches. This iterative transformation is repeated until the final design emerges. Artificial Intelligence and Deep Neural Networks have been developed to imitate human cognitive processes. Amongst these networks, the Conditional Generative Adversarial Network (cGAN) has been developed for image-to-image translation and is able to generate realistic images from abstract sketches. To mimic the cyclic process of abstracting and imaging in architectural concept design, a Cyclic-cGAN that consists of two cGANs is proposed in this paper. The first cGAN transforms sketches to images, while the second from images to sketches. The training of the Cyclic-cGAN is presented and its performance illustrated by using two sketches from well-known architects, and two from architecture students. The results show that the proposed Cyclic-cGAN can emulate architects' mode of visual reasoning through sketching. This novel approach of utilising deep neural networks may open the door for further development of Artificial Intelligence in assisting architects in conceptual design.
keywords	visual cognition; design computation; machine learning; artificial intelligence
series	eCAADe
email
last changed	2022/06/07 07:55

_id	acadia20_406
id	acadia20_406
authors	Duong, Eric; Vercoe, Garrett; Baharlou, Ehsan
year	2020
title	Engelbart
doi	https://doi.org/10.52842/conf.acadia.2020.1.406
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. 406-415.
summary	The internet has long been viewed as a cyberspace of free and collective information, allowing for an increase in the diversity of ideas and viewpoints available to the general public. However, critics argue that the emergence of personalization algorithms on social media and other internet platforms instead reduces information diversity by forming “filter bubbles"" of viewpoints similar to the user’s own. The adoption of these personalization algorithms is due in part to advancements in natural language processing, which allow for textual analysis at unprecedented scales. This paper aims to utilize natural language processing and architectural spatial principles to present social media from a collective viewpoint rather than a personalized one. To accomplish this, the paper introduces Engelbart, a data-driven agent-based system, where real-time Twitter conversations are visualized within a two-dimensional environment. This environment is interacted with by the artificial intelligence (AI) agent, Engelbart, which summarizes crowdsourced thoughts and feelings about current trending topics. The functionality of this web application comes from the natural language processing of thousands of tweets per minute throughout several layers of operations, including sentiment analysis and word embeddings. Presented as an understandable interface, it incorporates the values of cybernetics, cyberspace, agent-based modeling, and data ethics to show the potential for social media to become a more transparent space for collective discussion.
series	ACADIA
type	paper
email
last changed	2023/10/22 12:06

_id	caadria2020_016
id	caadria2020_016
authors	Gardner, Nicole, Meng, Leo Lin and Haeusler, M. Hank
year	2020
title	Computational Pragmatism - Computational design as pragmatist tools for the age of the Anthropocene
doi	https://doi.org/10.52842/conf.caadria.2020.2.487
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. 487-496
summary	The age of Anthropocene describes a geological epoch wherein human action is recognised as a global-scale geophysical force that is reaping devastating consequences for the natural environment. What the Anthropocene and pragmatist thinking share is an understanding of the coevolution of life and the planet (in pragmatism's terminology human-environment relations) through a deeply systemic view. This paper outlines how core methods and theories currently engaged under the rubric of computational design can also be understood to align to key tenets of pragmatism. In so doing, the question this raises is how more recent advancements in computation that include so-called Artificial Intelligence (AI) applications in design might operationalise distributed, shared, and significantly, interactional notions of systemic agency? The argument put forward here is that a neo-pragmatist perspective of computational design must fundamentally engage AI as the age of the Anthropocene necessitates a relinquishing of the privileged view of human-only agency and control over systems towards a more dynamic and interactional model.
keywords	Computational Design; Pragmatism; Artificial Intelligence; Anthropocene
series	CAADRIA
email
last changed	2022/06/07 07:51

_id	sigradi2020_683
id	sigradi2020_683
authors	Granero, Adriana Edith; Piegari, Ricardo Gustavo
year	2020
title	How does AI affect higher design education? An investigation to open the debate
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. 683-688
summary	This research tries to open the debate about the possibility offered by Artificial Intelligence. Is there a possibility that AI will help us teach in the Architecture career? Can the student have an AI tutor? The architect's learning is carried out through University Education, which is a complex system of: physical spaces, duration and organization of studies, financing, diplomas and degrees, teaching staff and methods, population or applicants, admission requirements. How does AI affect University Education? Will it generate more opportunities? We proposed an experience with AI and images to evaluate this convergent culture.
keywords	Digital Image, Knowledge and Image Generation, Artificial Intelligence, Algorithmic Images, Generative Images
series	SIGraDi
email
last changed	2021/07/16 11:52

_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	acadia20_698
id	acadia20_698
authors	Kimm, Geoff; Burry, Mark
year	2020
title	Steering into the Skid
doi	https://doi.org/10.52842/conf.acadia.2020.1.698
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. 698-707.
summary	What if any perceived risks of lost authorship and artistic control posed by a wholesale embrace of artificial intelligence by the architectural profession were instead opportunities? AI’s potential to automate design has been pursued for over 50 years, yet aspirations of early researchers are not fully realized. Nonetheless, AI’s advances continue to be rapid; it is an increasingly viable adjunct to architectural practice, and there are fundamental reasons for why the perceived “risks” of AI cannot be dismissed lightly. Architects’ professional role at the intersection of social issues and technology, however, may allow them to avoid the obsolescence faced by other roles. To do this, we propose architects responsively arbitrage an ever-changing gap between maturing AI and mutable social expectations— arbitrage in the sense of seeking to exercise individual judgment to negotiate between diverse considerations and capacities for mutual advantage. Rather than feel threatened, evolving architectural practice can augment an expanded design process to generate and embed new subtleties and expectations that society may judge contemporary AI alone as being unable to achieve. Although there can be no road map to the future of AI in architecture, historical misevaluations of machines and our own human capabilities inhibit the intertwined, synergistic, and symbiotic union with AI needed to avoid a zero-sum confrontation. To act myopically, defensively, or not at all risks straitjacketing future definitions of what it means to be an architect, designer, or even a professionally unaligned creative and productive human being.
series	ACADIA
type	paper
email
last changed	2023/10/22 12:06

_id	caadria2020_163
id	caadria2020_163
authors	Koh, Immanuel
year	2020
title	The Augmented Museum - A Machinic Experience with Deep Learning
doi	https://doi.org/10.52842/conf.caadria.2020.2.639
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. 639-648
summary	Today we witness a shift in the role with which museum used to play -- from one that was simply a spatial container filled with physical artworks on display, to one that is now layered with the digital/online version of the artworks themselves. Deep learning algorithms have become an important means to process such large datasets of digital artworks in providing an alternative curatorial practice (biased/unbiased), and consequentially, augmenting the navigation of the museum's physical spaces. In collaboration with a selection of museums, a series of web/mobile applications have been made to investigate the potential of such machinic inference, as well as interference of the physical experience.
keywords	Machine Learning; Deep Learning; Experience Design; Artificial Intelligence
series	CAADRIA
email
last changed	2022/06/07 07:51

_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_130
id	acadia20_130
authors	Newton, David
year	2020
title	Anxious Landscapes
doi	https://doi.org/10.52842/conf.acadia.2020.2.130
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. 130-137.
summary	Advances in the field of machine learning over the last decade have revolutionized artificial intelligence by providing a flexible means to build analytic, predictive, and generative models from large datasets, but the allied design disciplines have yet to apply these tools at the urban level to draw analytic insights on how the built environment might impact human health. Previous research has found numerous correlations between the built environment and both physical and mental health outcomes—suggesting that the design of our cities may have significant impacts on human health. Developing methods of analysis that can provide insight on the correlations between the built environment and human health could help the allied design disciplines shape our cities in ways that promote human health. This research addresses these issues and contributes knowledge on the use of deep learning (DL) methods for urban analysis and mental health, specifically anxiety. Mental health disorders, such as anxiety, have been estimated to account for the largest proportion of global disease burden. The methods presented allow architects, planners, and urban designers to make use of large remote-sensing datasets (e.g., satellite and aerial images) for design workflows involving analysis and generative design tasks. The research also contributes insight on correlations between anxiety prevalence and specific urban design features—providing actionable intelligence for the planning and design of the urban fabric.
series	ACADIA
type	paper
email
last changed	2023/10/22 12:06

_id	ecaade2020_138
id	ecaade2020_138
authors	Patel, Sayjel Vijay, Tchakerian, Raffi, Lemos Morais, Renata, Zhang, Jie and Cropper, Simon
year	2020
title	The Emoting City - Designing feeling and artificial empathy in mediated environments
doi	https://doi.org/10.52842/conf.ecaade.2020.2.261
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. 261-270
summary	This paper presents a theoretical blueprint for implementing artificial empathy into the built environment. Transdisciplinary design principles have oriented the creation of a new model for autonomous environments integrating psychology, architecture, digital media, affective computing and interactive UX design. 'The Emoting City', an interactive installation presented at the 2019 Shenzhen Bi-City Biennale of Urbanism/Architecture, is presented as a first step to explore how to engage AI-driven sensing by integrating human perception, cognition and behaviour in a real-world scenario. The approach described encompasses two main elements: embedded cyberception and responsive surfaces. Its human-AI interface enables new modes of blended interaction that are conducive to self-empathy and insight. It brings forth a new proposition for the development of sensing systems that go beyond social robotics into the field of artificial empathy. The installation innovates in the design of seamless affective computing that combines 'alloplastic' and 'autoplastic' architectures. We believe that our research signals the emergence of a potential revolution in responsive environments, offering a glimpse into the possibility of designing intelligent spaces with the ability to sense, inform and respond to human emotional states in ways that promote personal, cultural and social evolution.
keywords	Artificial Intelligence; Responsive Architecture; Affective Computation; Human-AI Interfaces; Artificial Empathy
series	eCAADe
email
last changed	2022/06/07 07:59

_id	caadria2020_091
id	caadria2020_091
authors	Ren, Yue and Zheng, Hao
year	2020
title	The Spire of AI - Voxel-based 3D Neural Style Transfer
doi	https://doi.org/10.52842/conf.caadria.2020.2.619
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. 619-628
summary	In the architecture field, humans have mastered various skills for creating unique spatial experiences with unknown interplays between known contents and styles. Meanwhile, machine learning, as a popular tool for mapping different input factors and generating unpredictable outputs, links the similarity of the machine intelligence with the typical form-finding process. Style Transfer, therefore, is widely used in 2D visuals for mixing styles while inspiring the architecture field with new form-finding possibilities. Researchers have applied the algorithm in generating 2D renderings of buildings, limiting the results in 2D pixels rather than real full volume forms. Therefore, this paper aims to develop a voxel-based form generation methodology to extend the 3D architectural application of Style Transfer. Briefly, through cutting the original 3D model into multiple plans and apply them to the 2D style image, the stylized 2D results generated by Style Transfer are then abstracted and filtered as groups of pixel points in space. By adjusting the feature parameters with user customization and replacing pixel points with basic voxelization units, designers can easily recreate the original 3D geometries into different design styles, which proposes an intelligent way of finding new and inspiring 3D forms.
keywords	Form Finding; Machine Learning; Artificial Intelligence; Style Transfer
series	CAADRIA
email
last changed	2022/06/07 07:56

_id	sigradi2023_234
id	sigradi2023_234
authors	Santos, Ítalo, Andrade, Max, Zanchettin, Cleber and Rolim, Adriana
year	2023
title	Machine learning applied in the evaluation of airport projects in Brazil based on BIM models
source	García Amen, F, Goni Fitipaldo, A L and Armagno Gentile, Á (eds.), Accelerated Landscapes - Proceedings of the XXVII International Conference of the Ibero-American Society of Digital Graphics (SIGraDi 2023), Punta del Este, Maldonado, Uruguay, 29 November - 1 December 2023, pp. 875–887
summary	In a country with continental dimensions like Brazil, air transport plays a strategic role in the development of the country. In recent years, initiatives have been promoted to boost the development of air transport, among which the BIM BR strategy stands out, instituted by decree n-9.983 (2019), decree n-10.306 (2020) and more recently, the publication of the airport design manual (SAC, 2021). In this context, this work presents partial results of a doctoral research based on the Design Science Research (DSR) method for the application of Machine Learning (ML) techniques in the Artificial Intelligence (AI) subarea, aiming to support SAC airport project analysts in the phase of project evaluation. Based on a set of training and test data corresponding to airport projects, two ML algorithms were trained. Preliminary results indicate that the use of ML algorithms enables a new scenario to be explored by teams of airport design analysts in Brazil.
keywords	Airports, Artificial intelligence, BIM, Evaluation, Machine learning.
series	SIGraDi
email
last changed	2024/03/08 14:07

_id	caadria2020_054
id	caadria2020_054
authors	Shen, Jiaqi, Liu, Chuan, Ren, Yue and Zheng, Hao
year	2020
title	Machine Learning Assisted Urban Filling
doi	https://doi.org/10.52842/conf.caadria.2020.2.679
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. 679-688
summary	When drawing urban scale plans, designers should always define the position and the shape of each building. This process usually costs much time in the early design stage when the condition of a city has not been finally determined. Thus the designers spend a lot of time working forward and backward drawing sketches for different characteristics of cities. 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 urban design plans, helping designers automatically generate the predicted details of buildings configuration with a given condition of cities. Through the machine learning of image pairs, the result shows the relationship between the site conditions (roads, green lands, and rivers) and the configuration of buildings. This automatic design tool can help release the heavy load of urban designers in the early design stage, quickly providing a preview of design solutions for urban design tasks. The analysis of different machine learning models trained by the data from different cities inspires urban designers with design strategies and features in distinct conditions.
keywords	Artificial Intelligence; Urban Design; Generative Adversarial Networks; Machine Learning
series	CAADRIA
email
last changed	2022/06/07 07:56

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