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	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	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	sigradi2021_114
id	sigradi2021_114
authors	Cesar Rodrigues, Ricardo, Kenzo Imagawa, Marcelo, Rubio Koga, Renan and Bertola Duarte, Rovenir
year	2021
title	Big Data vs Smart Data on the Generation of Floor Plans with Deep Learning
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. 217–228
summary	Due to the progressive growth of data dimensionality, addressing how much data and time is required to train deep learning models has become an important research topic. Thus, in this paper, we present a benchmark for generating floor plans with Conditional Generative Adversarial Networks in which we compare 10 trained models on a dataset of 80.000 samples, the models use different data dimensions and hyper-parameters on the training phase, beyond this objective, we also tested the capability of Convolutional Neural Networks (CNN) to reduce the dataset noise. The models' assessment was made on more than 6 million with the Frétche Inception Distance (FID). The results show that such models can rapidly achieve similar or even better FID results if trained with 800 images of 512x512 pixels, in comparison to high dimensional datasets of 256x256 pixels, however, using CNNs to enhance data consistency reproduced optimal results using around 27.000 images.
keywords	Floor plans, Generative design, Generative adversarial networks, Smart Data, Dataset reduction.
series	SIGraDi
email
last changed	2022/05/23 12:10

_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	acadia21_112
id	acadia21_112
authors	Kahraman, Ridvan; Zechmeister, Christoph; Dong, Zhetao; Oguz, Ozgur S.; Drachenberg, Kurt; Menges, Achim; Rinderspacher, Katja
year	2021
title	Augmenting Design
doi	https://doi.org/10.52842/conf.acadia.2021.112
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. 112-121.
summary	In recent years, generative machine learning methods such as variational autoencoders (VAEs) and generative adversarial networks (GANs) have opened up new avenues of exploration for architects and designers. The presented work explores how these methods can be expanded by incorporating multiple abstract criteria directly into the formulation of the algorithm that negotiates these complex criteria and proposes a fitting design. It draws inspiration from the works of several design theorists who have developed such goal-oriented approaches to design, and sets up multiple-objective VAE and GAN frameworks with this idea in mind. The research demonstrates that by incorporating multiple constraints using auxiliary discriminator networks, the developed algorithms are able to generate innovative solutions to two example problems: the design of 2D digits, and the design of 3D voxel chairs. By speculating and examining the role of the designer in data based generative computational design workflows, the research aims to provide an approach for solving design tasks in the age of big data.
series	ACADIA
type	paper
email
last changed	2023/10/22 12:06

_id	caadria2021_220
id	caadria2021_220
authors	MacDonald, Katie and Schumann, Kyle
year	2021
title	Twinned Assemblage - Curating and Distilling Digital Doppelgangers
doi	https://doi.org/10.52842/conf.caadria.2021.1.693
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. 693-702
summary	Recent developments in digital fabrication have made increasingly intelligent use of machine visioning and 3D scanning. These technologies enable ever-higher resolution digital models of physical material, and present opportunities for physical material to gain agency in the design process. Digital design workflows using such technologies require ever-greater computing power as the resolution of digitized models increases, and high-fidelity 3D scanning systems become cost-prohibitive, creating obstacles to widespread use. Twinned assemblage uses consumer-grade photogrammetry software, lowering the cost of equipment required, and presents a series of distillation methods that strategically reduce the fidelity of data digitally describing a physical object. Distillation methods discussed include reducing a mesh to a low-poly geometry, identifying the location and orientation of an object's largest faces, and creating 2D sections, among others. These methods can be designed intentionally to extract or highlight certain qualities in digital models, that in turn inform aggregation strategies generated through computational simulation. This paper presents several examples of such aggregations in a variety of materials, conveying benefits and challenges of the process. Such methods present opportunities for granting agency to physical materials in the design process, and for the democratized use of digitizing technologies.
keywords	Authorship; Digitizing; Material Agency; Digital Design; Democratized Technology
series	CAADRIA
email
last changed	2022/06/07 07:59

_id	caadria2021_053
id	caadria2021_053
authors	Rhee, Jinmo and Veloso, Pedro
year	2021
title	Generative Design of Urban Fabrics Using Deep Learning
doi	https://doi.org/10.52842/conf.caadria.2021.1.031
source	A. Globa, J. van Ameijde, A. Fingrut, N. Kim, T.T.S. Lo (eds.), PROJECTIONS - Proceedings of the 26th CAADRIA Conference - Volume 1, The Chinese University of Hong Kong and Online, Hong Kong, 29 March - 1 April 2021, pp. 31-40
summary	This paper describes the Urban Structure Synthesizer (USS), a research prototype based on deep learning that generates diagrams of morphologically consistent urban fabrics from context-rich urban datasets. This work is part of a larger research on computational analysis of the relationship between urban context and morphology. USS relies on a data collection method that extracts GIS data and converts it to diagrams with context information (Rhee et al., 2019). The resulting dataset with context-rich diagrams is used to train a Wasserstein GAN (WGAN) model, which learns how to synthesize novel urban fabric diagrams with the morphological and contextual qualities present in the dataset. The model is also trained with a random vector in the input, which is later used to enable parametric control and variation for the urban fabric diagram. Finally, the resulting diagrams are translated to 3D geometric entities using computer vision techniques and geometric modeling. The diagrams generated by USS suggest that a learning-based method can be an alternative to methods that rely on experts to build rule sets or parametric models to grasp the morphological qualities of the urban fabric.
keywords	Deep Learning; Urban Fabric; Generative Design; Artificial Intelligence; Urban Morphology
series	CAADRIA
email
last changed	2022/06/07 07:56

_id	acadia21_246
id	acadia21_246
authors	Safley, Nick
year	2021
title	Reconnecting...
doi	https://doi.org/10.52842/conf.acadia.2021.246
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. 246-255.
summary	This design research reimagines the architectural detail in a postdigital framework and proposes digital methods to work upon discrete tectonics. Drawing upon Marco Frascari's writing The Tell-the-Tale Detail, the study aims to reimagine tectonic thinking for focused attention after the digital turn. Today, computational tools are powerful enough to perform operations more similar to physical tools than in the earlier digital era. These tools create a "digital materiality," where architects can manipulate digital information in parallel and overlapping ways to physical corollaries. (Abrons and Fure, 2018) To date, work in this area has focused on materiality specifically. This project reinterprets tectonics using texture map editing and point cloud information, particularly reconceptualizing jointing using images. Smartphone-based 3D digital scanning was used to captured details from a series of Carlo Scarpa's influential works, isolating these details from their physical sites and focusing attention upon individual tectonic moments. As digital scans, these details problematize the rhetoric of smoothness and seamlessness prevalent in digital architecture as they are discretely construed loci yet composed of digital meshes. (Jones 2014) Once removed from their contexts, reconnecting the digital scans into compositions of "compound details" necessitated a series of new mechanisms for constructing and construing not native to the material world. Using Photoshop editing of texture-mapped images, digital texturing of meshes, and interpretation of the initial material constructions, new joints within and between these the digital scanned details were created to reframe the original detail for the post-digital.
series	ACADIA
type	paper
email
last changed	2023/10/22 12:06

_id	ecaade2023_259
id	ecaade2023_259
authors	Sonne-Frederiksen, Povl Filip, Larsen, Niels Martin and Buthke, Jan
year	2023
title	Point Cloud Segmentation for Building Reuse - Construction of digital twins in early phase building reuse projects
doi	https://doi.org/10.52842/conf.ecaade.2023.2.327
source	Dokonal, W, Hirschberg, U and Wurzer, G (eds.), Digital Design Reconsidered - Proceedings of the 41st Conference on Education and Research in Computer Aided Architectural Design in Europe (eCAADe 2023) - Volume 2, Graz, 20-22 September 2023, pp. 327–336
summary	Point cloud processing has come a long way in the past years. Advances in computer vision (CV) and machine learning (ML) have enabled its automated recognition and processing. However, few of those developments have made it through to the Architecture, Engineering and Construction (AEC) industry. Here, optimizing those workflows can reduce time spent on early-phase projects, which otherwise could be spent on developing innovative design solutions. Simplifying the processing of building point cloud scans makes it more accessible and therefore, usable for design, planning and decision-making. Furthermore, automated processing can also ensure that point clouds are processed consistently and accurately, reducing the potential for human error. This work is part of a larger effort to optimize early-phase design processes to promote the reuse of vacant buildings. It focuses on technical solutions to automate the reconstruction of point clouds into a digital twin as a simplified solid 3D element model. In this paper, various ML approaches, among others KPConv Thomas et al. (2019), ShapeConv Cao et al. (2021) and Mask-RCNN He et al. (2017), are compared in their ability to apply semantic as well as instance segmentation to point clouds. Further it relies on the S3DIS Armeni et al. (2017), NYU v2 Silberman et al. (2012) and Matterport Ramakrishnan et al. (2021) data sets for training. Here, the authors aim to establish a workflow that reduces the effort for users to process their point clouds and obtain object-based models. The findings of this research show that although pure point cloud-based ML models enable a greater degree of flexibility, they incur a high computational cost. We found, that using RGB-D images for classifications and segmentation simplifies the complexity of the ML model but leads to additional requirements for the data set. These can be mitigated in the initial process of capturing the building or by extracting the depth data from the point cloud.
keywords	Point Clouds, Machine Learning, Segmentation, Reuse, Digital Twins
series	eCAADe
email
last changed	2023/12/10 10:49

