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	ecaade2018_389
id	ecaade2018_389
authors	Algeciras-Rodriguez, Jose
year	2018
title	Stochastic Hybrids - From references to design options through Self-Organizing Maps methodology.
doi	https://doi.org/10.52842/conf.ecaade.2018.1.119
source	Kepczynska-Walczak, A, Bialkowski, S (eds.), Computing for a better tomorrow - Proceedings of the 36th eCAADe Conference - Volume 1, Lodz University of Technology, Lodz, Poland, 19-21 September 2018, pp. 119-128
summary	This ongoing research aims to define a general assisted design method to offer non-trivial design options, where form is produced by merging characteristics from initial reference samples collection that serves as an input set. This project explores design processes laying on the use of non-linear procedures and experiments with Self-Organizing Map (SOM), as neural networks algorithms, to generate geometries. All processes are applied to a set of models representing classic sculpture, whose characteristics are encoded by the SOM process. The result of it is a set of new geometry resembling characteristics from the original references. This method produces hybrid forms that acquire characteristics from several input references. The resulting hybrid entities are intended to be non-trivial solutions to specific design situations, so far, at the stage of this research, mainly formal requirements.
keywords	Self-Orgnizing Maps; Cognitive Space; Design Options; Form Finding; Artificial Intelligence
series	eCAADe
email
last changed	2022/06/07 07:54

_id	ijac201816406
id	ijac201816406
authors	As, Imdat; Siddharth Pal and Prithwish Basu
year	2018
title	Artificial intelligence in architecture: Generating conceptual design via deep learning
source	International Journal of Architectural Computing vol. 16 - no. 4, 306-327
summary	Artificial intelligence, and in particular machine learning, is a fast-emerging field. Research on artificial intelligence focuses mainly on image-, text- and voice-based applications, leading to breakthrough developments in self-driving cars, voice recognition algorithms and recommendation systems. In this article, we present the research of an alternative graph- based machine learning system that deals with three-dimensional space, which is more structured and combinatorial than images, text or voice. Specifically, we present a function-driven deep learning approach to generate conceptual design. We trained and used deep neural networks to evaluate existing designs encoded as graphs, extract significant building blocks as subgraphs and merge them into new compositions. Finally, we explored the application of generative adversarial networks to generate entirely new and unique designs.
keywords	Architectural design, conceptual design, deep learning, artificial intelligence, generative design
series	journal
email
last changed	2019/08/07 14:04

_id	sigradi2018_1671
id	sigradi2018_1671
authors	Brito, Michele; de Sá, Ana Isabel; Borges, Jéssica; Rena, Natacha
year	2018
title	IndAtlas - Technopolitic platform for urban investigation
source	SIGraDi 2018 [Proceedings of the 22nd Conference of the Iberoamerican Society of Digital Graphics - ISSN: 2318-6968] Brazil, São Carlos 7 - 9 November 2018, pp. 1305-1312
summary	This article presents the project of the urban research platform IndAtlas, currently in early development stage by UFMG’s Research Group Indisciplinar. Through the association of crowdsourcing tools, a spatial database and the production of visualizations of different types, it is intended to create a Web platform for collecting, analyzing and depicting information about processes of production and transformation of urban space. It is proposed that the phenomena (themes) investigated in the platform are approached mainly from four axes: 1) spatial / territorial; 2) temporal; 3) social; 4) communicational. To do this, we try to combine online collaborative maps with the production of dynamic timelines and visualizations of networks of social actors (graphs), connected with social networks and Wiki pages. The article will address the development of Indisciplinar’s working method, which guided the proposal of the platform, as well as the functional and technical aspects to be observed for its implementation, the proposed architecture and the importance of interoperability for the project. Finally, the inquiries derived from the first test experiment of an IndAtlas test prototype will be presented. The experiment took place in a workshop belonging to the Cidade Eletrônika 2018 Festival – an arts and technology event. The workshop was offered in January of the same year, and it proposed a collaborative cartography of the Santa Tereza neighborhood, in Belo Horizonte / MG – a traditional neighborhood of great importance for historical heritage, currently subject to great real estate pressure and the focus of a series of territorial disputes.
keywords	IndAtlas, Crowdsourcing, Urban Technopolitics,, Digital Cartographies,, Spatial Data.
series	SIGRADI
email
last changed	2021/03/28 19:58

_id	ecaade2018_139
id	ecaade2018_139
authors	Cudzik, Jan and Radziszewski, Kacper
year	2018
title	Artificial Intelligence Aided Architectural Design
doi	https://doi.org/10.52842/conf.ecaade.2018.1.077
source	Kepczynska-Walczak, A, Bialkowski, S (eds.), Computing for a better tomorrow - Proceedings of the 36th eCAADe Conference - Volume 1, Lodz University of Technology, Lodz, Poland, 19-21 September 2018, pp. 77-84
summary	Tools and methods used by architects always had an impact on the way building were designed. With the change in design methods and new approaches towards creation process, they became more than ever before crucial elements of the creation process. The automation of architects work has started with computational functions that were introduced to traditional computer-aided design tools. Nowadays architects tend to use specified tools that suit their specific needs. In some cases, they use artificial intelligence. Despite many similarities, they have different advantages and disadvantages. Therefore the change in the design process is more visible and unseen before solution are brought in the discipline. The article presents methods of applying the selected artificial intelligence algorithms: swarm intelligence, neural networks and evolutionary algorithms in the architectural practice by authors. Additionally research shows the methods of analogue data input and output approaches, based on vision and robotics, which in future combined with intelligence based algorithms, might simplify architects everyday practice. Presented techniques allow new spatial solutions to emerge with relatively simple intelligent based algorithms, from which many could be only accomplished with dedicated software. Popularization of the following methods among architects, will result in more intuitive, general use design tools.
keywords	computer aideed design; artificial intelligence,; evolutionary algorithms; swarm behaviour; optimization; parametric design
series	eCAADe
email
last changed	2022/06/07 07:56

_id	ecaade2018_292
id	ecaade2018_292
authors	Dennemark, Martin, Aicher, Andreas, Schneider, Sven and Hailu, Tesfaye
year	2018
title	Generative Hydrology Network Analysis - A parametric approach to water infrastructure based urban planning
doi	https://doi.org/10.52842/conf.ecaade.2018.2.327
source	Kepczynska-Walczak, A, Bialkowski, S (eds.), Computing for a better tomorrow - Proceedings of the 36th eCAADe Conference - Volume 2, Lodz University of Technology, Lodz, Poland, 19-21 September 2018, pp. 327-334
summary	Urban water systems need to be dimensioned well to be economical and distribute water in a good quality to all consumers. Their pipe sizes are dependent on demand and location of consuming nodes. Within uncertain development of cities, planning sustainable hydraulic networks is challenging. This paper explores, how the definition of urban design parameters can be supported using parametric urban design models and computational water network analysis. For the latter we developed new components for Grasshopper based on the open accessible water analysis tool EPANET. In two example cases we demonstrate potential applications of this tool for water-sensitive planning of emerging cities to find optimal positions for water sources or pipe diameters. In subsequent research, this could be used to derive probability-based recommendations for the dimensioning of a water network within uncertain growth.
keywords	water infrastructure; urban planning; parametric design; uncertainty; emerging cities
series	eCAADe
email
last changed	2022/06/07 07:55

_id	sigradi2018_1772
id	sigradi2018_1772
authors	Farias, Ana C. C; Paio, Alexandra
year	2018
title	Technopolitics and participatory processes - Interactions between digital networks and streets in Lisbon
source	SIGraDi 2018 [Proceedings of the 22nd Conference of the Iberoamerican Society of Digital Graphics - ISSN: 2318-6968] Brazil, São Carlos 7 - 9 November 2018, pp. 1321-1327
summary	This article presents a survey and analysis of the technopolitical devices created in projects of local initiatives carried out in Lisbon, within the scope of the Neighborhoods and Zones of Priority Intervention program, of the City Council. The methodology adopted is based on the reading of the projects, analysis of the technopolitics encountered and interviews with representatives of the entities that proposed them. In this way, it was possible to observe tendencies, potentialities and difficulties faced in the proposal and use of technopolitics in the scope of these projects, which allowed to conclude that the digital dimension has an important role in the reinforcement and expansion of an existing articulation in the territories, between partner entities and communities.
keywords	Technopolitics; Lisbon; observatory; community-based action; digital technology
series	SIGRADI
email
last changed	2021/03/28 19:58

