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	sigradi2021_146
id	sigradi2021_146
authors	Yönder, Veli Mustafa, Dogan, Fehmi and Çavka, Hasan Burak
year	2021
title	Deciphering and Forecasting Characteristics of Bodrum Houses Using Artificial Intelligence (AI) Approaches
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. 241–252
summary	Computer vision (CV), artificial intelligence (AI), machine learning (ML), and deep learning (DL) applications, which are among the rapidly emerging and growing technologies, have the potential to be effectively used in the fields of architecture and construction. These applications are used not only in the field of architectural design development and construction site tracking but also to analyze and predict the architectural properties of existing buildings and heritage classification. This paper aims to classify and analyze the façades of Bodrum houses by using deep learning models, comprehensive relational database (RDB), and artificial neural network based clustering methods. Through the use of the above-mentioned methods, we managed to cluster Bodrum houses' façade attributes in five groups and testing image classification models in three different classifiers.
keywords	Image processing, Deep learning (DL), Classification, Hierarchical cluster analysis, Artificial neural networks (ANNs)
series	SIGraDi
email
last changed	2022/05/23 12:10

_id	ijac202119308
id	ijac202119308
authors	Dinçer, Sevde Gülizar; Yazar, Tugrul
year	2021
title	A comparative analysis of the digital re-constructions of muqarnas systems: The case study of Sultanhani muqarnas in Central Anatolia
source	International Journal of Architectural Computing 2021, Vol. 19 - no. 3, 360–385
summary	This paper presents a comparative case study on the digital modeling workflows of a particular muqarnas system. After the literature review and the definition of the context, several digital modeling workflows were described as element-based, tessellation-based and block-based workflows by using computer-aided design and parametric modeling software. As the case study of this research, these workflows were tested on a muqarnas design located at the Sultanhani Caravanserai in Central Anatolia. Then, workflows were compared according to three qualities: analytical, generative, and performative. The outcomes of element-based workflow has more analytical solutions for the study, where tessellation-based workflow has more generative potential and block-based workflow is more performative.
keywords	Anatolian Seljuk muqarnas, digital modeling, parametric modeling, architectural geometry, Sultanhani Caravanserai
series	journal
email
last changed	2024/04/17 14:29

_id	sigradi2021_121
id	sigradi2021_121
authors	Galbes Breda de Lima, Eduardo, Ferreira Peppe, Francisco, Cangussu Lima, Lucas Ítalo and Vizioli, Simone Helena Tanoue
year	2021
title	Comparative Study between 2D and 3D Digital Freehand Drawing Applied to the Architectural Design Process
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. 1065–1076
summary	This paper aims to discuss representative subjects, such as freehand drawing and its operational tools, which allow 2D sketching through graphic drawing tables, whereas virtual reality head-mounted displays are used for 3D sketches. A pilot project exercise was carried out in the building of the Centro de Divulgaçao Científica e Cultural (CDCC-USP), in Sao Carlos (SP - Brazil). As a result, this research presents a comparative chart that investigates the potential uses of these technologies in architecture teaching and in the act of designing. Moreover, this scenario includes the 360? image technology that presents itself as a vehicle of immersion and apprehension of space, having as a backdrop the unfoldings of social isolation arising from COVID - 19.
keywords	Processo projetivo, Fotografia 360°, Percepçao, Desenho digital, Croqui tridimensional.
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	caadria2021_445
id	caadria2021_445
authors	Noel, Vernelle A. A., Nikookar, Niloofar, Pye, Jamieson, Tran, Phuong 'Karen' and Laudeman, Sara
year	2021
title	The Infinite Line Active Bending Pavilion: Culture,Craft and Computation
doi	https://doi.org/10.52842/conf.caadria.2021.1.351
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. 351-360
summary	Active bending projects today employ highly specialized, complex computer software and machines for design, simulation, and materialization. At times, these projects lack a sensitivity to cultures limited in high-tech infrastructures but rich in low-tech knowledges. Situated Computations is an approach to computational design that grounds it in the social world by acknowledging historical, cultural, and material contexts of design and making, as well as the social and political structures that drive them. In this article, we ask, how can a Situated Computations approach to contemporary active bending broaden the design space and uplift low-tech cultural practices? To answer this question, we design and build "The Infinite Line"- an active bending pavilion that draws on the history, material practices, and knowledges in design in the Trinidad Carnival - for the 2019 International Association for Shell and Spatial Structures (IASS) exhibition in Barcelona, Spain. We conclude that Situated Computations provide an opportunity to integrate local knowledges, histories, design practices, and material behaviors as drivers in active bending approaches, so that structure, material practices, and cultural settings are considered concurrently.
keywords	Situated Computations; craft; wire-bending; active bending structures; Trinidad Carnival; dancing sculptures
series	CAADRIA
email
last changed	2022/06/07 07:58

_id	ecaade2021_208
id	ecaade2021_208
authors	Rodríguez Hernández, José Luis, Cortes Perez, Juan Pedro, Gradisar, Luka and Figueiredo, Bruno
year	2021
title	Structural Grid Predesign using Generative Design for Residential Building with Steel Structure on BIM Models - Structural grid predesign using generative design
doi	https://doi.org/10.52842/conf.ecaade.2021.2.059
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. 59-66
summary	Due to the more significant architectural complexity, it is helpful to include structural analysis studies in the early design stages. An architectural team typically carries out the structural grid fit in this phase. This limitation may lead to the structural distribution in the initial phase not being the most appropriate. This work aims to provide a tool for architects oriented design by optimising the cost of the structure, making an initial layout for residential buildings with the regular shape of steel structures using the generative design, which allows the creation of structural BIM models that comply with the requirements of stability and resistance for gravity design specified in the American code ASCE 360 as starting point on the conceptual design. The paper describes the computational design development for the structural building grid using multi-criteria optimisation solved by a genetic algorithm.
keywords	Generative Design; Building Information Modelling (BIM); Structural Predesign; Structural Grid; Multi-Objective Optimisation
series	eCAADe
email
last changed	2022/06/07 07:56

_id	sigradi2021_167
id	sigradi2021_167
authors	Vereza, Carolina Gaspar, Boner da Silva, Gabriel, Milhm, Julio de Oliveira and Leitao de Souza, Thiago
year	2021
title	The 360° Immersive Atmospheric Perspective: Interpretation and Creation of Circular Pictorial Layers of the Panorama of Rio de Janeiro by Victor Meirelles and Henri Langerock
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. 915–925
summary	This essay is part of an ongoing research entitled “The 360? immersive: investigation, representation and digital immersion of Rio de Janeiro city during 19th and 20th centuries”, developed in Phd Program in Urbanism, FAU-UFRJ, which analyses the aerial perspective of “The Panorama of Rio de Janeiro, by Victor Meirelles and Henri Langerock”, aiming at its digital reconstitution for 360? immersion experiences. To achieve this goal, digital and analog systems of representations will be developed and applied, including: computer graphics techniques, pictorial layers, 3D models, 3D renderings, 3D animations and videos in 360? format.
keywords	Panorama, Realidade Virtual, História da cidade, experiencia imersiva em 360°, Panorama do Rio de Janeiro, engines de jogos.
series	SIGraDi
email
last changed	2022/05/23 12:11

_id	cdrf2021_148
id	cdrf2021_148
authors	Mingxi Chen
year	2021
title	Research on Epidemic Prevention and Management Measures in University Based on GIS and ABM – Taking South China University of Technology (Wushan Campus) as an Example
doi	https://doi.org/https://doi.org/10.1007/978-981-16-5983-6_14
source	Proceedings of the 2021 DigitalFUTURES The 3rd International Conference on Computational Design and Robotic Fabrication (CDRF 2021)
summary	Prevention and management of epidemic is a protracted war. As large community in city, universities are key regions in the anti-epidemic period. However, the current epidemic prevention and management measures in many universities do not compatible with the spatial form and the characteristics of the population, likely to lead to waste of resources and cause conflicts. The research simulates campus environment by constructing GIS model, and simulates the behavior of campus crowd by ABM. Under the coupling effect of the two, the real-time calculation of the spread of epidemic in universities can be calculated in real-time, making up for the deficiency of GIS model which can only do static data analysis. On this basis, research takes South China University of Technology as an example and assumes three epidemic prevention management measures, i.e. closed-off management, zoning management and self prevention, respectively to simulate the spread of the epidemic, sum up the results of different management measures and provide certain suggestions.
series	cdrf
last changed	2022/09/29 07:53

_id	cdrf2021_231
id	cdrf2021_231
authors	Andrea Macruz, Ernesto Bueno, Gustavo G. Palma, Jaime Vega, Ricardo A. Palmieri, and Tan Chen Wu
year	2021
title	Measuring Human Perception of Biophilically-Driven Design with Facial Micro-expressions Analysis and EEG Biosensor
doi	https://doi.org/https://doi.org/10.1007/978-981-16-5983-6_22
source	Proceedings of the 2021 DigitalFUTURES The 3rd International Conference on Computational Design and Robotic Fabrication (CDRF 2021)
summary	This paper investigates the role technology and neuroscience play in aiding the design process and making meaningful connections between people and nature. Using two workshops as a vehicle, the team introduced advanced technologies and Quantified Self practices that allowed people to use neural data and pattern recognition as feedback for the design process. The objective is to find clues to natural elements of human perception that can inform the design to meet goals for well-being. A pattern network of geometric shapes that achieve a higher level of monitored meditation levels and point toward a positive emotional valence is proposed. By referencing biological forms found in nature, the workshops utilized an algorithmic process that explored how nature can influence architecture. To measure the impact, the team used FaceOSC for capture and an Artificial Neural Network for micro-expression recognition, and a MindWave sensor manufactured by NeuroSky, which documented the human response further. The methodology allowed us to establish a boundary logic, ranking geometric shapes that suggested positive emotions and a higher level of monitored meditation levels. The results pointed us to a deeper level of understanding relative to geometric shapes in design. They indicate a new way to predict how well-being factors can clarify and rationalize a more intuitive design process inspired by nature.
series	cdrf
email
last changed	2022/09/29 07:53

_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	caadria2021_389
id	caadria2021_389
authors	del Campo, Matias
year	2021
title	Architecture,Language and AI - Language,Attentional Generative Adversarial Networks (AttnGAN) and Architecture Design
doi	https://doi.org/10.52842/conf.caadria.2021.1.211
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. 211-220
summary	The motivation to explore Attentional Generative Adversarial Networks (AttnGAN) as a design technique in architecture can be found in the desire to interrogate an alternative design methodology that does not rely on images as starting point for architecture design, but language. Traditionally architecture design relies on visual language to initiate a design process, wither this be a napkin sketch or a quick doodle in a 3D modeling environment. AttnGAN explores the information space present in programmatic needs, expressed in written form, and transforms them into a visual output. The key results of this research are shown in this paper with a proof-of-concept project: the competition entry for the 24 Highschool in Shenzhen, China. This award-winning project demonstrated the ability of GraphCNN to serve as a successful design methodology for a complex architecture program. In the area of Neural Architecture, this technique allows to interrogate shape through language. An alternative design method that creates its own unique sensibility.
keywords	Artificial Intelligence; Machine Learning; Artificial Neural Networks; Semiotics; Design Methodology
series	CAADRIA
email
last changed	2022/06/07 07:55

_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	ijac202119205
id	ijac202119205
authors	Fukuda, Tomohiro; Marcos Novak, Hiroyuki Fujii, Yoann Pencreach
year	2021
title	Virtual reality rendering methods for training deep learning, analysing landscapes, and preventing virtual reality sickness
source	International Journal of Architectural Computing 2021, Vol. 19 - no. 2, 190–207
summary	Virtual reality (VR) has been proposed for various purposes such as design studies, presentation, simulation and communication in the field of computer-aided architectural design. This paper explores new roles for VR; in particular, we propose rendering methods that consist of post-processing rendering, segmentation rendering and shadow-casting rendering for more-versatile approaches in the use of data. We focus on the creation of a dataset of annotated images, composed of paired foreground-background and semantic-relevant images, in addition to traditional immersive rendering for training deep learning neural networks and analysing landscapes. We also develop a camera velocity rendering method using a customised segmentation rendering technique that calculates the linear and angular velocities of the virtual camera within the VR space at each frame and overlays a colour on the screen according to the velocity value. Using this velocity information, developers of VR applications can improve the animation path within the VR space and prevent VR sickness. We successfully applied the developed methods to urban design and a design project for a building complex. In conclusion, the proposed method was evaluated to be both feasible and effective.
keywords	Virtual reality, rendering, shader, deep learning, landscape analytics, virtual reality sickness, Fourth Industrial Revolution, computer-aided architectural design
series	journal
email
last changed	2024/04/17 14:29

_id	caadria2021_166
id	caadria2021_166
authors	Hu, Wei
year	2021
title	The experiment of neural network on the cognition of style
doi	https://doi.org/10.52842/conf.caadria.2021.2.061
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. 61-70
summary	This paper introduces a method to obtain quantified style description vector which is for computer analysis input by using image style classification task. In the experiment, 3331 architectural photos of three styles obtained by crawling and filtering were used as training data. A deep convolutional neural network was trained to map architectural images to high-dimensional feature space, and then the high-dimensional style description vector was used to output the measurement results of style cognition with fully connected neural network. Tested by test data-set of 371 architectural pictures, the accuracy rate of style cognition reached more than 80%. The neural network using architectural data training was applied to the style cognition of non-architectural objects, high accuracy rate was also achieved, it proved that this quantified style description vector did include the information about style cognition to some extent instead of simply classification. Finally, the similarities and differences between the cognitive characteristics of style of neural network and human beings are investigated.
keywords	deep neural network; style cognition experiment; eye tracker
series	CAADRIA
email
last changed	2022/06/07 07:50

_id	caadria2021_354
id	caadria2021_354
authors	Huang, Chenyu, Gong, Pixin, Ding, Rui, Qu, Shuyu and Yang, Xin
year	2021
title	Comprehensive analysis of the vitality of urban central activities zone based on multi-source data - Case studies of Lujiazui and other sub-districts in Shanghai CAZ
doi	https://doi.org/10.52842/conf.caadria.2021.2.549
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. 549-558
summary	With the use of the concept Central Activities Zone in the Shanghai City Master Plan (2017-2035) to replace the traditional concept of Central Business District, core areas such as Shanghai Lujiazui will be given more connotations in the future construction and development. In the context of todays continuous urbanization and high-speed capital flow, how to identify the development status and vitality characteristics is a prerequisite for creating a high-quality Central Activities Zone. Taking Shanghai Lujiazui sub-district etc. as an example, the vitality value of weekday and weekend as well as 19 indexes including density of functional facilities and building morphology is quantified by obtaining multi-source big data. Meanwhile, the correlation between various indexes and the vitality characteristics of the Central Activities Zone are tried to summarize in this paper. Finally, a neural network regression model is built to bridge the design scheme and vitality values to realize the prediction of the vitality of the Central Activities Zone. The data analysis method proposed in this paper is versatile and efficient, and can be well integrated into the urban big data platform and the City Information Modeling, and provides reliable reference suggestions for the real-time evaluation of future urban construction.
keywords	multi-source big data; Central Activities Zone; Vitality; Lujiazui
series	CAADRIA
email
last changed	2022/06/07 07:50

_id	sigradi2021_200
id	sigradi2021_200
authors	Karabagli, Kaan, Koc, Mustafa, Basu, Prithwish and As, Imdat
year	2021
title	A Machine Learning Approach to Translate Graph Representations into Conceptual Massing Models
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. 191–202
summary	Machine learning (ML) has popular applications in domains involving image, video, text and voice. However, in architecture, image-based ML systems face challenges capturing the complexity of three-dimensional space. In this paper, we leverage a graph-based ML system that can capture the inherent topology of architectural conceptual designs and identify high-performing latent patterns within such designs. In particular, our goal is to translate architectural graph data into three-dimensional massing models. We are building on our prior ML work, where we, a. discovered latent topological features, b. composed building blocks into new designs, c. evaluated their feasibility, and d. explored Generative Adversarial (Neural) Networks (GAN)-generated design variations. We trained the ML system with architectural design data that we gathered from an online architectural design competition platform, translated them into machine-readable graph representations, and identified their essential subgraphs to develop novel compositions. In this paper, we explore how these novel designs (outputted in graph form), can be translated into three-dimensional architectural form. We present an ML approach to turn graph representations into functional volumetric massing models. The ultimate goal of the study is to develop an end-to-end pipeline to generate architectural design - from a graph representation to a fully developed conceptual proxy of a designed product. The research question is promising in automating conceptual design, and we believe the outcome can be relevant to other design disciplines as well.
keywords	Architectural design, machine learning, conceptual design, deep learning, artificial intelligence
series	SIGraDi
email
last changed	2022/05/23 12:10

_id	caadria2021_305
id	caadria2021_305
authors	Keshavarzi, Mohammad, Afolabi, Oladapo, Caldas, Luisa, Yang, Allen Y. and Zakhor, Avideh
year	2021
title	GenScan: A Generative Method for Populating Parametric 3D Scan Datasets
doi	https://doi.org/10.52842/conf.caadria.2021.1.091
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. 91-100
summary	The availability of rich 3D datasets corresponding to the geometrical complexity of the built environments is considered an ongoing challenge for 3D deep learning methodologies. To address this challenge, we introduce GenScan, a generative system that populates synthetic 3D scan datasets in a parametric fashion. The system takes an existing captured 3D scan as an input and outputs alternative variations of the building layout including walls, doors, and furniture with corresponding textures. GenScan is fully automated system that can also be manually controlled by a user through an assigned user interface. Our proposed system utilizes a combination of a hybrid deep neural network and a parametrizer module to extract and transform elements of a given 3D scan. GenScan takes advantage of style transfer techniques to generate new textures for the generated scenes. We believe our system would facilitate data augmentation to expand the currently limited 3D geometry datasets commonly used in 3D computer vision, generative design and general 3D deep learning tasks.
keywords	Computational Geometry; Generative Modeling; 3D Manipulation; Texture Synthesis
series	CAADRIA
email
last changed	2022/06/07 07:52

_id	acadia21_512
id	acadia21_512
authors	Liu, Zidong
year	2021
title	Topological Networks Using a Sequential Method
doi	https://doi.org/10.52842/conf.acadia.2021.512
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. 512-519.
summary	The paper shares preliminary results of a novel sequential method to expand existing topology-based generative design. The approach is applied to building an interactive community design system based on a mobile interface. In the process of building an interactive design system, one of the core problems is to harness the complex topological network formed by user demands. After decades of graph theory research in architecture, a consensus on self-organized complex networks has emerged. However, how to convert input complex topological data into spatial layouts in generative designs is still a difficult problem worth exploring. The paper proposes a way to simplify the problem: in some cases, the spatial network of buildings can be approximated as a collection of sequences based on circulation analysis. In the process of network serialization, the personalized user demands are transformed into activity patterns and further into serial spaces. This network operation gives architects more room to play with their work. Rather than just designing an algorithm that directly translates users’ demands into shape, architects can be more actively involved in organizing spatial networks by setting up a catalogue of activity patterns of the residents, thus contributing to a certain balance of top-down order and bottom-up richness in the project. The research on data serialization lays a solid foundation for the future exploration of Recurrent Neural Network (RNN) applied to generative design.
series	ACADIA
type	paper
email
last changed	2023/10/22 12:06

_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_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
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last changed	2022/06/07 07:58

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