_id	caadria2021_196
id	caadria2021_196
authors	Lu, Yueheng, Tian, Runjia, Li, Ao, Wang, Xiaoshi and Jose Luis, Garcia del Castillo Lopez
year	2021
title	CubiGraph5K - Organizational Graph Generation for Structured Architectural Floor Plan Dataset
doi	https://doi.org/10.52842/conf.caadria.2021.1.081
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. 81-90
summary	In this paper, a novel synthetic workflow is presented for procedural generation of room relation graphs of floor plans from structured architectural datasets. Different from classical floor plan generation models, which are based on strong heuristics or low-level pixel operations, our method relies on parsing vectorized floor plans to generate their intended organizational graph for further graph-based deep learning. This research work presents the schema for the organizational graphs, describes the generation algorithms, and analyzes its time/space complexity. As a demonstration, a new dataset called CubiGraph5K is presented. This dataset is a collection of graph representations generated by the proposed algorithms, using the floor plans in the popular CubiCasa5K dataset as inputs. The aim of this contribution is to provide a matching dataset that could be used to train neural networks on enhanced floor plan parsing, analysis and generation in future research.
keywords	Graph Theory; Algorithm; Architecture Design Dataset; Organizational Graph
series	CAADRIA
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	ijac202119101
id	ijac202119101
authors	Budig, Michael; Oliver Heckmann, Markus, Hudert, Amanda Qi Boon Ng, Zack Xuereb Conti, and Clement Jun Hao Lork
year	2021
title	Computational screening-LCA tools for early design stages
source	International Journal of Architectural Computing 2021, Vol. 19 - no. 1, 6–22
summary	Life Cycle Assessment (LCA) has been widely adopted to identify the Global Warming Potential (GWP) in the construction industry and determine its high environmental impact through Greenhouse Gas (GHG) emissions, energy and resource consumptions. The consideration of LCA in the early stages of design is becoming increasingly important as a means to avoid costly changes at later stages of the project. However, typical LCA-based tools demand very detailed information about structural and material systems and thus become too laborious for designers in the conceptual stages, where such specifications are still loosely defined. In response, this paper presents a workflow for LCA-based evaluation where the selection of the construction system and material is kept open to compare the impacts of alternative design variants. We achieve this through a strict division into support and infill systems and a simplified visualization of a schematic floor layout using a shoebox approach, inspired from the energy modelling domain. The shoeboxes in our case are repeatable modules within a schematic floor plan layout, whose enclosures are defined by parametric 2D surfaces representing total ratios of permanent supports versus infill components. Thus, the assembly of modular surface enclosures simplifies the LCA evaluation process by avoiding the need to accurately specify the physical properties of each building component across the floor plan. The presented workflow facilitates the selection of alternative structural systems and materials for their comparison, and outputs the Global Warming Potential (GWP) in the form of an intuitive visualization output. The workflow for simplified evaluation is illustrated through a case study that compares the GWP for selected combinations of material choice and construction systems.
keywords	Computational life cycle assessment tool, embodied carbon, parametric design, construction systems, global warming potential
series	journal
email
last changed	2021/06/03 23:29

_id	ascaad2021_069
id	ascaad2021_069
authors	Cheddadi, Aqil; Kensuke Hotta, Yasushi Ikeda
year	2021
title	Exploring the Self-Organizing Structure of the Moroccan Medina: A Simulation Model for Generating Urban Form
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. 672-685
summary	This research explores the use of generative design and computational simulations in the exploration of urban compositions based on traditional urban forms from North Africa. Upon the examination of these urban settlements, we discuss the relationship between traditional urban form and generative urbanism theory. We investigate several factors that allow these self-generated urban tissues to be highly adaptive to social, spatial, and environmental change. Following this, we formulate guidelines to reinterpret some of the characteristics of these urban forms. Built on these features, the simulation seeks to explore the generation of abstract urban forms and their optimization. In this regard, this experiment utilizes 3D and parametric design tools (Rhinoceros 3D and Grasshopper) to define a generative urban simulation and optimization model. It explores the use of algorithmic design methodology in the definition and optimization of the generated urban form. For this purpose, grid-based operations with base modules are used in conjunction with introverted urban blocks. We employ evolutionary algorithms and Pareto front methodology to visualize and rank a multitude of optimized results that are evaluated using three different and conflicting design objectives: sun exposure, physical accessibility, and urban density. The results are ranked and analyzed by comparing the outcomes of these different objective functions. The result of this study shows that it is possible to allow a degree of diversification of a myriad of urban configurations with a generative form-finding algorithm while still maintaining a rather commendable adaptability to various design constraints in the case of high-density settings. In this research, it is anticipated that an algorithmic design model is a fitting contemporary solution that can simulate the philosophy of a design made without a designer and offer a wide range of objective-based spatial solutions. It sets the stage for a discussion about the relevance of reinterpreting traditional urban forms from north Africa by designing a generative model that allows for self-organization.
series	ASCAAD
email
last changed	2021/08/09 13:13

_id	sigradi2021_29
id	sigradi2021_29
authors	Delgado, Maria and Collins, Jeffrey
year	2021
title	Otavalo Textile Grammar: Patterns and Dialogues
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. 669–683
summary	This paper focuses on the woven textiles of Otavalo, Ecuador, as a case study for improved cultural representation in architectural design. A shape grammar methodology is used to identify specific geometry and elucidate relationship rules found in existing artifacts. These geometry and relationships are subsequently used to produce patterns; both replicas of traditional tapestries as well as new configurations. Extending from 2D to 3D and from digital to physical, sets of modular prototypes are developed based on patterns produced using the defined Otavalo Textile Grammar. Model parts are supplied to study participants; new building blocks for architecture as a spatial and social undertaking.
keywords	maker culture, design computation, shape grammars, digital craft
series	SIGraDi
email
last changed	2022/05/23 12:11

_id	sigradi2021_130
id	sigradi2021_130
authors	Hiilesmaa, Laura, Galbes Breda de Lima, Eduardo, Chieppe Carvalho, Leonardo, Wenzel Martins, Gisele and Vizioli, Simone Helena Tanoue
year	2021
title	Heritage Education: Computational Design of the Virtual Exhibition at the Cultural and Scientific Divulgation Center of USP
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. 605–616
summary	During the current pandemic situation, in 2020/2021, there has been an increased need for easier remote access to cultural and heritage sites, especially on users’ smartphones and personal devices. The exhibition from the 40 years anniversary of the Cultural and Scientific Divulgation Center (CDCC) of the University of Sao Paulo (USP) was selected in order to accomplish the fundamental objectives of this study. The transition of its contents to digital media was enabled by three main technologies: 360° panoramic images, used broadly in the virtual tour; close-range photogrammetry for the creation of 3D models of objects, such as the bust of Dante Alighieri; and informative GIFs of the Transparent Woman of Dresden. As a result of the methodology proposed, this paper introduces a link with the virtual tour developed, presenting an important resource to spread a multidisciplinary knowledge about this meaningful built heritage of Sao Carlos (SP).
keywords	Fotogrametria, Imagens Panorâmicas 360°, Educaçao Patrimonial, Patrimônios Materiais, Tour Virtual 360°.
series	SIGraDi
email
last changed	2022/05/23 12:11

_id	ecaade2021_037
id	ecaade2021_037
authors	Kikuchi, Takuya, Fukuda, Tomohiro and Yabuki, Nobuyoshi
year	2021
title	Automatic Diminished Reality-Based Virtual Demolition Method using Semantic Segmentation and Generative Adversarial Network for Landscape Assessment
doi	https://doi.org/10.52842/conf.ecaade.2021.2.529
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. 529-538
summary	In redevelopment projects in mature cities, it is important to visualize the future landscape. Diminished reality (DR) based methods have been proposed to represent the future landscape after the structures are removed. However, two issues remain to be addressed in previous studies. (1) the user needs to prepare 3D models of the structure to be removed and the background structure to be rendered after removal as preprocessing, and (2) the user needs to specify the structure to be removed in advance. In this study, we propose a DR method that detects the objects to be removed using semantic segmentation and completes the removal area using generative adversarial networks. With this method, virtual removal can be performed without preparing 3D models in advance and without specifying the removal target in advance. A prototype system was used for verification, and it was confirmed that the method can represent the future landscape after removal and can run at an average speed of about 8.75 fps.
keywords	landscape visualization; virtual demolition; diminished reality (DR); deep learning; generative adversarial network (GAN); semantic segmentation
series	eCAADe
email
last changed	2022/06/07 07:52

_id	caadria2023_395
id	caadria2023_395
authors	Luo, Jiaxiang, Mastrokalou, Efthymia, Aldaboos, Sarah and Aldabous, Rahaf
year	2023
title	Research on the Exploration of Sprayed Clay Material and Modeling System
doi	https://doi.org/10.52842/conf.caadria.2023.2.231
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. 231–240
summary	As a traditional building material, clay has been used by humans for a long time. From early civilisations, to the modern dependence on new technologies, the craft of clay making is commonly linked with the use of moulds, handmade creations, ceramic extruders, etc. (Schmandt and Besserat, 1977). Clay in the form of bricks is one of the oldest building materials known (Fernandes et al, 2010). This research expands the possibilities offered by standardised bricks by testing types of clay, forms, shapes, porosity, and structural methods. The traditional way of working with clay relies on human craftsmanship and is based on the use of semi-solid clay (Fernandes et al., 2010). However, there is little research on the use of clay slurry. With the rise of 3D printing systems in recent years, research and development has been emerging on using clay as a 3D printing filament (Gürsoy, 2018). Researchers have discovered that in order for 3D-printed clay slurry to solidify quickly to support the weight of the added layers during printing, curing agents such as lime, coal ash, cement, etc. have to be added to the clay slurry. After adding these substances, clay is difficult to be reused and can have a negative effect on the environment (Chen et al., 2021). In this study, a unique method for manufacturing clay elements of intricate geometries is proposed with the help of an internal skeleton that can be continuously reused. The study introduces the process of applying clay on a special structure through spraying and showcases how this method creates various opportunities for customisation of production.
keywords	Spray clay, Substructure, 3D printing, Modelling system, Reusable
series	CAADRIA
email
last changed	2023/06/15 23:14

_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	caadria2021_262
id	caadria2021_262
authors	Olthof, Owen, Globa, Anastasia and Stracchi, Paolo
year	2021
title	SISTEMA NERVI - Sustainable Production of Optimised Floor Slabs Through Digital Fabrication
doi	https://doi.org/10.52842/conf.caadria.2021.1.723
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. 723-732
summary	'Sistema Nervi' (the Nervi System) invented by Pier Luigi Nervi greatly economised the production of complex concrete forms optimised in both material usage and structurally. However it did not translate well into other contexts due to labour and material considerations (Leslie, 2018). This paper explores novel methodologies of producing optimised floor slabs and concrete structures, using digital fabrication techniques, focusing on both labour economisation and sustainability principles. A module from the Australia Square lobby slab has been used as the set geometry and was reproduced using differing techniques of fabrication for a comparative study. The study was conducted at scale (1:20). The viability for production at full scale (1:1) for manufacturing is discussed. The assessment criteria for the tests are divided into four categories: Cost, Time, Performance, and Sustainability. 3D printing of PLA plastic and ceramic clay extrusion printing has been used to produce removable or degradable formworks. These technologies have been selected due to their current market availability and associated costs. This study hopes to introduce improved methodologies for producing optimized concrete forms, as well as the sustainability potentials of a degradable formwork such as ceramic clay. Both systems were ultimately able to produce workable formworks for optimised shapes and showed promise for reducing labour involved as well as presenting with material sustainability for discussion.
keywords	Concrete formwork; Sustainability; Degradable formwork; Optimised concrete; Advanced fabrication
series	CAADRIA
email
last changed	2022/06/07 08:00

_id	ascaad2021_127
id	ascaad2021_127
authors	Poustinchi, Ebrahim
year	2021
title	A Grasshopper Plug-In for Designing Virtual Camera Path in Rhino 3D using Cellphone Motion: Chameleon
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. 636-644
summary	Chameleon is a workflow plug-in for Grasshopper 3D that enables designers/users to design camera paths and orientation for animation and still rendering, using cellphone position and orientation. Working as a bridge between the physical world and the digital design environment of Rhino 3D, users using Chameleons, can develop animated and still frames from the first-person point of view with realistic walk-through motions/angles. Although this feature is available in animation software platforms, Chameleon aims to unlock this possibility in Rhino 3D environment and the most used design software for three-dimensional modeling. This new workflow also provokes new means and methods for creative interaction with design software, beyond the existing hardware interfaces such as keyboard and mouse.
series	ASCAAD
email
last changed	2021/08/09 13:13

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