_id	sigradi2018_1637
id	sigradi2018_1637
authors	Hosni, Cássia; Prata, Didiana; Ferrari, Erica; Lavigne, Nathalia
year	2018
title	Museum of the Underway Artists - Metanarratives on Networks
source	SIGraDi 2018 [Proceedings of the 22nd Conference of the Iberoamerican Society of Digital Graphics - ISSN: 2318-6968] Brazil, São Carlos 7 - 9 November 2018, pp. 1167-1172
summary	This article draws attention to an experience of workshop Masp.Etc.Br carried out by the Research Group Aesthetics of Memory of the 21st Century at the São Paulo Museum of Art (MASP), in which an aesthetic mapping of Paulista Avenue was done to discuss collaborative processes of image production organized through hashtags. The material gathered was an ample set of images posted on Instagram. After two collaborative edition processes, a final version was projected as a video at the free span of the Museum. These experiences bring attention to the aesthetics of the database and narratives in social networks.
keywords	São Paulo Museum of Art; MASP; Paulista Avenue; Public space; Database Aesthetics
series	SIGRADI
email
last changed	2021/03/28 19:58

_id	acadia18_156
id	acadia18_156
authors	Huang, Weixin; Zheng, Hao
year	2018
title	Architectural Drawings Recognition and Generation through Machine Learning
doi	https://doi.org/10.52842/conf.acadia.2018.156
source	ACADIA // 2018: Recalibration. On imprecisionand infidelity. [Proceedings of the 38th Annual Conference of the Association for Computer Aided Design in Architecture (ACADIA) ISBN 978-0-692-17729-7] Mexico City, Mexico 18-20 October, 2018, pp. 156-165
summary	With the development of information technology, the ideas of programming and mass calculation were introduced into the design field, resulting in the growth of computer- aided design. With the idea of designing by data, we began to manipulate data directly, and interpret data through design works. Machine Learning as a decision making tool has been widely used in many fields. It can be used to analyze large amounts of data and predict future changes. Generative Adversarial Network (GAN) is a model framework in machine learning. It’s specially designed to learn and generate output data with similar or identical characteristics. Pix2pixHD is a modified version of GAN that learns image data in pairs and generates new images based on the input. The author applied pix2pixHD in recognizing and generating architectural drawings, marking rooms with different colors and then generating apartment plans through two convolutional neural networks. Next, in order to understand how these networks work, the author analyzed their framework, and provided an explanation of the three working principles of the networks, convolution layer, residual network layer and deconvolution layer. Lastly, in order to visualize the networks in architectural drawings, the author derived data from different layer and different training epochs, and visualized the findings as gray scale images. It was found that the features of the architectural plan drawings have been gradually learned and stored as parameters in the networks. As the networks get deeper and the training epoch increases, the features in the graph become more concise and clearer. This phenomenon may be inspiring in understanding the designing behavior of humans.
keywords	full paper, design study, generative design, ai + machine learning, ai & machine learning
series	ACADIA
type	paper
email
last changed	2022/06/07 07:49

_id	lasg_whitepapers_2019_133
id	lasg_whitepapers_2019_133
authors	Ji, Haru Hyunkyung; and Graham Wakefield
year	2019
title	Selected Artificial Natures, 2017-2018
source	Living Architecture Systems Group White Papers 2019 [ISBN 978-1-988366-18-0] Riverside Architectural Press: Toronto, Canada 2019. pp.133 - 142
summary	Artificial Nature is a research-creation collaboration co-founded by Haru Hyunkyung Ji and Graham Wakefield in 2007. It has led to a decade of immersive installations in which the invitation is to become part of an alien ecosystem rich in feedback networks.1 Here we present four recent works in this series between 2017 and 2018.
keywords	living architecture systems group, organicism, intelligent systems, design methods, engineering and art, new media art, interactive art, dissipative systems, technology, cognition, responsiveness, biomaterials, artificial natures, 4DSOUND, materials, virtual projections,
email
last changed	2019/07/29 14:02

_id	ecaade2018_111
id	ecaade2018_111
authors	Khean, Nariddh, Fabbri, Alessandra and Haeusler, M. Hank
year	2018
title	Learning Machine Learning as an Architect, How to? - Presenting and evaluating a Grasshopper based platform to teach architecture students machine learning
doi	https://doi.org/10.52842/conf.ecaade.2018.1.095
source	Kepczynska-Walczak, A, Bialkowski, S (eds.), Computing for a better tomorrow - Proceedings of the 36th eCAADe Conference - Volume 1, Lodz University of Technology, Lodz, Poland, 19-21 September 2018, pp. 95-102
summary	Machine learning algorithms have become widely embedded in many aspects of modern society. They have come to enhance systems, such as individualised marketing, social media services, and search engines. However, contrasting its growing ubiquity, the architectural industry has been comparatively resistant in its adoption; objectively one of the slowest industries to integrate with machine learning. Machine learning expertise can be separate from professionals in other fields; however, this separation can be a major hinderance in architecture, where interaction between the designer and the design facilitates the production of favourable outcomes. To bridge this knowledge gap, this research suggests that the solution lies with architectural education. Through the development of a novel educative framework, the research aims to teach architecture students how to implement machine learning. Exploration of student-centred pedagogical strategies was used to inform the conceptualisation of the educative module, which was subsequently implemented into an undergraduate computational design studio, and finally evaluated on its ability to effectively teach designers machine learning. The developed educative module represents a step towards greater technological adoption in the architecture industry.
keywords	Artificial Intelligence; Machine Learning; Neural Networks; Student-Centred Learning; Educative Framework
series	eCAADe
email
last changed	2022/06/07 07:52

_id	caadria2018_126
id	caadria2018_126
authors	Khean, Nariddh, Kim, Lucas, Martinez, Jorge, Doherty, Ben, Fabbri, Alessandra, Gardner, Nicole and Haeusler, M. Hank
year	2018
title	The Introspection of Deep Neural Networks - Towards Illuminating the Black Box - Training Architects Machine Learning via Grasshopper Definitions
doi	https://doi.org/10.52842/conf.caadria.2018.2.237
source	T. Fukuda, W. Huang, P. Janssen, K. Crolla, S. Alhadidi (eds.), Learning, Adapting and Prototyping - Proceedings of the 23rd CAADRIA Conference - Volume 2, Tsinghua University, Beijing, China, 17-19 May 2018, pp. 237-246
summary	Machine learning is yet to make a significant impact in the field of architecture and design. However, with the combination of artificial neural networks, a biologically inspired machine learning paradigm, and deep learning, a hierarchical subsystem of machine learning, the predictive capabilities of machine learning processes could prove a valuable tool for designers. Yet, the inherent knowledge gap between the fields of architecture and computer science has meant the complexity of machine learning, and thus its potential value and applications in the design of the built environment remain little understood. To bridge this knowledge gap, this paper describes the development of a learning tool directed at architects and designers to better understand the inner workings of machine learning. Within the parametric modelling environment of Grasshopper, this research develops a framework to express the mathematic and programmatic operations of neural networks in a visual scripting language. This offers a way to segment and parametrise each neural network operation into a basic expression. Unpacking the complexities of machine learning in an intermediary software environment such as Grasshopper intends to foster the broader adoption of artificial intelligence in architecture.
keywords	machine learning; neural network; action research; supervised learning; education
series	CAADRIA
email
last changed	2022/06/07 07:52

_id	caadria2018_314
id	caadria2018_314
authors	Kim, Jin Sung, Song, Jae Yeol and Lee, Jin Kook
year	2018
title	Approach to the Extraction of Design Features of Interior Design Elements Using Image Recognition Technique
doi	https://doi.org/10.52842/conf.caadria.2018.2.287
source	T. Fukuda, W. Huang, P. Janssen, K. Crolla, S. Alhadidi (eds.), Learning, Adapting and Prototyping - Proceedings of the 23rd CAADRIA Conference - Volume 2, Tsinghua University, Beijing, China, 17-19 May 2018, pp. 287-296
summary	This paper aims to propose deep learning-based approach to the auto-recognition of their design features of interior design elements using given digital images. The recently image recognition technique using convolutional neural networks has shown great success in the various field of research and industry. The open-source frameworks and pre-trained image recognition models supporting image recognition task enable us to easily retrain the models to apply them on any domain. This paper describes how to apply such techniques on interior design process and depicts some demonstration results in that approaches. Furniture that is one of the most common interior design elements has sub-feature including implicit design features, such as style, shape, function as well as explicit properties, such as component, materials, and size. This paper shows to retrain the model to extract some of the features for efficiently managing and utilizing such design information. The target element is chair and the target design features are limited to functional features, materials, seating capacity and design style. Total 3933 chair images dataset and 6 retrained image recognition models were utilized for retraining. Through the combination of those multiple models, inference demonstration also has been described.
keywords	Deep learning; Image recognition; Interior design elements; Design feature; Chair
series	CAADRIA
email
last changed	2022/06/07 07:52

_id	caadria2018_158
id	caadria2018_158
authors	Koh, Immanuel
year	2018
title	Learning Design Trends from Social Networks - Data Mining, Analysis & Visualization of Grasshopper® Online User Community
doi	https://doi.org/10.52842/conf.caadria.2018.2.277
source	T. Fukuda, W. Huang, P. Janssen, K. Crolla, S. Alhadidi (eds.), Learning, Adapting and Prototyping - Proceedings of the 23rd CAADRIA Conference - Volume 2, Tsinghua University, Beijing, China, 17-19 May 2018, pp. 277-286
summary	The paper has demonstrated that the increasingly online relationship between designers and their digital tools can be quantitatively represented, described and analyzed through the data-mining of design-domain specific and tool-based social network (i.e. Grasshopper3D). It explores design trends' correlations based on network user groups' size, users' demographics, nodes' degree centrality and discussion threads' popularity.
keywords	Social Networks; Design Trends; Big Data; Parametric Design Tools; Data Visualization
series	CAADRIA
email
last changed	2022/06/07 07:51

_id	acadia18_166
id	acadia18_166
authors	Kvochick, Tyler
year	2018
title	Sneaky Spatial Segmentation. Reading Architectural Drawings with Deep Neural Networks and Without Labeling Data
doi	https://doi.org/10.52842/conf.acadia.2018.166
source	ACADIA // 2018: Recalibration. On imprecisionand infidelity. [Proceedings of the 38th Annual Conference of the Association for Computer Aided Design in Architecture (ACADIA) ISBN 978-0-692-17729-7] Mexico City, Mexico 18-20 October, 2018, pp. 166-175
summary	Currently, it is nearly impossible for an artificial neural network to generalize a task from very few examples. Humans, however, excel at this. For instance, it is not necessary for a designer to see thousands or millions of unique examples of how to place a given drawing symbol in a way that meets the economic, aesthetic, and performative goals of the project. In fact, the goals can be (and usually are) communicated abstractly in natural language. Machine learning (ML) models, however, do need numerous examples. The methods that we explore here are an attempt to circumvent this in order to make ML models more immediately useful. In this work, we present progress on the application of contemporary ML techniques to the design process in the architecture, engineering, and construction (AEC) industry. We introduce a technique to partially circumvent the data hungriness of neural networks, which is a significant impediment to their application outside of the ML research community. We also show results on the applicability of this technique to real-world drawings and present research that addresses how some fundamental attributes of drawings as images affect the way they are interpreted in deep neural networks. Our primary contribution is a technique to train a neural network to segment real-world architectural drawings after using only generated pseudodrawings.
keywords	full paper, representation + perception, computation, ai & machine learning
series	ACADIA
type	paper
email
last changed	2022/06/07 07:51

_id	caadria2018_293
id	caadria2018_293
authors	Lee, Jisun and Lee, Hyunsoo
year	2018
title	The Visible and Invisible Network of a Self-Organizing Town - Agent-Based Simulation for Investigating Urban Development Process
doi	https://doi.org/10.52842/conf.caadria.2018.2.411
source	T. Fukuda, W. Huang, P. Janssen, K. Crolla, S. Alhadidi (eds.), Learning, Adapting and Prototyping - Proceedings of the 23rd CAADRIA Conference - Volume 2, Tsinghua University, Beijing, China, 17-19 May 2018, pp. 411-420
summary	This study applies self-organization as a methodology to understand the complex process of city networks caused by interactions between spatial structures and individual behaviors. The agent-based simulations have been conducted to investigate the visible and invisible networks understanding the self-organized aspects of city development processes. To develop optimal future networks providing connectivity and accessibility this study investigates spatial network configurations from internal individual behavior and movement. As results, it was found that the spatial configurations of the agent movement trails match to the current district boundaries and the similar network patterns were seen in various control values of agent behavior settings. This study contributes to searching out the hierarchy of network structures which is an important factor for re-planning of the way system.
keywords	Agent-based simulation; network analysis ; self organization ; urban development process ; Physarum polycephalum
series	CAADRIA
email
last changed	2022/06/07 07:52

_id	caadria2018_018
id	caadria2018_018
authors	Lin, Yuming and Huang, Weixin
year	2018
title	Social Behavior Analysis in Innovation Incubator Based on Wi-Fi Data - A Case Study on Yan Jing Lane Community
doi	https://doi.org/10.52842/conf.caadria.2018.2.197
source	T. Fukuda, W. Huang, P. Janssen, K. Crolla, S. Alhadidi (eds.), Learning, Adapting and Prototyping - Proceedings of the 23rd CAADRIA Conference - Volume 2, Tsinghua University, Beijing, China, 17-19 May 2018, pp. 197-206
summary	Innovation incubator is an emerging kind of office space which focuses on promoting social interaction in the space. From the perspective of environmental behavior, the complex relationship between a particular space form and the social interactions is well worth exploring. Based on Wi-Fi positioning data, this paper examined the spatial and temporal behavior in innovation incubators. Using the interdisciplinary social networks analysis, this paper further analyzed the social interactions in this space, mining out social structures such as gathering and community, and analyzing the relationship between these structures and spaces. The result shows that human behavior in innovation incubators has some interesting characteristics, and the social structures are closely linked with the functional area of innovation incubator. This paper provides a new perspective and introduces interdisciplinary approaches to study the social behaviors in a particular space form, which has great potential in future research.
keywords	environmental behavior study; social behavior analysis; innovation incubator; Wi-Fi IPS; social network
series	CAADRIA
email
last changed	2022/06/07 07:59

_id	caadria2018_083
id	caadria2018_083
authors	Luo, Dan, Wang, Jinsong and Xu, Weiguo
year	2018
title	Robotic Automatic Generation of Performance Model for Non-Uniform Linear Material via Deep Learning
doi	https://doi.org/10.52842/conf.caadria.2018.1.039
source	T. Fukuda, W. Huang, P. Janssen, K. Crolla, S. Alhadidi (eds.), Learning, Adapting and Prototyping - Proceedings of the 23rd CAADRIA Conference - Volume 1, Tsinghua University, Beijing, China, 17-19 May 2018, pp. 39-48
summary	In the following research, a systematic approach is developed to generate an experiment-based performance model that computes and customizes properties of non-uniform linear materials to accommodate the form of designated curve under bending and natural force. In this case, the test subject is an elastomer strip of non-uniform sections. A novel solution is provided to obtain sufficient training data required for deep learning with an automatic material testing mechanism combining robotic arm automation and image recognition. The collected training data are fed into a deep combination of neural networks to generate a material performance model. Unlike most traditional performance models that are only able to simulate the final form from the properties and initial conditions of the given materials, the trained neural network offers a two-way performance model that is also able to compute appropriate material properties of non-uniform materials from target curves. This network achieves complex forms with minimal and effective programmed materials with complicated nonlinear properties and behaving under natural forces.
keywords	Material performance model; Deep Learning; Robotic automation; Material computation; Neural network
series	CAADRIA
email
last changed	2022/06/07 07:59

_id	ijac201816304
id	ijac201816304
authors	Miao, Yufan; Reinhard Koenig, Katja Knecht, Kateryna Konieva, Peter Buš and Mei-Chih Chang
year	2018
title	Computational urban design prototyping: Interactive planning synthesis methods—a case study in Cape Town
source	International Journal of Architectural Computing vol. 16 - no. 3, 212-226
summary	This article is motivated by the fact that in Cape Town, South Africa, approximately 7.5 million people live in informal settlements and focuses on potential upgrading strategies for such sites. To this end, we developed a computational method for rapid urban design prototyping. The corresponding planning tool generates urban layouts including street network, blocks, parcels and buildings based on an urban designer’s specific requirements. It can be used to scale and replicate a developed urban planning concept to fit different sites. To facilitate the layout generation process computationally, we developed a new data structure to represent street networks, land parcellation, and the relationship between the two. We also introduced a nested parcellation strategy to reduce the number of irregular shapes generated due to algorithmic limitations. Network analysis methods are applied to control the distribution of buildings in the communities so that preferred neighborhood relationships can be considered in the design process. Finally, we demonstrate how to compare designs based on various urban analysis measures and discuss the limitations that arise when we apply our method in practice, especially when dealing with more complex urban design scenarios.
keywords	Procedural modeling, spatial synthesis, generative design, urban planning
series	journal
email
last changed	2019/08/07 14:03

_id	ecaade2018_323
id	ecaade2018_323
authors	Newton, David
year	2018
title	Multi-Objective Qualitative Optimization (MOQO) in Architectural Design
doi	https://doi.org/10.52842/conf.ecaade.2018.1.187
source	Kepczynska-Walczak, A, Bialkowski, S (eds.), Computing for a better tomorrow - Proceedings of the 36th eCAADe Conference - Volume 1, Lodz University of Technology, Lodz, Poland, 19-21 September 2018, pp. 187-196
summary	Architectural design problems are often multi-objective in nature, involving both qualitative and quantitative objectives. Previous research has focused exclusively on the development of multi-objective optimization algorithms that work with multiple quantitative objectives. No previous research has looked at the topic of multi-objective qualitative optimization (MOQO), in which multiple qualitative objectives are optimized simultaneously. This research addresses MOQO through the development of a unique multi-objective optimization algorithm for the conceptual design phase that uses three-dimensional convolutional neural networks (3D CNNs) to measure user-defined qualities in architectural massing models.
keywords	multi-objective optimization; generative design; multi-objective qualitative optimization; algorithmic design
series	eCAADe
email
last changed	2022/06/07 07:58

_id	sigradi2023_141
id	sigradi2023_141
authors	Nunes, Ana Julia Thomaz de Frias, Vasconcelos, Guilherme Nunes de and van Stralen, Mateus de Sousa
year	2023
title	National Mapping of Researchers and Artists in Immersive Technologies: A Collaborative Tool for Understanding the Landscape of Scientific and Artistic Production in Brazil
source	García Amen, F, Goni Fitipaldo, A L and Armagno Gentile, Á (eds.), Accelerated Landscapes - Proceedings of the XXVII International Conference of the Ibero-American Society of Digital Graphics (SIGraDi 2023), Punta del Este, Maldonado, Uruguay, 29 November - 1 December 2023, pp. 1525–1534
summary	This paper presents an initial version of the mapping of Brazilian actors in the field of immersive technologies applied to architecture, urbanism, design, and arts. The mapping includes academic and artistic actors who have produced within the researched theme between 2018-2023. Google Scholar and institutional repositories were the main search tools for relevant productions. After gathering the data, validation procedures were conducted. The eligible names were then added to an online table, intended to be collaborative and dynamic The results revealed a total of 45 mapped actors, including eight artists and 38 academics, affiliated with 21 institutions across Brazil. The collaborative online table situates Brazilian immersive technologies production and help to identify affinities between actors, fostering collaborations and research networks. It provides the possibility of visualizing the distribution of these actors nationwide, informing the academic and non-academic communities about the national artistic and scientific productions within the field.
keywords	Virtual Reality, Augmented Reality, Mixed Reality, Mapping, Immersive Technology, Brazilian context
series	SIGraDi
email
last changed	2024/03/08 14:08

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