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 625

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

_id	ecaade2018_399
id	ecaade2018_399
authors	Cutellic, Pierre
year	2018
title	UCHRON - An Event-Based Generative Design Software Implementing Fast Discriminative Cognitive Responses from Visual ERP BCI
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. 131-138
doi	https://doi.org/10.52842/conf.ecaade.2018.2.131
summary	This research aims at investigating BCI technologies in the broad scope of CAAD applications exploiting early visual cognition in computational design. More precisely, this paper will describe the investigation of key BCI and ML components for the implementation and development of a software supporting this research : Uchron. It will be organised as follows. Firstly, it will introduce the pursued interest and contribution that visual-ERP EEG based BCI application for Generative Design may provide through a synthetic review of precedents and BCI technology. Secondly, selected BCI components will be described and a methodology will be presented to provide an appropriate framework for a CAAD software approach. This section main focus is on the processing component of the BCI. It distinguishes two key aspects of discrimination and generation in its design and proposes a new model based on GAN for modulated adversarial design. Emphasis will be made on the explicit use of inference loops integrating fast human cognitive responses and its individual capitalisation through time in order to reflect towards the generation of design and architectural features.
keywords	Human Computer Interaction; Neurodesign; Generative Design; Design Computing and Cognition; Machine Learning
series	eCAADe
email
last changed	2022/06/07 07:56

_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
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
doi	https://doi.org/10.52842/conf.ecaade.2018.1.095
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
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
doi	https://doi.org/10.52842/conf.caadria.2018.2.237
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_198
id	caadria2018_198
authors	Reinhardt, Dagmar, Candido, Christhina, Cabrera, Densil, Wozniak-O'Connor, Dylan, Watt, Rodney, Bickerton, Chris, Titchkosky, Ninotschka and Houda, Maryam
year	2018
title	Onsite Robotic Fabrication for Flexible Workspaces - Towards Design and Robotic Fabrication of an Integrated Responsive Ceiling System for A Workspace Environment
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. 59-68
doi	https://doi.org/10.52842/conf.caadria.2018.1.059
summary	Open, flexible workspaces were introduced decades ago, but architectural design approaches to ceiling systems have not changed substantially. This paper discusses the development of strategies and prototypes for a lightweight, integrated ceiling structure that is robotically woven. Through geometrically complex, fibre-reinforced building elements that are produced onsite, a new distribution system for data and light can be provided and support individual and multi-group collaborations in an contemporary open-plan office for maximum flexibility. The paper introduces applied design research with case studies that test robotic weaving on an architectural ceiling. The second part contextualises the presented work by linking it to workspace scenarios and an on-site robotic process with a resulting data distribution that is designed to produce degrees of freedom for high flexibility in use, allowing occupants to organise the workspace layout autonomously so that workflow constellations in different teams can be adequately expressed through space. The paper concludes with a discussion of a framework for robotic methods developed for the carbon-fibre overhead weaving processes, followed by conclusions and outlook towards future potentials.
keywords	open collaborative workspace; robotic onsite weaving; carbon fiber; integrated ceiling systems
series	CAADRIA
email
last changed	2022/06/07 08:00

_id	ecaade2018_164
id	ecaade2018_164
authors	Chang, Mei-Chih, Buš, Peter, Tartar, Ayça, Chirkin, Artem and Schmitt, Gerhard
year	2018
title	Big-Data Informed Citizen Participatory Urban Identity Design
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. 669-678
doi	https://doi.org/10.52842/conf.ecaade.2018.2.669
summary	The identity of an urban environment is important because it contributes to self-identity, a sense of community, and a sense of place. However, under present-day conditions, the identities of expanding cities are rapidly deteriorating and vanishing, especially in the case of Asian cities. Therefore, cities need to build their urban identity, which includes the past and points to the future. At the same time, cities need to add new features to improve their livability, sustainability, and resilience. In this paper, using data mining technologies for various types of geo-referenced big data and combine them with the space syntax analysis for observing and learning about the socioeconomic behavior and the quality of space. The observed and learned features are identified as the urban identity. The numeric features obtained from data mining are transformed into catalogued levels for designers to understand, which will allow them to propose proper designs that will complement or improve the local traditional features. A workshop in Taiwan, which focuses on a traditional area, demonstrates the result of the proposed methodology and how to transform a traditional area into a livable area. At the same time, we introduce a website platform, Quick Urban Analysis Kit (qua-kit), as a tool for citizens to participate in designs. After the workshop, citizens can view, comment, and vote on different design proposals to provide city authorities and stakeholders with their ideas in a more convenient and responsive way. Therefore, the citizens may deliver their opinions, knowledge, and suggestions for improvements to the investigated neighborhood from their own design perspective.
keywords	Urban identity; unsupervised machine learning; Principal Component Analysis (PCA); citizen participated design; space syntax
series	eCAADe
email
last changed	2022/06/07 07:56

_id	ecaade2018_301
id	ecaade2018_301
authors	Cocho-Bermejo, Ana, Birgonul, Zeynep and Navarro-Mateu, Diego
year	2018
title	Adaptive & Morphogenetic City Research Laboratory
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. 659-668
doi	https://doi.org/10.52842/conf.ecaade.2018.2.659
summary	"Smart City" business model is guiding the development of future metropolises. Software industry sales to town halls for city management services efficiency improvement are, these days, a very pro?table business. Being the model decided by the industry, it can develop into a dangerous situation in which the basis of the new city design methodologies is decided by agents outside academia expertise. Drawing on complex science, social physics, urban economics, transportation theory, regional science and urban geography, the Lab is dedicated to the systematic analysis of, and theoretical speculation on, the recently coined "Science of Cities" discipline. On the research agenda there are questions arising from the synthesis of architecture, urban design, computer science and sociology. Collaboration with citizens through inclusion and empowerment, and, relationships "City-Data-Planner-Citizen" and "Citizen-Design-Science", configure Lab's methodology provoking a dynamic responsive process of design that is yet missing on the path towards the real responsive city.
keywords	Smart City; Morphogenetic Urban Design; Internet of Things; Building Information Modelling; Evolutionary Algorithms; Machine Learning & Artificial Intelligence
series	eCAADe
email
last changed	2022/06/07 07:56

_id	caadria2018_215
id	caadria2018_215
authors	Raspall, Felix and Banon, Carlos
year	2018
title	3D Printing Architecture: Towards Functional Space Frames
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. 215-224
doi	https://doi.org/10.52842/conf.caadria.2018.1.215
summary	In architecture, the use of Additive Manufacturing (AM) technologies has been primarily limited to the production of scale models. Its application for functional buildings components has been typically undermined by the long production time, elevated cost to manufacture parts and the low mechanical properties of 3D printed components. As AM becomes faster, cheaper and stronger, opportunities for architectures that make creative use of AM to produce functional architectural pieces are emerging. In this paper, we propose and discuss the application of AM in complex space frames and the theoretical and practical implications. Three built projects by the authors support our hypothesis that AM has a clear application in architecture and that space frames constitutes a promising structural typology. In addition, we investigate how AM can be used to resolve architectural systems beyond structure and enclosure, such as data and power transmission. The paper presents background research and our contribution to the digital design tools, the manufacturing and assembly processes, and the analysis of the performances of the building components and the final built pieces.
keywords	Additive Manufacturing; Digital Design; Space frames
series	CAADRIA
email
last changed	2022/06/07 08:00

_id	caadria2018_000
id	caadria2018_000
authors	T. Fukuda, W. Huang, P. Janssen, K. Crolla, S. Alhadidi (eds.)
year	2018
title	CAADRIA 2018: Learning, Prototyping and Adapting, Volume 1
source	Learning, Adapting and Prototyping - Proceedings of the 23rd CAADRIA Conference - Volume 1, Tsinghua University, Beijing, China, 17-19 May 2018, 578 p.
doi	https://doi.org/10.52842/conf.caadria.2018.1
summary	Rapidly evolving technologies are increasingly shaping our societies as well as our understanding of the discipline of architecture. Computational developments in fields such as machine learning and data mining enable the creation of learning networks that involve architects alongside algorithms in developing new understanding. Such networks are increasingly able to observe current social conditions, plan, decide, act on changing scenarios, learn from the consequences of their actions, and recognize patterns out of complex activity networks. While digital technologies have already enabled architecture to transcend static physical boxes, new challenges of the present and visions for the future continue to call for both innovative responses integrating emerging technologies into experimental architectural practice and their critical reflection. In this process, the capability of adapting to complex social and environmental challenges through learning, prototyping and verifying solution proposals in the context of rapidly shifting realities has become a core challenge to the architecture discipline. Supported by advancing technologies, architects and researchers are creating new frameworks for digital workflows that engage with new challenges in a variety of ways. Learning networks that recognize patterns from massive data, rapid prototyping systems that flexibly iterate innovative physical solutions, and adaptive design methods all contribute to a flexible and networked digital architecture that is able to learn from both past and present to evolve towards a promising vision of the future.
series	CAADRIA
last changed	2022/06/07 07:49

_id	caadria2018_001
id	caadria2018_001
authors	T. Fukuda, W. Huang, P. Janssen, K. Crolla, S. Alhadidi (eds.)
year	2018
title	CAADRIA 2018: Learning, Prototyping and Adapting, Volume 2
source	Learning, Adapting and Prototyping - Proceedings of the 23rd CAADRIA Conference - Volume 2, Tsinghua University, Beijing, China, 17-19 May 2018, 610 p.
doi	https://doi.org/10.52842/conf.caadria.2018.2
summary	Rapidly evolving technologies are increasingly shaping our societies as well as our understanding of the discipline of architecture. Computational developments in fields such as machine learning and data mining enable the creation of learning networks that involve architects alongside algorithms in developing new understanding. Such networks are increasingly able to observe current social conditions, plan, decide, act on changing scenarios, learn from the consequences of their actions, and recognize patterns out of complex activity networks. While digital technologies have already enabled architecture to transcend static physical boxes, new challenges of the present and visions for the future continue to call for both innovative responses integrating emerging technologies into experimental architectural practice and their critical reflection. In this process, the capability of adapting to complex social and environmental challenges through learning, prototyping and verifying solution proposals in the context of rapidly shifting realities has become a core challenge to the architecture discipline. Supported by advancing technologies, architects and researchers are creating new frameworks for digital workflows that engage with new challenges in a variety of ways. Learning networks that recognize patterns from massive data, rapid prototyping systems that flexibly iterate innovative physical solutions, and adaptive design methods all contribute to a flexible and networked digital architecture that is able to learn from both past and present to evolve towards a promising vision of the future.
series	CAADRIA
last changed	2022/06/07 07:49

_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	acadia18_176
id	acadia18_176
authors	Bidgoli, Ardavan; Veloso,Pedro
year	2018
title	DeepCloud. The Application of a Data-driven, Generative Model in Design
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. 176-185
doi	https://doi.org/10.52842/conf.acadia.2018.176
summary	Generative systems have a significant potential to synthesize innovative design alternatives. Still, most of the common systems that have been adopted in design require the designer to explicitly define the specifications of the procedures and in some cases the design space. In contrast, a generative system could potentially learn both aspects through processing a database of existing solutions without the supervision of the designer. To explore this possibility, we review recent advancements of generative models in machine learning and current applications of learning techniques in design. Then, we describe the development of a data-driven generative system titled DeepCloud. It combines an autoencoder architecture for point clouds with a web-based interface and analog input devices to provide an intuitive experience for data-driven generation of design alternatives. We delineate the implementation of two prototypes of DeepCloud, their contributions, and potentials for generative design.
keywords	full paper, design tools software computing + gaming, ai & machine learning, generative design, autoencoders
series	ACADIA
type	paper
email
last changed	2022/06/07 07:52

_id	ecaade2018_210
id	ecaade2018_210
authors	Ezzat, Mohammed
year	2018
title	A Computational Tool for Mapping the Users' Urban Cognition - A Framework and a Representation for the Evolutionary Optimization of the Fuzzy Binary Relation between the Urban Conceptions of "Us" and "Others"
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. 667-676
doi	https://doi.org/10.52842/conf.ecaade.2018.1.667
summary	The paper proposes a computational tool for simulating the users' urban cognitive systems, or more specifically the long-term memory associated with the knowledge of urbanism and its related urban visual features. The tool builds on our comprehensive theory of Urbanism, which presents a monolithic, structured, comprehensive, professional conception of Urbanism based on which any relativistic users' urban conceptions could be predicted as a restructuring of the professional conception. These versatile relativistic conceptions would emerge based on a nurturing environment, which is a conception of the empirical/anthropological collected data of the intended users' reflections against their preferred constructed urban environments. Once the users' conceptions of Urbanism are formulated, which is the first phase of the simulation, the users' impressions against any examined urban constructs are attainable, which is the second phase of the simulation. The two phases, the framework, would be monolithically represented by a proposed novel cellular graph. The proposed computational tool is thought of as a robust technique for the computational incorporation of the users' urban identity, and some of its constituents could be considered as a needed common platform of communication for a successful Human-Computer interaction in the field of urban analysis/design.
keywords	a comprehensive model of Urbanism; a default professional conception of Urbanism; the relativistic users' conceptions of Urbanism ; recognized extracted urban features ; the users' urban identity; A comprehensive theory for space syntax:
series	eCAADe
email
last changed	2022/06/07 07:55

_id	caadria2018_052
id	caadria2018_052
authors	Fung, Enrica and Crolla, Kristof
year	2018
title	Choreographed Architecture - Body-Spatial Exploration
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. 101-110
doi	https://doi.org/10.52842/conf.caadria.2018.1.101
summary	This paper presents a design-methodological case study that looks into the practical expansion of conventional conceptual architectural design media by incorporating contemporary technology of motion capture. It discusses challenges of integrating dance movement as a real-time input parameter for architectural design that aims at translating body motion into space. The paper consists of four parts, beginning with a historic background overview of scientists, physiologists, artists, choreographers, and architects who have attempted capturing body motion and turning the motion into space. The second part of the paper discusses the iterative development of the 'Dance Machine' as a methodological tool for the integration of motion capture into conceptual architectural design. Thirdly, the paper discusses tested design applications of the 'Dance Machine' by looking at two sited applications. Finally, the overall methodology is critically assessed and discussed in the light of continuous development of creative applications of motion capturing technology. The paper concludes by highlighting the architectural potential found in specific qualities of dance and by advocating for a broader palette of tools, techniques, and input methods for the conceptual design of architecture.
keywords	Choreographed architecture; Motion capture; Conceptual design media; Space design; Human body
series	CAADRIA
email
last changed	2022/06/07 07:50

_id	acadia20_382
id	acadia20_382
authors	Hosmer, Tyson; Tigas, Panagiotis; Reeves, David; He, Ziming
year	2020
title	Spatial Assembly with Self-Play Reinforcement Learning
source	ACADIA 2020: Distributed Proximities / Volume I: Technical Papers [Proceedings of the 40th Annual Conference of the Association of Computer Aided Design in Architecture (ACADIA) ISBN 978-0-578-95213-0]. Online and Global. 24-30 October 2020. edited by B. Slocum, V. Ago, S. Doyle, A. Marcus, M. Yablonina, and M. del Campo. 382-393.
doi	https://doi.org/10.52842/conf.acadia.2020.1.382
summary	We present a framework to generate intelligent spatial assemblies from sets of digitally encoded spatial parts designed by the architect with embedded principles of prefabrication, assembly awareness, and reconfigurability. The methodology includes a bespoke constraint-solving algorithm for autonomously assembling 3D geometries into larger spatial compositions for the built environment. A series of graph-based analysis methods are applied to each assembly to extract performance metrics related to architectural space-making goals, including structural stability, material density, spatial segmentation, connectivity, and spatial distribution. Together with the constraint-based assembly algorithm and analysis methods, we have integrated a novel application of deep reinforcement (RL) learning for training the models to improve at matching the multiperformance goals established by the user through self-play. RL is applied to improve the selection and sequencing of parts while considering local and global objectives. The user’s design intent is embedded through the design of partial units of 3D space with embedded fabrication principles and their relational constraints over how they connect to each other and the quantifiable goals to drive the distribution of effective features. The methodology has been developed over three years through three case study projects called ArchiGo (2017–2018), NoMAS (2018–2019), and IRSILA (2019-2020). Each demonstrates the potential for buildings with reconfigurable and adaptive life cycles.
series	ACADIA
type	paper
email
last changed	2023/10/22 12:06

_id	acadia18_156
id	acadia18_156
authors	Huang, Weixin; Zheng, Hao
year	2018
title	Architectural Drawings Recognition and Generation through Machine Learning
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
doi	https://doi.org/10.52842/conf.acadia.2018.156
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	acadia18_232
id	acadia18_232
authors	Kilian, Axel
year	2018
title	The Flexing Room Architectural Robot. An Actuated Active-Bending Robotic Structure using Human Feedback
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. 232-241
doi	https://doi.org/10.52842/conf.acadia.2018.232
summary	Advances in autonomous control of object-scale robots, both anthropomorphic and vehicular, are posing new human–machine interface challenges. In architecture, very few examples of autonomous inhabitable robotic architecture exist. A number of factors likely contribute to this condition, among them the scale and cost of architectural adaptive systems, but on a more fundamental conceptual level also the questions of how architectural robots would communicate with their human inhabitants. The Flexing Room installation is a room-sized actuated active-bending skeleton structure. It uses rudimentary social feedback by counting people to inform its behavior in the form of actuated poses of the room enclosure. An operational full-scale prototype was constructed and tested. To operate it no geometric-based simulation was used; the only communication between computer and structure was in sending values for the air pressure settings and in gathering sensor feedback. The structure’s physical state was resolved through the embodied computation of its interconnected parts, and the people-counting sensor feedback influences its next action. Future work will explore the development of learning processes to improve the human–machine coexistence in space.
keywords	full paper, fabrication & robotics, non-production robotics, materials/adaptive systems, flexible structures
series	ACADIA
type	paper
email
last changed	2022/06/07 07:52

_id	ecaade2018_233
id	ecaade2018_233
authors	Kontiza, Iacovina, Spathi, Theodora and Bedarf, Patrick
year	2018
title	Spatial Graded Patterns - A case study for large-scale differentiated space frame structures utilising high-speed 3D-printed joints
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. 39-46
doi	https://doi.org/10.52842/conf.ecaade.2018.2.039
summary	Geometric differentiation is no longer a production setback for industrial grade architectural components. This paper introduces a design and fabrication workflow for non-repetitive large-scale space frame structures composed of custom-manufactured nodes, which exploits the advantages of latest advancements in 3D-printing technology. By integrating design, fabrication and material constraints into a computational methodology, the presented approach addresses additive manufacturing of functional industry-grade parts in short time, high speed and low cost. The resulting case study of a 4.5 x 4.5 x 2.5 m lightweight kite structure comprises 1380 versatile fully-customised connectors and outlines the manifold potential of additive manufacturing for architecture much bigger than the machine built space. First, after briefly introducing space frames in architecture, this paper discusses the computational framework of generating irregular space frames and parametric joint design. Second, it examines the advantages of MJF printing in conjunction with integrating smart sequencing details for the following assembly process. Finally, a conclusive outlook is given on improvements and further developments for bespoke 3D-printed space frame structures.
keywords	3D-printing; Multi-Jet Fusion; Space Frame; Graded Subdivision
series	eCAADe
email
last changed	2022/06/07 07:51

_id	sigradi2023_243
id	sigradi2023_243
authors	O. Oporto, Italo, Martínez Arias, Andrea and Villouta Gutierrez, Daniela
year	2023
title	Iluminación y configuración espacial: Una metodología de análisis íntegra: El caso del Servicio de Psiquiatría Guillermo Grant Benavente en Concepción, Chile.”
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. 385–396
summary	Our everyday environment plays a significant role in shaping our social and emotional interactions. It has been empirically evidenced that natural daylight mitigates depression, insomnia, and other disorders (Weber, 2022). This resonates with the fact that individuals with disrupted circadian rhythms are more susceptible to mental health perturbations (Menculini et al., 2018). The current investigation delves into the correlation between luminosity and spatial configuration within the Guillermo Grantt Benavente Psychiatry Service in Concepción, Chile. The contention is that proficient spatial connectivity and exposure to natural daylight can potentially enhance therapeutic dimensions. The overarching objective is to comprehend this nexus for formulating an architectural design methodology. Specific objectives encompass: 1. Defining the communal spaces under scrutiny; 2. Analyzing luminosity and spatial attributes. The methodological approach encompasses a hybrid framework encompassing interviews, spatial analysis, and illuminance measurements. An intricate interrelationship among preferred spaces, illuminance, and spatial characteristics is anticipated.
keywords	Environment, Lighting, Space Syntax, Mental health, Psychiatric residence
series	SIGraDi
email
last changed	2024/03/08 14:07

_id	caadria2018_070
id	caadria2018_070
authors	Pandjaitan, Poltak
year	2018
title	Architectonics of Crystal Space
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. 183-192
doi	https://doi.org/10.52842/conf.caadria.2018.1.183
summary	The basic research project addresses the question of spatiality in architecture and how to define space by geometrically mediating between spatialities. Based on interdisciplinary explorations of crystal structures and their specific constitutions, spatial paradigms are examined and implemented in the algebraic framework of crystals. The goal of the ongoing research is not to resemble and mimic these emergent crystal arrangements. It is only about the general principle of these formation processes particularly with regard to aperiodic quasicrystals. Through the purposive abstraction and translation of spatialities combined with the notion of crystals as a code like structure, it is possible to scrutinize the meaning of space in order to create space for new architectonical articulations.
keywords	crystal; quasicrystal; lattice; aperiodic; architectonics
series	CAADRIA
email
last changed	2022/06/07 08:00

_id	caadria2018_310
id	caadria2018_310
authors	Qiu, Lili, Li, Yuan and Rao, Jintong
year	2018
title	The Evolution of Kulangsu's Urban Morphology in its Period of Public Settlement, Based on Space Syntax
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. 421-430
doi	https://doi.org/10.52842/conf.caadria.2018.2.421
summary	Heritage protection entails the integrated construction of conservation and collective memory based on historical authenticity. Therefore, the study of history is essential for accomplishing the task of heritage conservation. This article applies a space syntax methodology to explore how urban morphology evolved at a heritage site on Kulangsu Island. The analysis reveals the characteristic features of urban morphology and offers suggestions for optimizing the historical space of world heritage. The effectiveness and limitations of the research are also debated and substantiated.
keywords	Urban morphology; Kulangsu; Historical evolution; Space syntax; GIS
series	CAADRIA
email
last changed	2022/06/07 08:00

For more results click below: