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	caadria2021_039
id	caadria2021_039
authors	Chen, Jielin, Stouffs, Rudi and Biljecki, Filip
year	2021
title	Hierarchical (multi-label) architectural image recognition and classification
doi	https://doi.org/10.52842/conf.caadria.2021.1.161
source	A. Globa, J. van Ameijde, A. Fingrut, N. Kim, T.T.S. Lo (eds.), PROJECTIONS - Proceedings of the 26th CAADRIA Conference - Volume 1, The Chinese University of Hong Kong and Online, Hong Kong, 29 March - 1 April 2021, pp. 161-170
summary	The task of architectural image recognition for both architectural functionality and style remains an open challenge. In addition, the paucity of well-organized, large-scale architectural image datasets with specific consideration for the domain of architectural design research has hindered the exploration of these challenging tasks. Drawing upon images from the professional architectural website ArchdailyÂ®, and leveraging state-of-the-art deep-learning-based classification models, we explore a hierarchical multi-label classification model as a potential baseline for the task of architectural image classification. The resulting model showcases the potential for innovative architectural discipline-related analyses and demonstrates some heuristic insights for visual feature extraction pertaining to both architectural functionality and architectural style.
keywords	image recognition; hierarchical classification; multi-label classification; architectural functionality; style
series	CAADRIA
email
last changed	2022/06/07 07:55

_id	caadria2021_382
id	caadria2021_382
authors	Heidari, Farahbod, Saleh Tabari, Mohammad Hassan, Mahdavinejad, Mohammadjavad, Werner, Liss C. and Roohabadi, Maryam
year	2021
title	Bio-Energy Management from Micro-Algae Bio-Computational Based Reactor
doi	https://doi.org/10.52842/conf.caadria.2021.1.401
source	A. Globa, J. van Ameijde, A. Fingrut, N. Kim, T.T.S. Lo (eds.), PROJECTIONS - Proceedings of the 26th CAADRIA Conference - Volume 1, The Chinese University of Hong Kong and Online, Hong Kong, 29 March - 1 April 2021, pp. 401-410
summary	Microalgae are a sustainable source of unique properties with potential for various applications. Biofuel production has led to the use of them as bioreactors on an architectural scale. Most of these efforts cannot manage the output due to the lack of intelligent control and monitoring over environmental micro-scale growth. This research presents the possibility of control and monitoring over the bio-energy retrieved through micro-organisms in bio-reactors, specifically the growth environments computation. To achieve monitoring, three dimensions of the medium culture captured by cameras, and with the advantage of image processing, the picture frames pixel values measured. In this process, we use the Python OpenCV Library as an image processing reference. Finally, a specifically developed algorithm analyses the calculated 3d-matrix. By changing the environmental parameters, control happens by directly recognizing changes in density and outputs. This researchs computational process has proposed a novel approach for controlling particle-based environments to reach the desired functions of microorganisms, This approach can use in a wide range of cases as a method.
keywords	Bio-Computation; Monitoring; Image Processing; Pattern Recognition; Multi-Functional Bio-Materials
series	CAADRIA
email
last changed	2022/06/07 07:49

_id	acadia21_48
id	acadia21_48
authors	Nahmad Vazquez, Alicia; Chen, Li
year	2021
title	Automated Generation of Custom Fit PPE Inserts
doi	https://doi.org/10.52842/conf.acadia.2021.048
source	ACADIA 2021: Realignments: Toward Critical Computation [Proceedings of the 41st Annual Conference of the Association of Computer Aided Design in Architecture (ACADIA) ISBN 979-8-986-08056-7]. Online and Global. 3-6 November 2021. edited by B. Bogosian, K. Dörfler, B. Farahi, J. Garcia del Castillo y López, J. Grant, V. Noel, S. Parascho, and J. Scott. 48-57.
summary	This research presents a machine learning-based interactive design method for the creation of customized inserts that improve the fit of the PPE 3M 1863 and 3M 8833 respiratory face masks. These two models are the most commonly used by doctors and professionals during the recent covid19 pandemic. The proper fit of the mask is crucial for their performance. Characteristics and fit of current leading market brands were analyzed to develop a parametric design software workflow that results in a 3D printed insert customized to specific facial features and the mask that will be used. The insert provides a perfect fit for the respirator mask. Statistical face meshes were generated from an anthropometric database, and 3D facial scans and photos were taken from 200 doctors and nurses on an NHS trust hospital. The software workflow can start from either a 2D image of the face (picture) or a 3D mesh taken from a scanning device. The platform uses machine learning and a parametric design workflow based on key performance facial parameters to output the insert between the face and the 3M masks. It also generates the 3d printing file, which can be processed onsite at the hospital. The 2D image approach and the 3D scan approach initializing the system were digitally compared, and the resultant inserts were physically tested by 20 frontline personnel in an NHS trust hospital. Finally, we demonstrate the criticality of proper fit on masks for doctors and nurses and the versatility of our approach augmenting an already tested product through customized digital design and fabrication.
series	ACADIA
type	paper
email
last changed	2023/10/22 12:06

_id	ecaade2021_038
id	ecaade2021_038
authors	Nakabayashi, Mizuki, Fukuda, Tomohiro and Yabuki, Nobuyoshi
year	2021
title	Mixed Reality Landscape Visualization Method with Automatic Discrimination Process for Dynamic Occlusion Handling Using Instance Segmentation
doi	https://doi.org/10.52842/conf.ecaade.2021.2.539
source	Stojakovic, V and Tepavcevic, B (eds.), Towards a new, configurable architecture - Proceedings of the 39th eCAADe Conference - Volume 2, University of Novi Sad, Novi Sad, Serbia, 8-10 September 2021, pp. 539-546
summary	Mixed reality (MR), which blends real and virtual worlds, has attracted attention as a visualization method in landscape design. MR-based landscape visualization enables stakeholders to examine landscape changes at actual scale in real-time at the actual project site. One challenge in MR-based landscape visualization is occlusion, which occurs when virtual objects obscure physical objects that are in the foreground. Previous research proposed an MR-based landscape visualization method with dynamic occlusion by using semantic segmentation of deep learning. However, this method has two problems. The first is that the same kind of objects that are grouped into one or overlapped types are classified as the same object, and the other is that the foreground objects have to be defined in pre-processing. In this study, we developed a system for large-scale MR landscape visualization that enables the recognition of each physical object individually using instance segmentation, and it is possible to accurately represent the positional relationship by comparing the coordinate information of the 3D virtual model and all physical objects.
keywords	landscape visualization; mixed reality; instance segmentation; dynamic occlusion handling; deep learning
series	eCAADe
email
last changed	2022/06/07 07:59

_id	ecaade2021_159
id	ecaade2021_159
authors	Yazicioglu, Gülin and Gürsel Dino, Ipek
year	2021
title	From Streetscape to Data - Semantic segmentation for the prediction of outdoor thermal comfort
doi	https://doi.org/10.52842/conf.ecaade.2021.1.555
source	Stojakovic, V and Tepavcevic, B (eds.), Towards a new, configurable architecture - Proceedings of the 39th eCAADe Conference - Volume 1, University of Novi Sad, Novi Sad, Serbia, 8-10 September 2021, pp. 555-562
summary	In recent years, the increasing pace of urbanization is expected to increase the temperatures in urban contexts and amplify the Urban Heat Island effect. This phenomenon has a negative impact on the urbanites' thermal comfort in outdoor spaces. Modeling and simulation-based approaches can precisely calculate outdoor thermal comfort; however, they are labor-intensive and high in computational cost. This difficulty might discourage decision-makers to consider outdoor thermal comfort conditions, which can affect their strategies at the beginning stage of design. This paper aims to propose a statistical model that can predict outdoor comfort using semantic segmentation of 2D street view images. Firstly, 78 panoramic street images of selected three streets in Istanbul are used to calculate the specific object classes that have an influence on outdoor temperature using semantic segmentation. Following, the streets' outdoor thermal comfort is calculated in Ladybug/Grasshopper. Lastly, two multi-variate regression models are built using the percentages of these object classes in each image and outdoor thermal comfort in given locations on the streets. Initial results show that the proposed regression models can predict UTCI with R2=0.78 and R2=0.80, indicating the semantic segmentation can support the calculation of outdoor comfort.
keywords	multivariate linear regression model; semantic segmentation; universal thermal climate index (UTCI)
series	eCAADe
email
last changed	2022/06/07 07:57

_id	acadia21_318
id	acadia21_318
authors	Borhani, Alireza; Kalantar, Negar
year	2021
title	Nesting Fabrication
doi	https://doi.org/10.52842/conf.acadia.2021.318
source	ACADIA 2021: Realignments: Toward Critical Computation [Proceedings of the 41st Annual Conference of the Association of Computer Aided Design in Architecture (ACADIA) ISBN 979-8-986-08056-7]. Online and Global. 3-6 November 2021. edited by B. Bogosian, K. Dörfler, B. Farahi, J. Garcia del Castillo y López, J. Grant, V. Noel, S. Parascho, and J. Scott. 318-327.
summary	Positioned at the intersection of the computational modes of design and production, this research explains the principles and applications of a novel fabrication-informed geometric system called nesting. Applying the nesting fabrication method, the authors reimage the construction of complex forms by proposing geometric arrangements that lessen material waste and reduce production time, transportation cost, and storage space requirements. Through this method, appearance and performance characteristics are contingent on fabrication constraints and material behavior. In this study, the focus is on developing design rules for this method and investigating the main parameters involved in dividing the global geometry of a complex volume into stackable components when the first component in the stack gives shape to the second. The authors introduce three different strategies for nesting fabrication: 2D, 2.5D, and 3D nesting. Which of these strategies can be used depends on the geometrical needs of the design and available tools and materials. Next, by revisiting different fabrication approaches, the authors introduce readers to the possibility of large-scale objects with considerable overhangs without the need for nearly any temporary support structures. After establishing a workflow starting with the identification of geometric rules of nesting and ending with fabrication limits, this work showcases the proposed workflow through a series of case studies, demonstrating the feasibility of the suggested method and its capacity to integrate production constraints into the design process. Traversing from pragmatic to geometrical concerns, the approach discussed here offers an integrated approach supporting functional, structural, and environmental matters important when turning material, technical, assembly, and transportation systems into geometric parameters.
series	ACADIA
type	paper
email
last changed	2023/10/22 12:06

_id	acadia21_444
id	acadia21_444
authors	Crawford, Assia
year	2021
title	Mitochondrial Matrix
doi	https://doi.org/10.52842/conf.acadia.2021.444
source	ACADIA 2021: Realignments: Toward Critical Computation [Proceedings of the 41st Annual Conference of the Association of Computer Aided Design in Architecture (ACADIA) ISBN 979-8-986-08056-7]. Online and Global. 3-6 November 2021. edited by B. Bogosian, K. Dörfler, B. Farahi, J. Garcia del Castillo y López, J. Grant, V. Noel, S. Parascho, and J. Scott. 444-453.
summary	The following project was created as part of an art residency with the Wellcome Centre for Mitochondrial Research (WCMR) at Newcastle University. The WCMR specializes in leading-edge research into mitochondrial disease, investigating causes, treatments, and ways of avoiding hereditary transmission. Mitochondria is believed to have started off as a separate species that through symbiosis came to be the powerhouse of each cell in our bodies (Hird 2009). Mitochondrial disease is a genetic disorder that is caused by genetic mutations of the DNA of the mitochondria or the cell that in turn affects the mitochondria (Bolano 2018). Mitochondria is a hereditary condition and can affect people at different stages in their lives. It can affect various organs and has a link to various types of conditions. Therefore, the patient experience is unique to each individual and the elusive nature of the condition can make it particularly challenging due to the complexity of the disorder as well as the inaccessible scale on which these variations occur (Chinnery 2014)
series	ACADIA
type	project
email
last changed	2023/10/22 12:06

_id	ijac202119106
id	ijac202119106
authors	Del Campo, Matias; Alexandra Carlson, and Sandra Manninger
year	2021
title	Towards Hallucinating Machines - Designing with Computational Vision
source	International Journal of Architectural Computing 2021, Vol. 19 - no. 1, 88–103
summary	There are particular similarities in how machines learn about the nature of their environment, and how humans learn to process visual stimuli. Machine Learning (ML), more specifically Deep Neural network algorithms rely on expansive image databases and various training methods (supervised, unsupervised) to “make sense” out of the content of an image. Take for example how students of architecture learn to differentiate various architectural styles. Whether this be to differentiate between Gothic, Baroque or Modern Architecture, students are exposed to hundreds, or even thousands of images of the respective styles, while being trained by faculty to be able to differentiate between those styles. A reversal of the process, striving to produce imagery, instead of reading it and understanding its content, allows machine vision techniques to be utilized as a design methodology that profoundly interrogates aspects of agency and authorship in the presence of Artificial Intelligence in architecture design. This notion forms part of a larger conversation on the nature of human ingenuity operating within a posthuman design ecology. The inherent ability of Neural Networks to process large databases opens up the opportunity to sift through the enormous repositories of imagery generated by the architecture discipline through the ages in order to find novel and bespoke solutions to architectural problems. This article strives to demystify the romantic idea of individual artistic design choices in architecture by providing a glimpse under the hood of the inner workings of Neural Network processes, and thus the extent of their ability to inform architectural design.The approach takes cues from the language and methods employed by experts in Deep Learning such as Hallucinations, Dreaming, Style Transfer and Vision. The presented approach is the base for an in-depth exploration of its meaning as a cultural technique within the discipline. Culture in the extent of this article pertains to ideas such as the differentiation between symbolic and material cultures, in which symbols are defined as the common denominator of a specific group of people.1 The understanding and exchange of symbolic values is inherently connected to language and code, which ultimately form the ingrained texture of any form of coded environment, including the coded structure of Neural Networks.A first proof of concept project was devised by the authors in the form of the Robot Garden. What makes the Robot Garden a distinctively novel project is the motion from a purely two dimensional approach to designing with the aid of Neural Networks, to the exploration of 2D to 3D Neural Style Transfer methods in the design process.
keywords	Artificial intelligence, design agency, neural networks, machine learning, machine vision
series	journal
email
last changed	2021/06/03 23:29

_id	cdrf2021_275
id	cdrf2021_275
authors	E. Özdemir, L. Kiesewetter, K. Antorveza, T. Cheng, S. Leder, D. Wood, and A. Menges
year	2021
title	Towards Self-shaping Metamaterial Shells: A Computational Design Workflow for Hybrid Additive Manufacturing of Architectural Scale Double-Curved Structures
doi	https://doi.org/https://doi.org/10.1007/978-981-16-5983-6_26
source	Proceedings of the 2021 DigitalFUTURES The 3rd International Conference on Computational Design and Robotic Fabrication (CDRF 2021)
summary	Double curvature enables elegant and material-efficient shell structures, but their construction typically relies on heavy machining, manual labor, and the additional use of material wasted as one-off formwork. Using a material’s intrinsic properties for self-shaping is an energy and resource-efficient solution to this problem. This research presents a fabrication approach for self-shaping double-curved shell structures combining the hygroscopic shape-changing and scalability of wood actuators with the tunability of 3D-printed metamaterial patterning. Using hybrid robotic fabrication, components are additively manufactured flat and self-shape to a pre-programmed configuration through drying. A computational design workflow including a lattice and shell-based finite element model was developed for the design of the metamaterial pattern, actuator layout, and shape prediction. The workflow was tested through physical prototypes at centimeter and meter scales. The results show an architectural scale proof of concept for self-shaping double-curved shell structures as a resource-efficient physical form generation method.
series	cdrf
email
last changed	2022/09/29 07:53

_id	ecaaderis2023_30
id	ecaaderis2023_30
authors	Fiuza, Rebeca, Barcelos, Letícia and Cardoso, Daniel
year	2023
title	COVID-19 and the City: An Analysis of the Correlation between Urban and Social Factors and COVID-19 in Fortaleza, Brazil
source	De Luca, F, Lykouras, I and Wurzer, G (eds.), Proceedings of the 9th eCAADe Regional International Symposium, TalTech, 15 - 16 June 2023, pp. 45–52
summary	The COVID-19 pandemic has been the biggest sanitary crisis humanity has ever faced, the virus has contaminated 662.717.929 people worldwide and killed 6.701.270 people. However, these numbers were not distributed equally at international, national or urban scale. In Fortaleza, Brazil, city studied in this paper, data from 2021 and 2022 epidemiologic reports suggest a contamination pattern that starts in neighborhoods with higher Human Development Index (HDI) and then goes to lower HDI neighborhoods, however, throughout all of this cycle, low HDI neighborhoods tend to have a higher lethality rate. These facts raised the hypothesis that those neighborhoods have specific urban and social factors that affect the capacity to respond and prevent COVID-19. The main objective of this paper is to identify the correlation of some urban and social factors with COVID-19 data. To achieve that, the authors selected seven variables (access to water rate, literacy rate, waste collection rate, population density, access to electric energy rate, sanitation rate and average monthly income) to correlate with four COVID- 19 indicators (total number of cases, total number of deaths, contamination rate and lethality rate). For this, it was chosen to apply Spearman’s correlation coefficient and for the calculation the statistical software Jamovi was used. The results show that the literacy rate, the access to electric energy rate and average monthly income have a positive correlation with the contamination rate, however these same variables have a negative correlation with the lethality rate.
keywords	COVID-19, Urban Factors, Spearman's Coefficient Correlation, Public Health
series	eCAADe
email
last changed	2024/02/05 14:28

_id	acadia21_212
id	acadia21_212
authors	Gillespie, David; Qin, Zehao; Aish, Francis
year	2021
title	An Extended Reality Collaborative Design System
doi	https://doi.org/10.52842/conf.acadia.2021.212
source	ACADIA 2021: Realignments: Toward Critical Computation [Proceedings of the 41st Annual Conference of the Association of Computer Aided Design in Architecture (ACADIA) ISBN 979-8-986-08056-7]. Online and Global. 3-6 November 2021. edited by B. Bogosian, K. Dörfler, B. Farahi, J. Garcia del Castillo y López, J. Grant, V. Noel, S. Parascho, and J. Scott. 212-221.
summary	This paper presents a new system that enables an eXtended Reality (XR) collaborative design review process, by augmenting an existing physical mockup or environment with virtual models at 1:1 scale in-situ. By using this new hybrid approach, existing context can be extended with minimal or no base physical structure through a simulated VR/AR environment to facilitate stakeholder design collaboration in a manner that was previously either cost prohibitive or technically unfeasible. Through combining real and virtual in this way, the sense of realism can be enhanced, increasing engagement and participation in the design process. An approach to apply AR/VR to uncontrolled environments is described, allowing it to overcome challenges such as tracking and mapping, and allowing users to walk around freely in-situ. Two examples are presented where the system has been used in live project environments, one as a design tool for client review and engagement, and the other as part of a public planning process.
series	ACADIA
type	paper
email
last changed	2023/10/22 12:06

_id	caadria2023_446
id	caadria2023_446
authors	Guida, George
year	2023
title	Multimodal Architecture: Applications of Language in a Machine Learning Aided Design Process
doi	https://doi.org/10.52842/conf.caadria.2023.2.561
source	Immanuel Koh, Dagmar Reinhardt, Mohammed Makki, Mona Khakhar, Nic Bao (eds.), HUMAN-CENTRIC - Proceedings of the 28th CAADRIA Conference, Ahmedabad, 18-24 March 2023, pp. 561–570
summary	Recent advances in Natural Language Processing (NLP) and Diffusion Models (DMs) are leading to a significant change in the way architecture is conceived. With capabilities that surpass those of current generative models, it is now possible to produce an unlimited number of high-quality images (Dhariwal and Nichol 2021). This opens up new opportunities for using synthetic images and marks a new phase in the creation of multimodal 3D forms, central to architectural concept design stages. Presented here are three methodologies of generation of meaningful 2D and 3D designs, merging text-to-image diffusion models Stable Diffusion, and DALL-E 2 with computational methods. These allow designers to intuitively navigate through a multimodal feedback loop of information originating from language and aided by artificial intelligence tools. This paper contributes to our understanding of machine-augmented design processes and the importance of intuitive user interfaces (UI) in enabling new dialogues between humans and machines. Through the creation of a prototype of an accessible UI, this exchange of information can empower designers, build trust in these tools, and increase control over the design process.
keywords	Machine Learning, Diffusion Models, Concept Design, Semantics, User Interface, Design Agency
series	CAADRIA
email
last changed	2023/06/15 23:14

_id	ijac202119201
id	ijac202119201
authors	Gumuskaya, Gizem
year	2021
title	Multimaterial bioprinting—minus the printer: Synthetic bacterial patterning with UV-responsive genetic circuits
source	International Journal of Architectural Computing 2021, Vol. 19 - no. 2, 121–141
summary	In this paper, we argue that synthetic biology can help us employ living systems’ unique capacity for self-construction and biomaterial production toward developing novel architectural fabrication paradigms, in which both the raw material production and its refinement into a target structure can be merged into a single computational process embedded in the living structure itself. To demonstrate, here we introduce bioPheme, a novel biofabrication method for engineering bacteria to build biomaterial(s) of designer’s choice into arbitrary 2D geometries specified via transient UV tracing. To this end, we present the design, construction, and testing of the enabling synthetic DNA circuit, which, once inserted into a bacterial colony, allows the bacteria to execute spatial computation by interacting with one another based on the if-then rules encoded in this circuit. At the heart of this genetic circuit is a pair of UV sensor – actuator, and a pair of cell-to-cell signal transmitter – receptor modules, created with genes extracted from the virus ? Phage and marine bacterium Vibrio fischeri, respectively. These modules are wired together to help designers engineer bacteria to build macro-scale structures with seamlessly integrated biomaterials, thereby bridge the molecular and architectural scales. In this way, a bacterial lawn can be programmed to produce different objects with complementary biomaterial compositions, such as a biomineralized superstructure and an elastic tissue filling in-between. In summary, this paper focuses on how scientists’ increasing ability to harness the innate computational capacity of living cells can help designers create self-constructing structures for architectural biofabrication. Through the discussions in this paper, we aim to initiate a shift in today’s biodesign practices toward a greater appreciation and adoption of bottom-up governance of living structures. We are confident that such a paradigm shift will allow for more efficient and sustainable biofabrication systems in the 4th industrial revolution and beyond.
keywords	Synthetic biology, architecture, optogenetics, design computation, genetic circuits, biofabrication, synthetic morphogenesis, computational fabrication, architectural fabrication, biodesign
series	journal
email
last changed	2024/04/17 14:29

_id	caadria2021_130
id	caadria2021_130
authors	Han, Yoojin and Lee, Hyunsoo
year	2021
title	Exploring the Key Attributes of Lifestyle Hotels: A Content Analysis of User-Created Content on Instagram
doi	https://doi.org/10.52842/conf.caadria.2021.1.071
source	A. Globa, J. van Ameijde, A. Fingrut, N. Kim, T.T.S. Lo (eds.), PROJECTIONS - Proceedings of the 26th CAADRIA Conference - Volume 1, The Chinese University of Hong Kong and Online, Hong Kong, 29 March - 1 April 2021, pp. 71-80
summary	This study aims to investigate the key attributes of lifestyle hotels by analyzing user-created content on Instagram, an image-based social network service. In an era of uncertainty in the tourism and hospitality industry, it is inevitable that hotels must create a competitive identity. However, even with the significant growth of the lifestyle hotel segment, the concept of a lifestyle hotel is still vague. Therefore, to explore how to define, perceive, and interpret lifestyle hotels and to suggest their crucial attributes, this paper examines user-created content on Instagram. The data from 20,886 Instagram posts related to lifestyle hotels, including 2,209 locations, 43,586 hashtags, and 20,866 images, were analyzed using Vision AI, a social network analysis method and computer vision technology. The results of this study demonstrated that lifestyle hotels are perceived as design-focused branded hotels that represent the urban lifestyle and share both vacation and urban activities. Furthermore, the results reflected one of the latest hospitality trends-a holiday in an urban setting in addition to the primary purpose of traveling. Finally, this research suggests broader uses of big data and deep learning for analyzing how a place is consumed in a geospatial context.
keywords	Lifestyle Hotel; Hospitality Experiences; User-Created Content; Social Network Analysis; Vision AI
series	CAADRIA
email
last changed	2022/06/07 07:50

_id	caadria2021_117
id	caadria2021_117
authors	Ikeno, Kazunosuke, Fukuda, Tomohiro and Yabuki, Nobuyoshi
year	2021
title	Can a Generative Adversarial Network Remove Thin Clouds in Aerial Photographs? - Toward Improving the Accuracy of Generating Horizontal Building Mask Images for Deep Learning in Urban Planning and Design
doi	https://doi.org/10.52842/conf.caadria.2021.2.377
source	A. Globa, J. van Ameijde, A. Fingrut, N. Kim, T.T.S. Lo (eds.), PROJECTIONS - Proceedings of the 26th CAADRIA Conference - Volume 2, The Chinese University of Hong Kong and Online, Hong Kong, 29 March - 1 April 2021, pp. 377-386
summary	Information extracted from aerial photographs is widely used in the fields of urban planning and architecture. An effective method for detecting buildings in aerial photographs is to use deep learning to understand the current state of a target region. However, the building mask images used to train the deep learning model must be manually generated in many cases. To overcome this challenge, a method has been proposed for automatically generating mask images by using textured 3D virtual models with aerial photographs. Some aerial photographs include thin clouds, which degrade image quality. In this research, the thin clouds in these aerial photographs are removed by using a generative adversarial network, which leads to improvements in training accuracy. Therefore, the objective of this research is to propose a method for automatically generating building mask images by using 3D virtual models with textured aerial photographs to enable the removable of thin clouds so that the image can be used for deep learning. A model trained on datasets generated by the proposed method was able to detect buildings in aerial photographs with an accuracy of IoU = 0.651.
keywords	Urban planning and design; Deep learning; Generative Adversarial Network (GAN); Semantic segmentation; Mask image
series	CAADRIA
email
last changed	2022/06/07 07:50

_id	acadia21_454
id	acadia21_454
authors	Kaiser, Kimball; Aljomairi, Maryam
year	2021
title	DTS Printer: Spatial Inkjet Printing
doi	https://doi.org/10.52842/conf.acadia.2021.454
source	ACADIA 2021: Realignments: Toward Critical Computation [Proceedings of the 41st Annual Conference of the Association of Computer Aided Design in Architecture (ACADIA) ISBN 979-8-986-08056-7]. Online and Global. 3-6 November 2021. edited by S. Parascho, J. Scott, and K. Dörfler. 454-459.
summary	Inkjet printing has become abundantly available to businesses, offi ces, and households ever since its commercialization in the late 1980s. Although roughly forty years have passed, the desktop printer is still limited to printing on thin fl at surfaces, mainly paper (Mills 1998). On the other hand, while larger fl atbed printing technology does offer printing on a wide-range of substrates of various thicknesses, it is limited to 2-axis printing and is mainly used for large scale commercial applications due to high machine costs. Motivated by the ambition of printing on irregular surfaces of varied mediums, improving upon high price points of existing fl at-bed printing machines, and contributing to the public knowledge of distributed manufacturing, the Direct-To-Substrate (DTS) printer is an exploration into an integrated z-axis within inkjet printing. To realign a familiar technology used by many and hack it for the purposes of expanded capabilities, the DTS allows a user to manufacture a three-dimensional artifact and later print graphics directly upon said geometry using the same machine. To remain as accessible as possible, the DTS printer is a computer-numerically-controlled desktop machine made from common, sourceable hardware parts with a tool-changeable end effector, that currently accepts a Dremel tool as a router, and a hacked inkjet cartridge
series	ACADIA
type	project
email
last changed	2023/10/22 12:06

_id	acadia21_222
id	acadia21_222
authors	Lok, Leslie; Samaniego, Asbiel; Spencer, Lawson
year	2021
title	Timber De-Standardized
doi	https://doi.org/10.52842/conf.acadia.2021.222
source	ACADIA 2021: Realignments: Toward Critical Computation [Proceedings of the 41st Annual Conference of the Association of Computer Aided Design in Architecture (ACADIA) ISBN 979-8-986-08056-7]. Online and Global. 3-6 November 2021. edited by B. Bogosian, K. Dörfler, B. Farahi, J. Garcia del Castillo y López, J. Grant, V. Noel, S. Parascho, and J. Scott. 222-231.
summary	Timber De-Standardized is a framework that salvages irregular and regular shaped tree logs by utilizing a mixed reality (MR) interface for the design, fabrication, and assembly of a structurally viable tree log assembly. The process engages users through a direct, hands-on design approach to iteratively modify and design irregular geometry at full scale within an immersive MR environment without altering the original material. A digital archive of 3D scanned logs are the building elements from which users, designing in the MR environment, can digitally harvest (though slicing) and place the elements into a digitally constructed whole. The constructed whole is structurally analyzed and optimized through recursive feedback loops to preserve the user’s predetermined design. This iterative toggling between the physical and virtual emancipates the use of irregular tree log structures while informing and prioritizing the user’s design intent. To test this approach, a scaled prototype was developed and fabricated in MR. By creating a framework that links a holographic digital design to a physical catalog of material, the interactive workflow provides greater design agency to users as co-creators in processing material parts. This participation enables users to have a direct impact on the design of discretized tree logs that would otherwise have been discarded in standardized manufacturing. This paper presents an approach in which complex tree log structures can be made without the use of robotic fabrication tools. This workflow opens new opportunities for design in which users can freely configure structures with non-standardized elements within an intuitive MR environment.
series	ACADIA
type	paper
email
last changed	2023/10/22 12:06

_id	caadria2021_027
id	caadria2021_027
authors	Lu, Ming, Zhou, Yifan, Wang, Xiang and Yuan, Philip F.
year	2021
title	An optimization method for large-scale 3D printing - Generate external axis motion using Fourier series
doi	https://doi.org/10.52842/conf.caadria.2021.1.683
source	A. Globa, J. van Ameijde, A. Fingrut, N. Kim, T.T.S. Lo (eds.), PROJECTIONS - Proceedings of the 26th CAADRIA Conference - Volume 1, The Chinese University of Hong Kong and Online, Hong Kong, 29 March - 1 April 2021, pp. 683-692
summary	With the increase in labor costs, more and more robot constructions appear in building construction and spatial structure fabrication. There are many robots working on large-scale objects. When the reach range of the robot cannot meet the requirements, so an external axis is needed. The external axis is usually a linear motion device, which can significantly increase the operating range of the robotic arm. In actual construction, it is also widely used. This article introduces a 3d printing coffee bar project. Because this project is of a large scale and needs to be printed at one time, the XYZ external axis was used in this project to complete the task. Inspired by this project, this article study several methods of optimizing the motion of external axes in large-scale construction. Finally, we chose to use the Fourier series as the most suitable method to optimize the printing path and programed this method as a component of FUROBOT for more convenient use. This article explains the principle of this method in detail. Finally, this article uses a 3D printing example to illustrate the precautions in actual use.
keywords	robotics; motion optimize; Fourier series; 3D printing; external axis
series	CAADRIA
email
last changed	2022/06/07 07:59

_id	sigradi2021_11
id	sigradi2021_11
authors	Mela, Débora, Carmo Pena Martinez, Andressa and Henrique Lima Zuin, Affonso
year	2021
title	Leaf Coverage Quantification for the Design of Vegetated Shading Geometries Using Algorithmic Modeling, Coupled with Imaging Software
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. 1463–1473
summary	One of the most significant parameters for obtaining positive benefits from vegetation is the leaf area index. This parameter influences the shading of the plant, acting as a solar control device in the architecture. In this sense, this work aims to collect average parameters of the percentage of leaf cover of climbing species, in a high tropical climate, through digital mapping and pixel counting, using the image software ImageJ for digital image processing and analysis. With these parameters, it will be possible to simulate the shading of the vines and predict their growth. This simulation can help designers make decisions such as mesh configurations, planting spacing, and regular maintenance. The research hopes to fill a gap in the literature on specific data on leaf cover of climbing species, which can serve as an input to the algorithmic modeling of green facades in architecture.
keywords	Digital image processing, Algorithmic design, Green shading devices, Leaf area index, Pixel counting.
series	SIGraDi
email
last changed	2022/05/23 12:11

_id	ecaade2021_291
id	ecaade2021_291
authors	Mondal, Joy
year	2021
title	Differences between Architects' and Non-architects' Visual Perception of Originality of Tower Typology - Quantification of subjective evaluation using Deep Learning
doi	https://doi.org/10.52842/conf.ecaade.2021.1.065
source	Stojakovic, V and Tepavcevic, B (eds.), Towards a new, configurable architecture - Proceedings of the 39th eCAADe Conference - Volume 1, University of Novi Sad, Novi Sad, Serbia, 8-10 September 2021, pp. 65-74
summary	The paper presents a computational methodology to quantify the differences in visual perception of originality of the rotating tower typology between architects and non-architects. A parametric definition of the Absolute Tower Building D with twelve variables is used to generate 250 design variants. Subsequently, sixty architects and sixty non-architects were asked to rate the design variants, in comparison to the original design, on a Likert scale of 'Plagiarised' to 'Original'. With the crowd-sourced evaluation data, two neural networks - one each for architects and non-architects - were trained to predict the originality score of 15,000 design variants. The results indicate that architects are more lenient at seeing design variants as original. The average originality score by architects is 27.74% higher than the average originality score by non-architects. Compared to a non-architect, an architect is 1.93 times likelier to see a design variant as original. In 92.01% of the cases, architects' originality score is higher than non-architects'. The methodology can be used to capture and predict any subjective opinion.
keywords	Originality; Visual perception; Crowd-sourced; Subjective evaluation; Deep learning; Neural network
series	eCAADe
email
last changed	2022/06/07 07:58

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