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

PDF papers
References

Hits 1 to 20 of 613

Reformat results as: short short into frame detailed detailed into frame

_id	ascaad2021_022
id	ascaad2021_022
authors	Baºarir, Lale; Kutluhan Erol
year	2021
title	Briefing AI: From Architectural Design Brief Texts to Architectural Design Sketches
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. 23-31
summary	The main focus of this research is to uncover the underlying intuitive knowledge of architecture with the help of machine learning models. To achieve this, a generic architectural design process is considered and divided into iterative portions based on their output for each phase. This study looks into the initial portion of the architectural design process called “Briefing”. The authors search for the intuition that exists within the design process and how it can be learned by artificial intelligence (AI) that is currently gained through master-apprentice relationship and experience that builds up this knowledge. In this study, a way to enable users to attain an architectural design sketch while defining an architectural design problem with text is explored. This on-going research decomposes the components of the briefing and preliminary design sketching processes. Therefore the domain knowledge at each phase is considered for translating to constraints via natural language processing (NLP) and machine learning (ML) models such as Generative Adversarial Networks (GANs).
series	ASCAAD
type	normal paper
email
last changed	2021/08/09 13:11

_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	ijac202119404
id	ijac202119404
authors	Ghandi, Mona; Blaisdell, Marcus; Ismail, Mohamed
year	2021
title	Embodied empathy: Using affective computing to incarnate human emotion and cognition in architecture
source	International Journal of Architectural Computing 2021, Vol. 19 - no. 4, 532–552
summary	This research aims to develop a cyber-physical adaptive architectural space capable of real-time responses topeople’s emotions, based on biological and neurological data. To achieve this goal, we integrated artificialintelligence (AI), wearable technology, sensory environments, and adaptive architecture to create anemotional bond between a space and its occupants and encourage affective emotional interactions betweenthe two. The project’s objectives were to (1) measure and analyze biological and neurological data to detectemotions, (2) map and illustrate that emotional data, and (3) link occupants’emotions and cognition to a builtenvironment through a real-time emotive feedback loop. Using an interactive installation as a case study, thiswork examines the cognition-emotion-space interaction through changes in volume, color, and light as ameans of emotional expression. It contributes to the current theory and practice of cyber-physical design andthe role AI plays, as well as the interaction of technology and empathy.
keywords	Places and awareness, artificial intelligence and machine learning in design, intelligent responsive spaces,affective computing in architecture, cognition-emotion-space interaction, embodied empathy, neuromorphicdesign, cyber-physical neurospaces
series	journal
email
last changed	2024/04/17 14:29

_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	ecaade2021_158
id	ecaade2021_158
authors	Joyce, Sam Conrad and Nazim, Ibrahim
year	2021
title	Limits to Applied ML in Planning and Architecture - Understanding and defining extents and capabilities
doi	https://doi.org/10.52842/conf.ecaade.2021.1.243
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. 243-252
summary	There has been an exponential increase in Machine Learning (ML) research in design. Specifically, with Deep Learning becoming more accessible, frameworks like Generative Adversarial Networks (GANs), which are able to synthesise novel images are being used in the classification and generation of designs in architecture. While much of these explorations successfully demonstrate the 'magic' and potential of these techniques, their limits remain unclear, with only a few, but crucial, discussions on underlying fundamental limits and sensitivities of ML. This is a gap in our understanding of these tools especially within the complex context of planning and architecture. This paper seeks to discuss what limits ML in design as it exists today, by examining the state-of-the-art and mechanics of ML models relevant to design tasks. Aiming to help researchers to focus on productive uses of ML and avoid areas of over-promise.
keywords	Machine Learning; Artificial Intelligence; Creativity
series	eCAADe
email
last changed	2022/06/07 07:52

_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	sigradi2021_5
id	sigradi2021_5
authors	Ng, Provides, Fernandez, Alberto, Doria, David, Odaibat, Baha and Karastathi, Nikoletta
year	2021
title	AI In+form: Intelligence and Aggregation for Solar Designs in the Built Environment
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. 203–215
summary	Designers are increasingly challenged by a constant change of context and the interaction of layers of data from a huge variety of sources, from natural-artificial to human-machine. This research aims at mapping the interrelations of energy problems, bio- and artificial intelligence, and human-machine interaction to reflect and rethink the future of solar design. This paper first discusses its theoretical approach that stands at the convergence of light-harvesting systems, their aggregation and intelligence. Afterwhich, this paper explores their translation into iterative processes between designer and artificial intelligences, which is defined as rule/agent-based and machine learning systems; in particular, the relationship between Cellular Automata, Genetic Algorithm, and Generative Adversarial Networks (GANs) is discussed. Finally, it introduces a design project - @R.E.Ar_ - showing the proposed combinatorial pipeline and some preliminary results.
keywords	artificial intelligence, bio-inspired, solar design, Aggregation, human-machine interaction
series	SIGraDi
email
last changed	2022/05/23 12:10

_id	acadia23_v1_242
id	acadia23_v1_242
authors	Noel, Vernelle A.
year	2023
title	Carnival + AI: Heritage, Immersive virtual spaces, and Machine Learning
source	ACADIA 2023: Habits of the Anthropocene: Scarcity and Abundance in a Post-Material Economy [Volume 1: Projects Catalog of the 43rd Annual Conference of the Association of Computer Aided Design in Architecture (ACADIA) ISBN 979-8-9860805-8-1]. Denver. 26-28 October 2023. edited by A. Crawford, N. Diniz, R. Beckett, J. Vanucchi, M. Swackhamer 242-245.
summary	Built on a Situated Computations framework, this project explores preservation, reconfiguration, and presentation of heritage through immersive virtual experiences, and machine learning for new understandings and possibilities (Noel 2020; 2017; Leach and Campo 2022; Leach 2021). Using the Trinidad and Tobago Carnival - hereinafter referred to as Carnival - as a case study, Carnival + AI is a series of immersive experiences in design, culture, and artificial intelligence (AI). These virtual spaces create new digital modes of engaging with cultural heritage and reimagined designs of traditional sculptures in the Carnival (Noel 2021). The project includes three virtual events that draw on real events in the Carnival: (1) the Virtual Gallery, which builds on dancing sculptures in the Carnival and showcases AI-generated designs; (2) Virtual J’ouvert built on J’ouvert in Carnival with AI-generated J’ouvert characters specific; and (3) Virtual Mas which builds on the masquerade.
series	ACADIA
type	project
email
last changed	2024/04/17 13:58

_id	acadia21_564
id	acadia21_564
authors	Pellicano, Emily; Sturken, Carlo
year	2021
title	GPT-OA; Generative Pretrained Treatise--On Architecture
doi	https://doi.org/10.52842/conf.acadia.2021.564
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. 564-571.
summary	Technological advancements throughout the industrial era have created more efficient, more economical, and safer machines to aid – and often replace – human operations, continually altering our ways of knowledge and world making. Each industrial advancement radically changes social, political, economic, environmental, and even linguistic conditions. Currently upon us is artificial intelligence (AI); machine to human and machine to machine communications. Our investigation examines AI as a creative tool, instead of a machine for industry. Recent advancements in natural language processing have made artificially intelligent machines, specifi cally Generative Pretrained Transformers (GPT), a potential active partici- pant in a creative computational discourse. Our particular interest in GPT, and the core of this project, explores the role of language in machine learning and the role of the author and editor within a continually expanding network of agents in the construction of our collective environments.
series	ACADIA
type	field note
email
last changed	2023/10/22 12:06

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

_id	ijac202119406
id	ijac202119406
authors	Silva Dória, David Rodrigues; Ramaswami, Keshav; Claypool, Mollie; Retsin, Gilles
year	2021
title	Public parts, resocialized autonomous communal life
source	International Journal of Architectural Computing 2021, Vol. 19 - no. 4, 568–593
summary	Commoning embodies the product of social contracts and behaviors between groups of individuals. In thecase of social housing and the establishment of physical domains for life, commoning is an intersection of thesecontracts and the restrictions and policies that prohibit and allow them to occur within municipalities. Via aplatform-based project entitled Public Parts (2020), this article will also present positions on the reification ofthe common through a set of design methodologies and implementations of automation. This platform seeksto subvert typical platform models to decrease ownership, increase access, and produce a new form ofcommunal autonomous life amongst individuals that constitute the rapidly expanding freelance, work fromhome, and gig economies. Furthermore, this text investigates the consequences of merging domestic spacewith artificial intelligence by implementing machine learning to reconfigure spaces and program. Theproblems that arise from the deployment of machine learning algorithms involve issues of collection, usage,and ownership of data. Through the physical design of space, and a central AI which manages the platform andthe automated management of space, the core objective of Public Parts is to reify the common througharchitecture and collectively owned data.
keywords	Common, housing, platforms, reification, artificial intelligence, automation
series	journal
email
last changed	2024/04/17 14:29

_id	caadria2021_308
id	caadria2021_308
authors	Wang, Dasong and Snooks, Roland
year	2021
title	Intuitive Behavior - The Operation of Reinforcement Learning in Generative Design Processes
doi	https://doi.org/10.52842/conf.caadria.2021.1.101
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. 101-110
summary	The paper posits a novel approach for augmenting existing generative design processes to embed a greater level of design intention and create more sophisticated generative methodologies. The research presented in the paper is part of a speculative research project, Artificial Agency, that explores the operation of Machine Learning (ML) in generative design and robotic fabrication processes. By framing the inherent limitation of contemporary generative design approaches, the paper speculates on a heuristic approach that hybridizes a Reinforcement Learning based top-down evolutionary approach with bottom-up emergent generative processes. This approach is developed through a design experiment that establishes a topological field with intuitive global awareness of pavilion-scale design criteria. Theoretical strategies and technical details are demonstrated in the design experiment in regard to the translation of ML definitions within a generative design context as well as the encoding of design intentions. Critical reflections are offered in regard to the impacts, characteristics, and challenges towards the further development of the approach. The paper attempts to broaden the range and impact of Artificial Intelligence applications in the architectural discipline.
keywords	Machine Learning; Generative Design Process; Multi-Agent Systems; Reinforcement Learning
series	CAADRIA
email
last changed	2022/06/07 07:58

_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	caadria2021_446
id	caadria2021_446
authors	Zhou, Yifan and Park, Hyoung-June
year	2021
title	Sketch with Artificial Intelligence (AI) - A Multimodal AI Approach for Conceptual Design
doi	https://doi.org/10.52842/conf.caadria.2021.1.201
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. 201-210
summary	The goal of the research is to investigate an AI approach to assist architects with multimodal inputs (sketches and textual information) for conceptual design. With different textual inputs, the AI approach generates the architectural stylistic variations of a users initial sketch input as a design inspiration. A novel machine learning approach for the multimodal input system is introduced and compared to other approaches. The machine learning approach is performed through procedural training with the content curation of training data in order to control the fidelity of generated designs from the input and to manage their diversity. In this paper, the framework of the proposed AI approach is explained. Furthermore, the implementation of its prototype is demonstrated with various examples.
keywords	Artificial Intelligence; Stylistic Variations; Multimodal Input; Content Curation; Procedural Training
series	CAADRIA
email
last changed	2022/06/07 07:57

_id	caadria2021_088
id	caadria2021_088
authors	Batalle Garcia, Anna, Cebeci, Irem Yagmur, Vargas Calvo, Roberto and Gordon, Matthew
year	2021
title	Material (data) Intelligence - Towards a Circular Building Environment
doi	https://doi.org/10.52842/conf.caadria.2021.1.361
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. 361-370
summary	The integration of repurposed material in new construction products generates resiliency strategies that diminish the dependency on raw resources and reduce the CO2 emissions produced by their extraction, transportation, and manufacturing. This research emphasizes the need to expand preliminary data collation from pre-demolition sites to inform early design decisions. Material (data) Intelligence investigates how the merging of artificial intelligence and data analysis could have a crucial impact on achieving widespread material reuse. The first step consists of automating the process of detecting materials and construction elements from pre-demolition sites through drone photography and computer vision. The second part of the research links the resulting database with a computational design tool that can be integrated into construction software. This paper strengthens the potential of circular material flows in a digital paradigm and exposes the capability for constructing big data sets of reusable materials, digitally available, for sharing and organizing material harvesting.
keywords	computer vision; material database; automation; reclaimed material; digitalization
series	CAADRIA
email
last changed	2022/06/07 07:54

_id	ascaad2021_074
id	ascaad2021_074
authors	Belkaid, Alia; Abdelkader Ben Saci, Ines Hassoumi
year	2021
title	Human-Computer Interaction for Urban Rules Optimization
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. 603-613
summary	Faced with the complexity of manual and intuitive management of urban rules in architectural and urban design, this paper offers a collaborative and digital human-computer approach. It aims to have an Authorized Bounding Volume (ABV) which uses the best target values of urban rules. It is a distributed constraint optimization problem. The ABV Generative Model uses multi-agent systems. It offers an intelligent system of urban morphology able to transform the urban rules, on a given plot, into a morphological delimitation permitted by the planning regulations of a city. The overall functioning of this system is based on two approaches: construction and supervision. The first is conducted entirely by the machine and the second requires the intervention of the designer to collaborate with the machine. The morphological translation of urban rules is sometimes contradictory and may require additional external relevance to urban rules. Designer arbitration assists the artificial intelligence in accomplishing this task and solving the problem. The Human-Computer collaboration is achieved at the appropriate time and relies on the degree of constraint satisfaction with fitness function. The resolution of the distributed constraint optimization problem is not limited to an automatic generation of urban rules, but involves also the production of multiple optimal-ABV conditioned both by urban constraints as well as relevance, chosen by the designer.
series	ASCAAD
email
last changed	2021/08/09 13:13

_id	ecaade2021_035
id	ecaade2021_035
authors	Newton, David
year	2021
title	Visualizing Deep Learning Models for Urban Health Analysis
doi	https://doi.org/10.52842/conf.ecaade.2021.1.527
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. 527-536
summary	As humanity has become increasingly urbanized, physical and mental health problems have increased significantly among urban populations with a combined cost of treating these diseases estimated to be in the trillions of dollars. In parallel to these developments, a growing body of research suggests that the design of the built environment has significant correlations with both physical and mental health outcomes. This research, however, has been limited in its ability to make use of large remote sensing datasets to identify specific design features at the neighborhood scale that correlate with health outcomes. The development of methods that can efficiently find such correlations from ubiquitous remote sensing datasets, such as satellite images, would therefore allow researchers a greater level of insight into how specific urban planning and design features might relate to health. This research contributes knowledge on a novel mixed method workflow to address this issue.
keywords	Deep Learning; Urban Planning; Health; Artificial Intelligence; Remote Sensing
series	eCAADe
email
last changed	2022/06/07 07:58

_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_572
id	acadia21_572
authors	Rodrigues, Ricardo Cesar; Alzate-Martinez, Fábio A.; Escobar, Daniel; Mistry, Mayur
year	2021
title	Rendering Conceptual Design Ideas with Artificial Intelligence
doi	https://doi.org/10.52842/conf.acadia.2021.572
source	ACADIA 2021: Realignments: Toward Critical Computation [Proceedings of the 41st Annual Conference of the Association of Computer Aided Design in Architecture (ACADIA) ISBN 979-8-986-08056-7]. Online and Global. 3-6 November 2021. edited by S. Parascho, J. Scott, and K. Dörfler. 572-575.
summary	This paper documents a data-driven approach to a conceptual rendering workflow with Artificial Intelligence (AI) models. This work originates from the workshop ‘Intro to AI for Architectural Design Explorations’ lectured by the authors Mayur Mistry and Daniel Escobar, during the event ‘Inclusive FUTURES 2021’ at the Digital Futures platform. The observations reflect about the applicability of machine-augmented conceptual design. As a common practice in the fi eld, architects start designing their buildings by sketching their ideas, this is a process that attempts to translate a concept into a spatial and aesthetic solution. Nevertheless, the design process is an iterative and time-consuming task. For this reason, we must experiment new methods that can potentially enhance architectural practice.
series	ACADIA
type	field note
email
last changed	2023/10/22 12:06

For more results click below: