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	acadia17_552
id	acadia17_552
authors	Sjoberg, Christian; Beorkrem, Christopher; Ellinger, Jefferson
year	2017
title	Emergent Syntax: Machine Learning for the Curation of Design Solution Space
source	ACADIA 2017: DISCIPLINES & DISRUPTION [Proceedings of the 37th Annual Conference of the Association for Computer Aided Design in Architecture (ACADIA) ISBN 978-0-692-96506-1] Cambridge, MA 2-4 November, 2017), pp. 552- 561
doi	https://doi.org/10.52842/conf.acadia.2017.552
summary	The expanding role of computational models in the process of design is producing exponential growth in parameter spaces. As designers, we must create and implement new methods for searching these parameter spaces, considering not only quantitative optimization metrics but also qualitative features. This paper proposes a methodology that leverages the pattern modeling properties of artificial neural networks to capture designers' inexplicit selection criteria and create user-selection-based fitness functions for a genetic solver. Through emulation of learned selection patterns, fitness functions based on trained networks provide a method for qualitative evaluation of designs in the context of a given population. The application of genetic solvers for the generation of new populations based on the trained network selections creates emergent high-density clusters in the parameter space, allowing for the identification of solutions that satisfy the designer’s inexplicit criteria. The results of an initial user study show that even with small numbers of training objects, a search tool with this configuration can begin to emulate the design criteria of the user who trained it.
keywords	design methods; information processing; AI; machine learning; generative system
series	ACADIA
email
last changed	2022/06/07 07:56

_id	caadria2024_87
id	caadria2024_87
authors	Li, Jiongye and Stouffs, Rudi
year	2024
title	Distribution of Carbon Storage and Potential Strategies to Enhance Carbon Sequestration Capacity in Singapore: A Study Based on Machine Learning Simulation and Geospatial Analysis
source	Nicole Gardner, Christiane M. Herr, Likai Wang, Hirano Toshiki, Sumbul Ahmad Khan (eds.), ACCELERATED DESIGN - Proceedings of the 29th CAADRIA Conference, Singapore, 20-26 April 2024, Volume 2, pp. 89–98
doi	https://doi.org/10.52842/conf.caadria.2024.2.089
summary	The expansion of urbanization leads to significant changes in land use, consequently affecting carbon storage. This research aims to investigate the carbon loss due to land use alterations and proposes strategies for mitigation. Utilizing existing land use data from 2017 and 2022, along with simulated data for 2025 generated by an ANN model and Cellular Automata, we identified changes in land use. These changes were then correlated with variations in carbon storage, both gains and losses. Our findings reveal a significant loss of 36,859 metric tons of carbon storage from 2017 to 2022. The projection for 2025 estimates a further reduction, reaching a total loss of 83,409 metric tons. By employing the LISA method, we identified that low-carbon storage zones are concentrated in the southeast region of the research site. By overlaying these zones with areas of carbon storage loss, we pinpointed regions severely affected by carbon depletion. Consequently, we propose that mitigation strategies should be imperatively implemented in these identified areas to counteract the trend of carbon storage loss. This approach offers urban planners a solution to identify areas experiencing carbon storage decline. Moreover, our research methodology provides a novel framework for scholars studying similar carbon issues.
keywords	land use and land cover (LULC) changes, simulated LULC, machine learning model, carbon storage changes, GIS
series	CAADRIA
email
last changed	2024/11/17 22:05

_id	acadia17_426
id	acadia17_426
authors	Moorman, Andrew
year	2017
title	Pattern Making and Learning: Non-Routine Practices in Generative Design
source	ACADIA 2017: DISCIPLINES & DISRUPTION [Proceedings of the 37th Annual Conference of the Association for Computer Aided Design in Architecture (ACADIA) ISBN 978-0-692-96506-1] Cambridge, MA 2-4 November, 2017), pp. 426- 435
doi	https://doi.org/10.52842/conf.acadia.2017.426
summary	We now witness an upsurge in mainstream generative design tools fortified by simulation that speed up the concealed linear synthesis of optimized design alternatives. In pursuit of optimality, these tools saturate local machines or cloud servers with analysis and design iteration data, only to discard it once the procedure has concluded. Largely absent, however, are tools for an active, adaptive relationship with design exploration and the reuse of corresponding design data and metadata. In Pattern Making and Pattern Learning, we propose that these characteristics are mutually beneficial. This paper presents a series of revisions to the optimization framework for routine design synthesis that examine a potential symbiosis between the production of large datasets (big data) and non-routine practices of making in design. Our engagement with iterative design exercises is twofold: as a supply of computer-generated design information to foster user intuition and explore the design space on non-objective terms, and as a supply of human-generated design information to learn artifacts of user preference in the interest of design software personalization. These concepts are applied to the generation of functionally graded patterning in chair design, combining methods of physical production with programmable sheet material behavior through a custom interactive synthesis framework.
keywords	design methods; information processing; ai & machine learning; simulation & optimization; generative system
series	ACADIA
email
last changed	2022/06/07 07:58

_id	acadia17_474
id	acadia17_474
authors	Peng, Wenzhe; Zhang, Fan; Nagakura, Takehiko
year	2017
title	Machines’ Perception of Space: Employing 3D Isovist Methods and a Convolutional Neural Network in Architectural Space Classification
source	ACADIA 2017: DISCIPLINES & DISRUPTION [Proceedings of the 37th Annual Conference of the Association for Computer Aided Design in Architecture (ACADIA) ISBN 978-0-692-96506-1] Cambridge, MA 2-4 November, 2017), pp. 474- 481
doi	https://doi.org/10.52842/conf.acadia.2017.474
summary	Simple and common architectural elements can be combined to create complex spaces. Different spatial compositions of elements define different spatial boundaries, and each produces a unique local spatial experience to observers inside the space. Therefore an architectural style brings about a distinct spatial experience. While multiple representation methods are practiced in the field of architecture, there lacks a compelling way to capture and identify spatial experiences. Describing an observer’s spatial experiences quantitatively and efficiently is a challenge. In this paper, we propose a method that employs 3D isovist methods and a convolutional neural network (CNN) to achieve recognition of local spatial compositions. The case studies conducted validate that this methodology works well in capturing and identifying local spatial conditions, illustrates the pattern and frequency of their appearance in designs, and indicates peculiar spatial experiences embedded in an architectural style. The case study used small designs by Mies van der Rohe and Aldo van Eyck. The contribution of this paper is threefold. First, it introduces a sampling method based on 3D Isovist that generates a 2D image that can be used to represent a 3D space from a specific observation point. Second, it employs a CNN model to extract features from the sampled images, then classifies their corresponding space. Third, it demonstrates a few case studies where this space classification method is applied to different architectural styles.
keywords	design methods; information processing; AI; machine learning; computer vision; representation
series	ACADIA
email
last changed	2022/06/07 08:00

_id	ecaade2017_269
id	ecaade2017_269
authors	Rahmani Asl, Mohammad, Das, Subhajit, Tsai, Barry, Molloy, Ian and Hauck, Anthony
year	2017
title	Energy Model Machine (EMM) - Instant Building Energy Prediction using Machine Learning
source	Fioravanti, A, Cursi, S, Elahmar, S, Gargaro, S, Loffreda, G, Novembri, G, Trento, A (eds.), ShoCK! - Sharing Computational Knowledge! - Proceedings of the 35th eCAADe Conference - Volume 2, Sapienza University of Rome, Rome, Italy, 20-22 September 2017, pp. 277-286
doi	https://doi.org/10.52842/conf.ecaade.2017.2.277
summary	In the process of building design, energy performance is often simulated using physical principles of thermodynamics and energy behaviour using elaborate simulation tools. However, energy simulation is computationally expensive and time consuming process. These drawbacks limit opportunities for design space exploration and prevent interactive design which results in environmentally inefficient buildings. In this paper we propose Energy Model Machine (EMM) as a general and flexible approximation model for instant energy performance prediction using machine learning (ML) algorithms to facilitate design space exploration in building design process. EMM can easily be added to design tools and provide instant feedback for real-time design iterations. To demonstrate its applicability, EMM is used to estimate energy performance of a medium size office building during the design space exploration in widely used parametrically design tool as a case study. The results of this study support the feasibility of using machine learning approaches to estimate energy performance for design exploration and optimization workflows to achieve high performance buildings.
keywords	Machine Learning; Artificial Neural Networks; Boosted Decision Tree; Building Energy Performance; Parametric Modeling and Design; Building Performance Optimization
series	eCAADe
email
last changed	2022/06/07 08:00

_id	ijac201715101
id	ijac201715101
authors	Bieg, Kory and Clay Odom
year	2017
title	Lumifoil and Tschumi: Virtual projections and architectural interventions
source	International Journal of Architectural Computing vol. 15 - no. 1, 6-17
summary	This article introduces the theoretical and technical framework for the design of a temporary rooftop canopy on the red generator—one of the buildings designed by Bernard Tschumi for the Florida International University School of Architecture. The project, Lumifoil, was designed using both top-down and bottom-up computational techniques, including surface modeling via projected geometries and scripted cellular subdivisions and assemblies. Lumifoil attempts to synthesize these two often-conflicting design approaches into a generative design process which leverages context, form, surface, and structure as affective and effective actors. Lumifoil is the result of a design methodology which is both active and reactive to existing conditions of the site and new opportunities afforded by the program. It is contextual in its top-down relationship to Tschumi’s existing building and theory, generative in how details emerge bottom-up through scripts which lack any reference to site, and emergent in the resulting synthetic processes and effects which are produced. Through this methodological development, the project both tracks and responds to popular architectural theory and design from the mid-1990s to today. The theoretical underpinnings of the project build upon the idea that the actual (the real-life physical manifestation of matter) and the virtual (the potential for an object to be) are two constantly shifting paradigms in which design processes can intervene to help develop an architectural solution from a range of possibilities. The technical aspect of the project includes the collaborative workflow between the architecture offices of OTA+ and studio MODO with Arup Engineers to resolve structural issues using parametric modeling tools and structural analysis software. The final project is entirely parametric and fabrication is completely automated.
keywords	Tschumi, Parametric, Installation, Generative, Projection
series	other
type	normal paper
email
last changed	2019/08/02 08:16

_id	ecaade2017_134
id	ecaade2017_134
authors	Del Signore, Marcella
year	2017
title	pneuSENSE - Transcoding social ecologies
source	Fioravanti, A, Cursi, S, Elahmar, S, Gargaro, S, Loffreda, G, Novembri, G, Trento, A (eds.), ShoCK! - Sharing Computational Knowledge! - Proceedings of the 35th eCAADe Conference - Volume 2, Sapienza University of Rome, Rome, Italy, 20-22 September 2017, pp. 537-544
doi	https://doi.org/10.52842/conf.ecaade.2017.2.537
summary	Cities are continuously produced through entropic processes that mediate between complex networked systems and the immediacy urban life. Emergent media technologies inform new relationships between information and matter, code and space to redefine new urban ecosystems. Modes of perceiving, experiencing and inhabiting cities are radically changing along with a radical transformation of the tools that we use to design. Cities as complex and systemic organisms require approaches that engage new multi-scalar strategies to connect the physical layer with the system of networked ecologies. This paper aims at investigating emerging and novel forms of reading and producing urban spaces reimagining the physical city through intelligent and mediated processes. Through data agency and responsive urban processes, the design methodology explored the materialization of a temporary pneumatic structure and membrane that tested material performance through fabrication and sensing practices through the pneuSENSE project developed in July 2016 in New York at the Brooklyn Navy Yard during the 'HyperCities' IaaC- Institute for Advanced Architecture of Catalonia - Global Summer School.
keywords	responsive urban processes; data agency ; reciprocity between micro (body) and macro (environment); dynamics of social ecologies; mapped-environment
series	eCAADe
email
last changed	2022/06/07 07:55

_id	cf2017_630
id	cf2017_630
authors	Muehlbauer, Manuel; Song, Andy; Burry, Jane
year	2017
title	Towards Intelligent Control in Generative Design
source	Gülen Çagdas, Mine Özkar, Leman F. Gül and Ethem Gürer (Eds.) Future Trajectories of Computation in Design [17th International Conference, CAAD Futures 2017, Proceedings / ISBN 978-975-561-482-3] Istanbul, Turkey, July 12-14, 2017, pp. 630-647.
summary	This position paper proposes and defines the nature of a framework, which explores ways of integrating control system (CS) with machine intelligence for generative design (GD). This paper elaborates about the implications of and the potential for impact on GD. The framework described in this work can be used as an active tool to drive design processes and support decision making process in early stages of architectural design. This type of system can be either automated in nature or adaptive to regular user input as part of interactive design mechanisms. The module of CS in the framework would allow additional guidance during design and therefore reduce the need of manual input to enable a semi-automated design practice for lengthy generative processes. This study on GD reveals emergent properties of the framework, for example the introduction of intelligent control allows guidance of GD to meet specified performance criteria and intended aesthetic expressions with reduced need for user interaction.
keywords	Semi-Automated Design, Evolutionary Architecture, Generative Design, Architectural Optimisation, Artificial Intelligence
series	CAAD Futures
email
last changed	2017/12/01 14:38

_id	caadria2017_016
id	caadria2017_016
authors	Lee, Ju Hyun, Ostwald, Michael J. and Yu, Rongrong
year	2017
title	Investigating Visibility Properties in the Design of Aged-Care Facilities
source	P. Janssen, P. Loh, A. Raonic, M. A. Schnabel (eds.), Protocols, Flows, and Glitches - Proceedings of the 22nd CAADRIA Conference, Xi'an Jiaotong-Liverpool University, Suzhou, China, 5-8 April 2017, pp. 365-374
doi	https://doi.org/10.52842/conf.caadria.2017.365
summary	This paper uses a Space Syntax approach - a computational and mathematical method using graph-based measurements - to undertake a comparative assessment of the visibility properties of three architectural plans with unusual spatial requirements. Specifically, the method is used to compare the spatio-visual properties of an idealised plan for a residential aged-care facility with the actual plans used for two facilities. The purpose of this analysis is to begin to examine the ways in which syntactical values and isovist properties can be used to capture spatial and social characteristics of plans designed for the physical and cognitive needs of an ageing populace. The application of this approach seeks to support a better understanding of the relationship between spaces and their social properties in the design of aged-care facilities.
keywords	visibility analysis; Space Syntax; spatial cognition; social property
series	CAADRIA
email
last changed	2022/06/07 07:52

_id	ecaade2017_227
id	ecaade2017_227
authors	Lima, Elton C., Vieira, Aline, Mendes, Leticia T. and Griz, Cristiana
year	2017
title	“Houses for everybody” Brazilian competition - An application of shape grammar and space syntax for analyzing low-income housing
source	Fioravanti, A, Cursi, S, Elahmar, S, Gargaro, S, Loffreda, G, Novembri, G, Trento, A (eds.), ShoCK! - Sharing Computational Knowledge! - Proceedings of the 35th eCAADe Conference - Volume 2, Sapienza University of Rome, Rome, Italy, 20-22 September 2017, pp. 463-470
doi	https://doi.org/10.52842/conf.ecaade.2017.2.463
summary	This article focuses on the use of both shape grammar and space syntax as tools to identify and encode the principles and rules behind the design of low-income housing in Brazilian context. The idea is to use such rules as part of a methodology for analyzing quality space in social housing plans and aims to understand to which attributes of contemporary society redefine certain patterns of familial social conduct, particularly their ways of living and how these attributes impact housing spatial patterns.king this paragraph.
keywords	shape grammar; space syntax; design methodology
series	eCAADe
email
last changed	2022/06/07 07:59

_id	acadia17_92
id	acadia17_92
authors	Anzalone, Phillip; Bayard, Stephanie; Steenblik, Ralph S.
year	2017
title	Rapidly Deployed and Assembled Tensegrity System: An Augmented Design Approach
source	ACADIA 2017: DISCIPLINES & DISRUPTION [Proceedings of the 37th Annual Conference of the Association for Computer Aided Design in Architecture (ACADIA) ISBN 978-0-692-96506-1] Cambridge, MA 2-4 November, 2017), pp. 92-101
doi	https://doi.org/10.52842/conf.acadia.2017.092
summary	The Rapidly Deployable and Assembled Tensegrity (RDAT) project enables the efficient automated design and deployment of differential-geometry tensegrity structures through computation-driven design-to-installation workflow. RDAT employs the integration of parametric and solid-modeling methods with production by streamlining computer numerically controlled manufacturing through novel detailing and production techniques to develop an efficient manufacturing and assembly system. The RDAT project emerges from the Authors' research in academia and professional practice focusing on computationally produced full-scale performative building systems and their innovative uses in the building and construction industry.
keywords	design methods; information processing; AI; machine learning; form finding; VR; AR; mixed reality
series	ACADIA
email
last changed	2022/06/07 07:54

_id	acadia17_164
id	acadia17_164
authors	Brugnaro, Giulio; Hanna, Sean
year	2017
title	Adaptive Robotic Training Methods for Subtractive Manufacturing
source	ACADIA 2017: DISCIPLINES & DISRUPTION [Proceedings of the 37th Annual Conference of the Association for Computer Aided Design in Architecture (ACADIA) ISBN 978-0-692-96506-1] Cambridge, MA 2-4 November, 2017), pp. 164-169
doi	https://doi.org/10.52842/conf.acadia.2017.164
summary	This paper presents the initial developments of a method to train an adaptive robotic system for subtractive manufacturing with timber, based on sensor feedback, machine-learning procedures and material explorations. The methods were evaluated in a series of tests where the trained networks were successfully used to predict fabrication parameters for simple cutting operations with chisels and gouges. The results suggest potential benefits for non-standard fabrication methods and a more effective use of material affordances.
keywords	design methods; information processing; construction; robotics; ai & machine learning; digital craft; manual craft
series	ACADIA
email
last changed	2022/06/07 07:52

_id	caadria2017_070
id	caadria2017_070
authors	Chen, Nai Chun, Xie, Jenny, Tinn, Phil, Alonso, Luis, Nagakura, Takehiko and Larson, Kent
year	2017
title	Data Mining Tourism Patterns - Call Detail Records as Complementary Tools for Urban Decision Making
source	P. Janssen, P. Loh, A. Raonic, M. A. Schnabel (eds.), Protocols, Flows, and Glitches - Proceedings of the 22nd CAADRIA Conference, Xi'an Jiaotong-Liverpool University, Suzhou, China, 5-8 April 2017, pp. 685-694
doi	https://doi.org/10.52842/conf.caadria.2017.685
summary	In this study we show how Call Detail Record (CDR) can be used to better understand the travel patterns of visitors. We show how Origin-Destination (OD) Interactive Maps can provide transportation information through CDR. We then use aggregation of CDR to show the differences between the travel patterns of visitors from different countries and of different lengths of stay. We also show that visitors move differently during event periods and non-event periods, reflecting the importance of real-time data available by CDR. From CDR, we can gain more detailed and complete information about how tourists move compared to traditional surveys, which can be used to aid smarter transportation systems and urban resource planning.
keywords	Machine Learning; Call Detail Record; Original-Destination Matrix; Urban Design Tool
series	CAADRIA
email
last changed	2022/06/07 07:55

_id	acadia17_238
id	acadia17_238
authors	El-Zanfaly, Dina
year	2017
title	A Multisensory Computational Model for Human-Machine Making and Learning
source	ACADIA 2017: DISCIPLINES & DISRUPTION [Proceedings of the 37th Annual Conference of the Association for Computer Aided Design in Architecture (ACADIA) ISBN 978-0-692-96506-1] Cambridge, MA 2-4 November, 2017), pp. 238-247
doi	https://doi.org/10.52842/conf.acadia.2017.238
summary	Despite the advancement of digital design and fabrication technologies, design practices still follow Alberti’s hylomorphic model of separating the design phase from the construction phase. This separation hinders creativity and flexibility in reacting to surprises that may arise during the construction phase. These surprises often come as a result of a mismatch between the sophistication allowed by the digital technologies and the designer’s experience using them. These technologies and expertise depend on one human sense, vision, ignoring other senses that could be shaped and used in design and learning. Moreover, pedagogical approaches in the design studio have not yet fully integrated digital technologies as design companions; rather, they have been used primarily as tools for representation and materialization. This research introduces a multisensory computational model for human-machine making and learning. The model is based on a recursive process of embodied, situated, multisensory interaction between the learner, the machines and the thing-in-the-making. This approach depends heavily on computational making, abstracting, and describing the making process. To demonstrate its effectiveness, I present a case study from a course I taught at MIT in which students built full-scale, lightweight structures with embedded electronics. This model creates a loop between design and construction that develops students’ sensory experience and spatial reasoning skills while at the same time enabling them to use digital technologies as design companions. The paper shows that making can be used to teach design while enabling the students to make judgments on their own and to improvise.
keywords	education, society & culture; fabrication
series	ACADIA
email
last changed	2022/06/07 07:55

_id	acadia17_284
id	acadia17_284
authors	Hu, Zhengrong; Park, Ju Hong
year	2017
title	HalO [Indoor Positioning Mobile Platform]: A Data-Driven, Indoor-Positioning System With Bluetooth Low Energy Technology To Datafy Indoor Circulation And Classify Social Gathering Patterns For Assisting Post Occupancy Evaluation
source	ACADIA 2017: DISCIPLINES & DISRUPTION [Proceedings of the 37th Annual Conference of the Association for Computer Aided Design in Architecture (ACADIA) ISBN 978-0-692-96506-1] Cambridge, MA 2-4 November, 2017), pp. 284-291
doi	https://doi.org/10.52842/conf.acadia.2017.284
summary	Post-Occupancy Evaluation (POE) as an integrated field between architecture and sociology has created practical guidelines for evaluating indoor human behavior within a built environment. This research builds on recent attempts to integrate datafication and machine learning into POE practices that may one day assist Building Information Modeling (BIM) and multi-agent modeling. This research is based on two premises: 1) that the proliferation of Bluetooth Low Energy (BLE) technology allows us to collect a building user’s data cost-effectively and 2) that the growing application of machine learning algorithms allows us to process, analyze and synthesize data efficiently. This study illustrates that the mobile platform HalO can serve as a generic tool for datafication and automation of data analysis of the movement of a building user. In this research, the iOS mobile application HalO, combined with BLE beacons enable building providers (architects, developers, engineers and facility managers etc.) to collect the user’s indoor location data. Triangulation was used to pinpoint the user’s indoor positions, and k-means clustering was applied to classify users into different gathering groups. Through four research procedures—Design Intention Analysis, Data Collection, Data Storage and Data Analysis—the visualized and classified data helps building providers to better evaluate building performance, optimize building operations and improve the accuracy of simulations.
keywords	design methods; information processing; data mining; IoT; AI; machine learning
series	ACADIA
email
last changed	2022/06/07 07:49

_id	ijac201715105
id	ijac201715105
authors	Nahmad Vazque, Alicia and Wassim Jabi
year	2017
title	Investigations in robotic-assisted design: Strategies for symbiotic agencies in material-directed generative design processes
source	International Journal of Architectural Computing vol. 15 - no. 1, 70-86
summary	The research described in this article utilises a phase-changing material, three-dimensional scanning technologies and a six-axis industrial robotic arms as vehicles to enable a novel framework where robotic technology is utilised as an ‘amplifier’ of the design process to realise geometries that derive from both constructive visions and architectural visions through iterative feedback loops between them. The robot in this scenario is not a fabrication tool but the enabler of an environment where the material, robotic and human agencies interact. This article describes the exploratory research for the development of a dialogic design process, sets the framework for its implementation, carries out an evaluation based on designer use and concludes with a set of observations. One of the main findings of this article is that a deeper collaboration that acknowledges the potential of these tools, in a learning-by-design method, can lead to new choreographies for architectural design and fabrication.
keywords	Robotic fabrication, human-machine networks, digital design, agency
series	other
type	normal paper
email
last changed	2019/08/02 08:28

_id	ecaade2017_271
id	ecaade2017_271
authors	Narahara, Taro
year	2017
title	Collective Construction Modeling and Machine Learning: Potential for Architectural Design
source	Fioravanti, A, Cursi, S, Elahmar, S, Gargaro, S, Loffreda, G, Novembri, G, Trento, A (eds.), ShoCK! - Sharing Computational Knowledge! - Proceedings of the 35th eCAADe Conference - Volume 2, Sapienza University of Rome, Rome, Italy, 20-22 September 2017, pp. 341-348
doi	https://doi.org/10.52842/conf.ecaade.2017.2.341
summary	Recently, there are significant developments in artificial intelligence using advanced machine learning algorithms such as deep neural networks. These new methods can defeat human expert players in strategy-based board games such as Go and video games such as Breakout. This paper suggests a way to incorporate such advanced computing methods into architectural design through introducing a simple conceptual design project inspired by computational interpretations of wasps' collective constructions. At this stage, the paper's intent is not to introduce a practical and fully finished tool directly useful for architectural design. Instead, the paper proposes an example of a program that can potentially become a conceptual framework for incorporating such advanced methods into architectural design.
keywords	Design tools; Stigmergy; Machine learning
series	eCAADe
email
last changed	2022/06/07 07:58

_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	ecaade2017_122
id	ecaade2017_122
authors	Peralta, Mercedes and Loyola, Mauricio
year	2017
title	Performative Materiality - A DrawBot for Materializing Kinetic Human-Machine Interaction in Architectural Space
source	Fioravanti, A, Cursi, S, Elahmar, S, Gargaro, S, Loffreda, G, Novembri, G, Trento, A (eds.), ShoCK! - Sharing Computational Knowledge! - Proceedings of the 35th eCAADe Conference - Volume 1, Sapienza University of Rome, Rome, Italy, 20-22 September 2017, pp. 611-618
doi	https://doi.org/10.52842/conf.ecaade.2017.1.611
summary	This paper presents an exploration of movement as a design material to evidence human-machine interaction in an architectural space. An autonomous robotic vehicle with environmental sensory capabilities interacts kinetically with people by recognizing their emotional states from their body postures. A drawing device installed in the vehicle leaves a trace on the floor as a material testimony to the mutual dynamics. The complex yet surprisingly intuitive choreographic interaction of the machine and its social and physical environment blurs the boundaries between drawing, machine, and performance. In general, the project conceptualizes movement as a design material, drawing as a performative action, and social interaction as a physical force, all of which can be enhanced or mediated by digital technologies to produce results with aesthetic value.
keywords	Human-Machine Interaction; Drawing Machine; Performance Design
series	eCAADe
email
last changed	2022/06/07 08:00

_id	ecaade2017_220
id	ecaade2017_220
authors	Quartara, Andrea and Figliola, Angelo
year	2017
title	Tangible Computing - Manufacturing of Intertwined Logics
source	Fioravanti, A, Cursi, S, Elahmar, S, Gargaro, S, Loffreda, G, Novembri, G, Trento, A (eds.), ShoCK! - Sharing Computational Knowledge! - Proceedings of the 35th eCAADe Conference - Volume 2, Sapienza University of Rome, Rome, Italy, 20-22 September 2017, pp. 115-122
doi	https://doi.org/10.52842/conf.ecaade.2017.2.115
summary	This paper explores the process of digital materialization through robotic fabrication techniques by presenting three wooden projects. The analysis of the case studies is oriented to underline the impact that computation had on architectural construction due to its methodological and instrumental innovations over the last decades. The absorption of computing and digital fabrication logics within the discipline is explored from either an architectural point of view and from the improvements related to automation of the constructive process. On the one hand the case studies are caught because of the desire to expand material complexity and, on the other hand because of the integration with other technological systems. The narrative allows gathering pros and cons in three different investigative macro areas: material culture, methodological oversights, and operative setbacks coming from digital machine and communicational constraints. This analytical investigation helps the definition of a new pathway for future researches, looking forward the assimilation of digital materiality learning in building construction.
keywords	computational design; file-to-factory; large-scale robotic woodworking; new production methods
series	eCAADe
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last changed	2022/06/07 08:00

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