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 530

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

_id	acadia16_98
id	acadia16_98
authors	Smith, Shane Ida; Lasch, Chris
year	2016
title	Machine Learning Integration for Adaptive Building Envelopes: An Experimental Framework for Intelligent Adaptive Control
doi	https://doi.org/10.52842/conf.acadia.2016.098
source	ACADIA // 2016: POSTHUMAN FRONTIERS: Data, Designers, and Cognitive Machines [Proceedings of the 36th Annual Conference of the Association for Computer Aided Design in Architecture (ACADIA) ISBN 978-0-692-77095-5] Ann Arbor 27-29 October, 2016, pp. 98-105
summary	This paper describes the development of an Intelligent Adaptive Control (IAC) framework that uses machine learning to integrate responsive passive conditioning at the envelope into a building’s comprehensive conventional environmental control system. Initial results show that by leveraging adaptive computational control to orchestrate the building’s mechanical and passive systems together, there exists a demonstrably greater potential to maximize energy efficiency than can be gained by focusing on either system individually, while the addition of more passive conditioning strategies significantly increase human comfort, health and wellness building-wide. Implicitly, this project suggests that, given the development and ever increasing adoption of building automation systems, a significant new site for computational design in architecture is expanding within the post-occupancy operation of a building, in contrast to architects’ traditional focus on the building’s initial design. Through the development of an experimental framework that includes physical material testing linked to computational simulation, this project begins to describe a set of tools and procedures by which architects might better conceptualize, visualize, and experiment with the design of adaptive building envelopes. This process allows designers to ultimately engage in the opportunities presented by active systems that govern the daily interactions between a building, its inhabitants, and their environment long after construction is completed. Adaptive material assemblies at the envelope are given special attention since it is here that a building’s performance and urban expression are most closely intertwined.
keywords	model predictive control, reinforcement learning, energy performance, adaptive envelope, sensate systems
series	ACADIA
type	paper
email
last changed	2022/06/07 07:56

_id	ascaad2016_027
id	ascaad2016_027
authors	Cocho-Bermejo, Ana
year	2016
title	Time in Adaptable Architecture - Deployable emergency intelligent membrane
source	Parametricism Vs. Materialism: Evolution of Digital Technologies for Development [8th ASCAAD Conference Proceedings ISBN 978-0-9955691-0-2] London (United Kingdom) 7-8 November 2016, pp. 249-258
summary	The term "Parametricism" widespread mainly by Patrick Schumacher (Schumacher, 2008) is worthy of study. Developing the concept of Human Oriented Parametric Architecture, the need of implementing time as the lost parameter in current adaptive design techniques will be discussed. Morphogenetic processes ideas will be discussed through the principle of an adaptable membrane as a case study. A model implementing a unique Arduino[i] on the façade will control its patterns performance through an Artificial Neural Network that will understand the kind of scenario the building is in, activating a Genetic Algorithm that will optimize the insulation performance of the ETFE pillows. The system will work with a global behavior for façade pattern performance and with a local one for each pillow, giving the option of individual sun-shading control. Machine learning implementation will give the façade the possibility to learn from the efficacy of its decisions through time, eliminating the need of a general on-off behavior.
series	ASCAAD
email
last changed	2017/05/25 13:31

_id	ecaade2016_216
id	ecaade2016_216
authors	Zarzycki, Andrzej
year	2016
title	Adaptive Designs with Distributed Intelligent Systems - Building Design Applications
doi	https://doi.org/10.52842/conf.ecaade.2016.1.681
source	Herneoja, Aulikki; Toni Österlund and Piia Markkanen (eds.), Complexity & Simplicity - Proceedings of the 34th eCAADe Conference - Volume 1, University of Oulu, Oulu, Finland, 22-26 August 2016, pp. 681-690
summary	This paper discusses and demonstrates an integration of embedded electronic systems utilizing distributed sensors and localized actuators to increase the adaptability and environmental performance of a building envelope. It reviews state-of-the-art technologies utilized in other fields that could be adopted into smart building designs. The case studies discussed here, sensors are embedded in construction assemblies provide a greater resolution of gathered data with a finer degree of actuation. These case studies adopt the Internet of Things (IoT) framework based on machine-to-machine (M2M) communication protocols as a potential solution for embedded building systems. stract here by clicking this paragraph.
wos	WOS:000402063700073
keywords	Adaptable Designs; Arduino Microcontrollers; ESP8266; Smart Buildings; Internet of Things
series	eCAADe
email
last changed	2022/06/07 07:57

_id	acadia16_116
id	acadia16_116
authors	Davis, Daniel
year	2016
title	Evaluating Buildings with Computation and Machine Learning
doi	https://doi.org/10.52842/conf.acadia.2016.116
source	ACADIA // 2016: POSTHUMAN FRONTIERS: Data, Designers, and Cognitive Machines [Proceedings of the 36th Annual Conference of the Association for Computer Aided Design in Architecture (ACADIA) ISBN 978-0-692-77095-5] Ann Arbor 27-29 October, 2016, pp. 116-123
summary	Although computers have significantly impacted the way we design buildings, they have yet to meaningfully impact the way we evaluate buildings. In this paper we detail two case studies where computation and machine learning were used to analyze data produced by building inhabitants. We find that a building’s ‘data exhaust’ provides a rich source of information for longitudinally analyzing people’s architectural preferences. We argue that computation-driven evaluation could supplement traditional post occupancy evaluations.
keywords	spatial analytics, machine learning, post occupancy evaluation
series	ACADIA
type	paper
email
last changed	2022/06/07 07:55

_id	acadia16_72
id	acadia16_72
authors	Harrison, Paul
year	2016
title	What Bricks Want: Machine Learning and Iterative Ruin
doi	https://doi.org/10.52842/conf.acadia.2016.072
source	ACADIA // 2016: POSTHUMAN FRONTIERS: Data, Designers, and Cognitive Machines [Proceedings of the 36th Annual Conference of the Association for Computer Aided Design in Architecture (ACADIA) ISBN 978-0-692-77095-5] Ann Arbor 27-29 October, 2016, pp. 72-77
summary	Ruin has a bad name. Despite the obvious complications, failure provides a rich opportunity—how better to understand a building’s physicality than to watch it collapse? This paper offers a novel method to exploit failure through physical simulation and iterative machine learning. Using technology traditionally relegated to special effects, we can now understand collapse on a granular level: since modern-day physics engines track object-object collisions, they enable a close reading of the spatial preferences that underpin ruin. In the case of bricks, that preference is relatively simple—to fall. By idealizing bricks as rigid bodies, one can understand the effects of gravitational force on each individual brick in a masonry structure. These structures are sometimes able to ‘settle,’ resulting in a stable equilibrium state; in many cases, it means that they will simply collapse. Analyzing ruin in this way is informative, to be sure, but it proves most useful when applied in series. The evolutionary solver described in this paper closely monitors the performance of constituent bricks and ensures that the most successful structures are emulated by later generations. The tool consists of two parts: a user interface for design and the solver itself. Once the architect produces a potential design, the solver performs an evolutionary optimization; after a few hundred iterations, the end result is a structurally sound version of the unstable original. It is hoped that this hybrid of top-down and bottom-up design strategies offers an architecture that is ultimately strengthened by its contingencies.
keywords	rigid body analysis, machine learning, multi-agent structural optimization, sensate systems
series	ACADIA
type	paper
email
last changed	2022/06/07 07:49

_id	ijac201614103
id	ijac201614103
authors	Savov, Anton; Oliver Tessmann and Stig Anton Nielsen
year	2016
title	Sensitive Assembly: Gamifying the design and assembly of fac?ade wall prototypes
source	International Journal of Architectural Computing vol. 14 - no. 1, 30-48
summary	The article describes a method for gamifying the design and assembly of computationally integrated structures built out of discrete identical blocks. As a case study, the interactive installation Sensitive Assembly was designed and built at the Digital Design Unit (Prof. Dr Oliver Tessmann) at the Technische Universita?t of Darmstadt and exhibited during the digital art festival NODE 2015 in Frankfurt in 2015. Sensitive Assembly invites people to play a Jenga-like game: starting from a solid wall, players are asked to remove and replace the installation’s building blocks to create windows to a nurturing light while challenging its stability. A computational system that senses the current state of the wall guides the physical interaction and predicts an approaching collapse or a new light beam breaking through. The installation extends the notion of real-time feedback from the digital into the physical and uses machine-learning techniques to predict future structural behaviour.
keywords	Gamification, prediction, feedback, interaction, assembly
series	journal
last changed	2016/06/13 08:34

_id	acadia19_392
id	acadia19_392
authors	Steinfeld, Kyle
year	2019
title	GAN Loci
doi	https://doi.org/10.52842/conf.acadia.2019.392
source	ACADIA 19:UBIQUITY AND AUTONOMY [Proceedings of the 39th Annual Conference of the Association for Computer Aided Design in Architecture (ACADIA) ISBN 978-0-578-59179-7] (The University of Texas at Austin School of Architecture, Austin, Texas 21-26 October, 2019) pp. 392-403
summary	This project applies techniques in machine learning, specifically generative adversarial networks (or GANs), to produce synthetic images intended to capture the predominant visual properties of urban places. We propose that imaging cities in this manner represents the first computational approach to documenting the Genius Loci of a city (Norberg-Schulz, 1980), which is understood to include those forms, textures, colors, and qualities of light that exemplify a particular urban location and that set it apart from similar places. Presented here are methods for the collection of urban image data, for the necessary processing and formatting of this data, and for the training of two known computational statistical models (StyleGAN (Karras et al., 2018) and Pix2Pix (Isola et al., 2016)) that identify visual patterns distinct to a given site and that reproduce these patterns to generate new images. These methods have been applied to image nine distinct urban contexts across six cities in the US and Europe, the results of which are presented here. While the product of this work is not a tool for the design of cities or building forms, but rather a method for the synthetic imaging of existing places, we nevertheless seek to situate the work in terms of computer-assisted design (CAD). In this regard, the project is demonstrative of a new approach to CAD tools. In contrast with existing tools that seek to capture the explicit intention of their user (Aish, Glynn, Sheil 2017), in applying computational statistical methods to the production of images that speak to the implicit qualities that constitute a place, this project demonstrates the unique advantages offered by such methods in capturing and expressing the tacit.
series	ACADIA
type	normal paper
email
last changed	2022/06/07 07:56

_id	ecaade2016_147
id	ecaade2016_147
authors	Tamke, Martin, Zwierzycki, Mateusz, Evers, Henrik Leander, Ochmann, Sebastian, Vock, Richard and Wessel, Raoul
year	2016
title	Tracking Changes in Buildings over Time - Fully Automated Reconstruction and Difference Detection of 3d Scan and BIM files
doi	https://doi.org/10.52842/conf.ecaade.2016.2.643
source	Herneoja, Aulikki; Toni Österlund and Piia Markkanen (eds.), Complexity & Simplicity - Proceedings of the 34th eCAADe Conference - Volume 2, University of Oulu, Oulu, Finland, 22-26 August 2016, pp. 643-651
summary	Architectural and Engineering Communities are interested in the detection of differences between different representations of the same building. These can be the differences between the design and the as-built-state of a building, or the detection of changes that occur over time and that are documented by consecutive 3D scans. Current approaches for the detection of differences between 3D scans and 3D building models are however laborious and work only on the level of a building element. We demonstrate a novel highly automated workflow to detect differences between representations of the same building. We discuss the underlying tools and methods and the ways to communicate deviations and differences in an appropriate manner and evaluate our approach with a rich set of real world datasets.
wos	WOS:000402064400065
keywords	3d scan; BIM; Machine learning; Point Clouds; Big Data
series	eCAADe
email
last changed	2022/06/07 07:56

_id	ecaade2016_036
id	ecaade2016_036
authors	Varinlioglu, Guzden, Halici, Suheyla Muge and Alacam, Sema
year	2016
title	Computational Thinking and the Architectural Curriculum - Simple to Complex or Complex to Simple?
doi	https://doi.org/10.52842/conf.ecaade.2016.1.253
source	Herneoja, Aulikki; Toni Österlund and Piia Markkanen (eds.), Complexity & Simplicity - Proceedings of the 34th eCAADe Conference - Volume 1, University of Oulu, Oulu, Finland, 22-26 August 2016, pp. 253-259
summary	Recent trends in architectural education and practice have encouraged the use of computational tools and methods for solving complex design problems. Newer technology can augment the design process by applying progressively more-advanced computational tools. However, the complex nature of these tools can lead to students getting lost at the skill-building stage, they can become trapped in computational design terminology, leading to designs of limited spatial quality. This paper introduces a pilot study from Izmir University of Economics (IUE) for the integration of computational design technology in the undergraduate architectural curricula, based on a workshop series using a top-down teaching strategy.
wos	WOS:000402063700028
keywords	Basic design; learning outcomes; keyword analysis; visual scripting environment (VSE)
series	eCAADe
email
last changed	2022/06/07 07:58

_id	caadria2016_259
id	caadria2016_259
authors	Chen, Jia-Yih and Shao-Chu Huang
year	2016
title	Adaptive Building Facade Optimisation: An integrated Green-BIM approach
doi	https://doi.org/10.52842/conf.caadria.2016.259
source	Living Systems and Micro-Utopias: Towards Continuous Designing, Proceedings of the 21st International Conference on Computer-Aided Architectural Design Research in Asia (CAADRIA 2016) / Melbourne 30 March–2 April 2016, pp. 259-268
summary	This study focused on the optimal design of adaptive build- ing fac?ade for achieving better energy performance. Iterative fac?ade components design are studied between virtual and physical models with integrated tools of BIM, parametric design and sensor devices. The main objectives of this study are: (1) exploring systematic design process via the analysis of adaptive components in responsive fac?ade design; (2) developing compliance checking system for green building regulations; (3) developing optimization system for adaptive fac?ade design process. This paper demonstrated the integration of various digital design methods and concluded with the energy modelling re- sults of a demo project unit for various fac?ade component designs.
keywords	Building fac?ade design; energy performance; design optimization; parametric design; BIM
series	CAADRIA
email
last changed	2022/06/07 07:55

_id	ecaade2016_ws-afuture
id	ecaade2016_ws-afuture
authors	Kim, Jaehwan, Schwartz, Mathew and Zarzycki, Andrzej
year	2016
title	The Wave of Autonomous Mobility:Architecture Facilitating Indoor Autonomous Navigation
doi	https://doi.org/10.52842/conf.ecaade.2016.1.053
source	Herneoja, Aulikki; Toni Österlund and Piia Markkanen (eds.), Complexity & Simplicity - Proceedings of the 34th eCAADe Conference - Volume 1, University of Oulu, Oulu, Finland, 22-26 August 2016, pp. 53-58
summary	When considering architectural and urban responses to autonomous mobility, it becomes evident that the future strategies will have to include a significant transformation to the built environment, particularly the ways it operates and interacts with inhabitants. Designers will not only need to rethink formal and functional arrangements but also, and perhaps primarily, consider the environment--buildings and cities--as active and equal actors with adaptive and autonomous behaviors similarly to those people or self-driving cars manifest. This paper discusses initial planning and design strategies for the integration of autonomous vehicles and other forms of autonomous mobility into the built environment. Specifically, it looks into necessary steps required to develop infrastructure to a level of autonomy that can facilitate a next generation of wayfinding and mobility. A growing research area into smaller personal mobility vehicles that would revolutionize elderly and disabled mobility brings to the light the major technical challenges present in current building infrastructure.
wos	WOS:000402063700004
keywords	Autonomous Vehicle; Navigation; Localization; Smart Buildings; Smart Infrastructure
series	eCAADe
email
last changed	2022/06/07 07:52

_id	acadia16_362
id	acadia16_362
authors	Beesley, Philip; Ilgun, Zeliha, Asya; Bouron, Giselle; Kadish, David; Prosser, Jordan; Gorbet, Rob; Kulic, Dana; Nicholas, Paul; Zwierzycki, Mateusz
year	2016
title	Hybrid Sentient Canopy: An implementation and visualization of proprioreceptive curiosity-based machine learning
doi	https://doi.org/10.52842/conf.acadia.2016.362
source	ACADIA // 2016: POSTHUMAN FRONTIERS: Data, Designers, and Cognitive Machines [Proceedings of the 36th Annual Conference of the Association for Computer Aided Design in Architecture (ACADIA) ISBN 978-0-692-77095-5] Ann Arbor 27-29 October, 2016, pp. 362-371
summary	This paper describes the development of a sentient canopy that interacts with human visitors by using its own internal motivation. Modular curiosity-based machine learning behaviour is supported by a highly distributed system of microprocessor hardware integrated within interlinked cellular arrays of sound, light, kinetic actuators and proprioreceptive sensors in a resilient physical scaffolding system. The curiosity-based system involves exploration by employing an expert system composed of archives of information from preceding behaviours, calculating potential behaviours together with locations and applications, executing behaviour and comparing result to prediction. Prototype architectural structures entitled Sentient Canopy and Sentient Chamber developed during 2015 and 2016 were developed to support this interactive behaviour, integrating new communications protocols and firmware, and a hybrid proprioreceptive system that configured new electronics with sound, light, and motion sensing capable of internal machine sensing and externally- oriented sensing for human interaction. Proprioreception was implemented by producing custom electronics serving photoresistors, pitch-sensing microphones, and accelerometers for motion and position, coupled to sound, light and motion-based actuators and additional infrared sensors designed for sensing of human gestures. This configuration provided the machine system with the ability to calculate and detect real-time behaviour and to compare this to models of behaviour predicted within scripted routines. Testbeds located at the Living Architecture Systems Group/Philip Beesley Architect Inc. (LASG/PBAI, Waterloo/Toronto), Centre for Information Technology (CITA, Copenhagen) National Academy of Sciences (NAS) in Washington DC are illustrated.
keywords	intedisciplinary/collaborative design, intelligent environments, artificial intelligence, sensate systems
series	ACADIA
type	paper
email
last changed	2022/06/07 07:54

_id	ecaade2016_079
id	ecaade2016_079
authors	Cheng, Chi-Li and Hou, June-Hao
year	2016
title	Biomimetic Robotic Construction Process - An approach for adapting mass irregular-shaped natural materials
doi	https://doi.org/10.52842/conf.ecaade.2016.1.133
source	Herneoja, Aulikki; Toni Österlund and Piia Markkanen (eds.), Complexity & Simplicity - Proceedings of the 34th eCAADe Conference - Volume 1, University of Oulu, Oulu, Finland, 22-26 August 2016, pp. 133-142
summary	Beaver dams are formed by two main processes. One is that beavers select proper woods for constructing. The other one is that streams aggregate those woods to be assembled. Using this approach to construction structure is suitable for natural environment. In this paper, we attempt to develop a construction process which is suitable for all-terrain construction robot in the future. This construction process is inspired by beavers' construction behavior in nature. Beavers select proper sticks to make the structure stable. We predict that particular properties of sticks contribute gravity-driven assembly of wood structure. Thus, we implement the system with machine learning to find proper properties of sticks to improve selection mechanism of construction process. During this construction process, 3D scanner on robotic arm scans and recognizes sticks on terrain, and then robot will select proper sticks and place them. After placement, the system will scan and record the results for learning mechanism.
wos	WOS:000402063700015
keywords	Biomimetic Design; Machine Learning; Natural Material; Point Cloud Analysis; Robotic Fabrication
series	eCAADe
email
last changed	2022/06/07 07:55

_id	sigradi2016_805
id	sigradi2016_805
authors	Cormack, Jordan; Sweet, Kevin S.
year	2016
title	Parametrically Fabricated Joints: Creating a Digital Workflow
source	SIGraDi 2016 [Proceedings of the 20th Conference of the Iberoamerican Society of Digital Graphics - ISBN: 978-956-7051-86-1] Argentina, Buenos Aires 9 - 11 November 2016, pp.412-417
summary	Timber joinery for furniture and architectural purpose has always been identified as a skill or craft. The craft is the demonstration of hand machined skill and precision which is passed down or developed through the iteration of creation and refined reflection. Using digital fabrication techniques provides new, typically unexplored ways of creating and designing joints. It is as if these limitations which bind the ratio of complexity and use are stretched. This means that these joints, from a technical standpoint, can be more advanced than historically hand-made joints as digital machines are not bound by the limitations of the human. The research investigated in this paper explores the ability to create sets of joints in a parametric environment that will be produced with CNC machines, thus redefining the idea of the joint through contemporary tools of creation and fabrication. The research also aims to provide a seamless, digital workflow from the flexible, parametric creation of the joint to the final physical fabrication of it. Traditional joints, more simple in shape and assembly, were first digitally created to ease the educational challenges of learning a computational workflow that entailed the creation and fabrication of geometrically programmed joints. Following the programming and manufacturing of these traditional joints, more advanced and complex joints were created as the understanding of the capabilities of the software and CNC machines developed. The more complex and varied joints were taken from a CAD virtual environment and tested on a 3-axis CNC machine and 3D printer. The transformation from the virtual environment to the physical highlighted areas that required further research and testing. The programmed joint was then refined using the feedback from the digital to physical process creating a more robust joint that was informed by reality.
keywords	Joinery; digital fabrication; parametric; scripting; machining
series	SIGRADI
email
last changed	2021/03/28 19:58

_id	caadria2024_186
id	caadria2024_186
authors	Huang, Jingfei and Tu, Han
year	2024
title	Inconsistent Affective Reaction: Sentiment of Perception and Opinion in Urban Environments
doi	https://doi.org/10.52842/conf.caadria.2024.2.395
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. 395–404
summary	The ascension of social media platforms has transformed our understanding of urban environments, giving rise to nuanced variations in sentiment reaction embedded within human perception and opinion, and challenging existing multidimensional sentiment analysis approaches in urban studies. This study presents novel methodologies for identifying and elucidating sentiment inconsistency, constructing a dataset encompassing 140,750 Baidu and Tencent Street view images to measure perceptions, and 984,024 Weibo social media text posts to measure opinions. A reaction index is developed, integrating object detection and natural language processing techniques to classify sentiment in Beijing Second Ring for 2016 and 2022. Classified sentiment reaction is analysed and visualized using regression analysis, image segmentation, and word frequency based on land-use distribution to discern underlying factors. The perception affective reaction trend map reveals a shift toward more evenly distributed positive sentiment, while the opinion affective reaction trend map shows more extreme changes. Our mismatch map indicates significant disparities between the sentiments of human perception and opinion of urban areas over the years. Changes in sentiment reactions have significant relationships with elements such as dense buildings and pedestrian presence. Our inconsistent maps present perception and opinion sentiments before and after the pandemic and offer potential explanations and directions for environmental management, in formulating strategies for urban renewal.
keywords	Urban Sentiment, Affective Reaction, Social Media, Machine Learning, Urban Data, Image Segmentation.
series	CAADRIA
email
last changed	2024/11/17 22:05

_id	ecaade2016_023
id	ecaade2016_023
authors	Olascoaga, Carlos Sandoval, Xu, Wenfei and Flores, Hector
year	2016
title	Crowd-Sourced Neighborhoods - User-Contextualized Neighborhood Ranking
doi	https://doi.org/10.52842/conf.ecaade.2016.2.019
source	Herneoja, Aulikki; Toni Österlund and Piia Markkanen (eds.), Complexity & Simplicity - Proceedings of the 34th eCAADe Conference - Volume 2, University of Oulu, Oulu, Finland, 22-26 August 2016, pp. 19-30
summary	Finding an attractive or best-fit neighborhood for a new resident of any city is not only important from the perspective of the resident him or herself, but has larger implications for developers and city planners. The environment or mood of the right neighborhood is not simply created through traditional characteristics such as income, crime, or zoning regulations - more ephemeral traits related to user-perception also have significant weight. Using datasets and tools previously unassociated with real-estate decision-making and neighborhood planning, such as social media and machine learning, we create a non-deterministic and customized way of discovering and understanding neighborhoods. Our project creates a customizable ranking system for the 195 neighborhoods in New York City that helps users find the one that best matches their preferences. Our team has developed a composite weighted score with urban spatial data and social media data to rank all NYC neighborhoods based on a series of questions asked to the user. The project's contribution is to provide a scientific and calibrated understanding of the impact that socially oriented activities and preferences have towards the uses of space.
wos	WOS:000402064400001
keywords	Textual Semantic analysis; machine learning; participatory planning; community detection; neighborhood definition
series	eCAADe
email
last changed	2022/06/07 08:00

_id	lasg_whitepapers_2016_134
id	lasg_whitepapers_2016_134
authors	Ruairi Glynn
year	2016
title	The Environmental Half of Machine Life
source	Living Architecture Systems Group White Papers 2016 [ISBN 978-1-988366-10-4 (EPUB)] Riverside Architectural Press 2016: Toronto, Canada pp. 134 - 141
summary	Living Architecture Systems Group "White Papers 2016" is a dossier produced for the occasion of the Living Architecture Systems Group launch event and symposium hosted on November 4 and 5 at the Sterling Road Studio in Toronto and the University of Waterloo School of Architecture at Cambridge. The "White Papers 2016" presents research contributions from the LASG partners, forming an overview of the partnership and highlighting oppportunities for future collaborations.
keywords	design, dissipative methods, design methods, synthetic cognition, neuroscience, metabolism, STEAM, organicism, field work, responsive systems, space, visualizations, sensors, actuators, signal flows, art and technology, new media art, digital art, emerging technologies, citizen building, bioinspiration, performance, paradigms, artificial nature, virtual design, regenerative design, 4DSOUND, spatial sound, biomanufacturing, eskin, delueze, bees, robotics
email
last changed	2019/07/29 14:00

_id	caadria2016_881
id	caadria2016_881
authors	Silvestre, Joaquim; Yasushi Ikeda and Franc?ois Gue?na
year	2016
title	Artificial Imagination of Architecture with Deep Convolutional Neural Network
doi	https://doi.org/10.52842/conf.caadria.2016.881
source	Living Systems and Micro-Utopias: Towards Continuous Designing, Proceedings of the 21st International Conference on Computer-Aided Architectural Design Research in Asia (CAADRIA 2016) / Melbourne 30 March–2 April 2016, pp. 881-890
summary	This paper attempts to determine if an Artificial Intelli- gence system using deep convolutional neural network (ConvNet) will be able to “imagine” architecture. Imagining architecture by means of algorithms can be affiliated to the research field of generative archi- tecture. ConvNet makes it possible to avoid that difficulty by automat- ically extracting and classifying these rules as features from large ex- ample data. Moreover, image-base rendering algorithms can manipu- late those abstract rules encoded in the ConvNet. From these rules and without constructing a prior 3D model, these algorithms can generate perspective of an architectural image. To conclude, establishing shape grammar with this automated system opens prospects for generative architecture with image-base rendering algorithms.
keywords	Machine learning; convolutional neural network; generative design; image-based rendering
series	CAADRIA
email
last changed	2022/06/07 07:56

_id	acadia20_238
id	acadia20_238
authors	Zhang, Hang
year	2020
title	Text-to-Form
doi	https://doi.org/10.52842/conf.acadia.2020.1.238
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. 238-247.
summary	Traditionally, architects express their thoughts on the design of 3D architectural forms via perspective renderings and standardized 2D drawings. However, as architectural design is always multidimensional and intricate, it is difficult to make others understand the design intention, concrete form, and even spatial layout through simple language descriptions. Benefiting from the fast development of machine learning, especially natural language processing and convolutional neural networks, this paper proposes a Linguistics-based Architectural Form Generative Model (LAFGM) that could be trained to make 3D architectural form predictions based simply on language input. Several related works exist that focus on learning text-to-image generation, while others have taken a further step by generating simple shapes from the descriptions. However, the text parsing and output of these works still remain either at the 2D stage or confined to a single geometry. On the basis of these works, this paper used both Stanford Scene Graph Parser (Sebastian et al. 2015) and graph convolutional networks (Kipf and Welling 2016) to compile the analytic semantic structure for the input texts, then generated the 3D architectural form expressed by the language descriptions, which is also aided by several optimization algorithms. To a certain extent, the training results approached the 3D form intended in the textual description, not only indicating the tremendous potential of LAFGM from linguistic input to 3D architectural form, but also innovating design expression and communication regarding 3D spatial information.
series	ACADIA
type	paper
email
last changed	2023/10/22 12:06

_id	ecaade2021_203
id	ecaade2021_203
authors	Arora, Hardik, Bielski, Jessica, Eisenstadt, Viktor, Langenhan, Christoph, Ziegler, Christoph, Althoff, Klaus-Dieter and Dengel, Andreas
year	2021
title	Consistency Checker - An automatic constraint-based evaluator for housing spatial configurations
doi	https://doi.org/10.52842/conf.ecaade.2021.2.351
source	Stojakovic, V and Tepavcevic, B (eds.), Towards a new, configurable architecture - Proceedings of the 39th eCAADe Conference - Volume 2, University of Novi Sad, Novi Sad, Serbia, 8-10 September 2021, pp. 351-358
summary	The gradual rise of artificial intelligence (AI) and its increasing visibility among many research disciplines affected Computer-Aided Architectural Design (CAAD). Architectural deep learning (DL) approaches are being developed and published on a regular basis, such as retrieval (Sharma et al. 2017) or design style manipulation (Newton 2019; Silvestre et al. 2016). However, there seems to be no method to evaluate highly constrained spatial configurations for specific architectural domains (such as housing or office buildings) based on basic architectural principles and everyday practices. This paper introduces an automatic constraint-based consistency checker to evaluate the coherency of semantic spatial configurations of housing construction using a small set of design principles to evaluate our DL approaches. The consistency checker informs about the overall performance of a spatial configuration followed by whether it is open/closed and the constraints it didn't satisfy. This paper deals with the relation of spaces processed as mathematically formalized graphs contrary to existing model checking software like Solibri.
keywords	model checking, building information modeling, deep learning, data quality
series	eCAADe
email
last changed	2022/06/07 07:54

For more results click below: