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	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	acadia20_382
id	acadia20_382
authors	Hosmer, Tyson; Tigas, Panagiotis; Reeves, David; He, Ziming
year	2020
title	Spatial Assembly with Self-Play Reinforcement Learning
doi	https://doi.org/10.52842/conf.acadia.2020.1.382
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.
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	caadria2018_126
id	caadria2018_126
authors	Khean, Nariddh, Kim, Lucas, Martinez, Jorge, Doherty, Ben, Fabbri, Alessandra, Gardner, Nicole and Haeusler, M. Hank
year	2018
title	The Introspection of Deep Neural Networks - Towards Illuminating the Black Box - Training Architects Machine Learning via Grasshopper Definitions
doi	https://doi.org/10.52842/conf.caadria.2018.2.237
source	T. Fukuda, W. Huang, P. Janssen, K. Crolla, S. Alhadidi (eds.), Learning, Adapting and Prototyping - Proceedings of the 23rd CAADRIA Conference - Volume 2, Tsinghua University, Beijing, China, 17-19 May 2018, pp. 237-246
summary	Machine learning is yet to make a significant impact in the field of architecture and design. However, with the combination of artificial neural networks, a biologically inspired machine learning paradigm, and deep learning, a hierarchical subsystem of machine learning, the predictive capabilities of machine learning processes could prove a valuable tool for designers. Yet, the inherent knowledge gap between the fields of architecture and computer science has meant the complexity of machine learning, and thus its potential value and applications in the design of the built environment remain little understood. To bridge this knowledge gap, this paper describes the development of a learning tool directed at architects and designers to better understand the inner workings of machine learning. Within the parametric modelling environment of Grasshopper, this research develops a framework to express the mathematic and programmatic operations of neural networks in a visual scripting language. This offers a way to segment and parametrise each neural network operation into a basic expression. Unpacking the complexities of machine learning in an intermediary software environment such as Grasshopper intends to foster the broader adoption of artificial intelligence in architecture.
keywords	machine learning; neural network; action research; supervised learning; education
series	CAADRIA
email
last changed	2022/06/07 07:52

_id	ecaade2018_315
id	ecaade2018_315
authors	Koehler, Daniel, Abo Saleh, Sheghaf, Li, Hua, Ye, Chuwei, Zhou, Yaonaijia and Navasaityte, Rasa
year	2018
title	Mereologies - Combinatorial Design and the Description of Urban Form.
doi	https://doi.org/10.52842/conf.ecaade.2018.2.085
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. 85-94
summary	This paper discusses the ability to apply machine learning to the combinatorial design-assembly at the scale of a building to urban form. Connecting the historical lines of discrete automata in computer science and formal studies in architecture this research contributes to the field of additive material assemblies, aggregative architecture and their possible upscaling to urban design. The following case studies are a preparation to apply deep-learning on the computational descriptions of urban form. Departing from the game Go as a testbed for the development of deep-learning applications, an equivalent platform can be designed for architectural assembly. By this, the form of a building is defined via the overlap between separate building parts. Building on part-relations, this research uses mereology as a term for a set of recursive assembly strategies, integrated into the design aspects of the building parts. The models developed by research by design are formally described and tested under a digital simulation environment. The shown case study shows the process of how to transform geometrical elements to architectural parts based merely on their compositional aspects either in horizontal or three-dimensional arrangements.
keywords	Urban Form; Discrete Automata ; Combinatorics; Part-Relations; Mereology; Aggregative Architecture
series	eCAADe
email
last changed	2022/06/07 07:51

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

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

_id	caadria2018_303
id	caadria2018_303
authors	Song, Jae Yeol, Kim, Jin Sung, Kim, Hayan, Choi, Jungsik and Lee, Jin Kook
year	2018
title	Approach to Capturing Design Requirements from the Existing Architectural Documents Using Natural Language Processing Technique
doi	https://doi.org/10.52842/conf.caadria.2018.2.247
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. 247-254
summary	This paper describes an approach to utilizing natural language processing (NLP) to capture design requirements from the natural language-based architectural documents. In various design stage of the architectural process, there are several different kinds of documents describing requirements for buildings. Capturing the design requirements from those documents is based on extracting information of objects, their properties, and relations. Until recently, interpreting and extracting that information from documents are almost done by a manual process. To intelligently automate the conventional process, the computer has to understand the semantics of natural languages. In this regards, this paper suggests an approach to utilizing NLP for semantic analysis which enables the computer to understand the semantics of the given text data. The proposed approach has following steps: 1) extract noun words which mostly represent objects and property data in Korean Building Act; 2) analyze the semantic relations between words, using NLP and deep learning; 3) Based on domain database, translate the noun words in objects and properties data and find out their relations.
keywords	NLP (Natural Language Processing); Deep learning; Design requirements; Korean Building Act; Semantic analysis
series	CAADRIA
email
last changed	2022/06/07 07:56

_id	acadia18_186
id	acadia18_186
authors	Yin, Hao; Guo, Zhe; Zhao, Yao; Yuan, Philip F.
year	2018
title	Behavior Visualization System Based on UWB Positioning Technology
doi	https://doi.org/10.52842/conf.acadia.2018.186
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. 186-195
summary	This paper takes behavioral performance as a starting point and uses ultra-wideband (UWB) positioning technology and visualization methods to accurately collect and present in-place behavioral data so as to explore the behavioral characteristics of space users. In this process, we learned the observation, quantification, and presentation of behavioral data from the evolution of behavioral research. Secondly, after a comparative analysis of four types of indoor positioning technologies, we selected UWB-positioning technology and the JavaScript programming language as the development tools for a behavior visualization system. Next, we independently developed the behavior visualization system, which required a deep understanding of the working principle of UWB technology and the visualization method of the JavaScript programming language. Finally, the system was applied to an actual space, collecting and presenting users’ behavioral characteristics and habits in order to verify the applicability of the system in the field of behavioral research.
keywords	full paper, design tools, ai + machine learning, big data, behavioral performance + simulation
series	ACADIA
type	paper
email
last changed	2022/06/07 07:57

_id	caadria2018_049
id	caadria2018_049
authors	Xu, Tongda, Wang, Dinglu, Yang, Mingyan, You, Xiaohui and Huang, Weixin
year	2018
title	An Evolving Built Environment Prototype - A Prototype of Adaptive Built Environment Interacting with Electroencephalogram Supported by Reinforcement Learning
doi	https://doi.org/10.52842/conf.caadria.2018.2.207
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. 207-215
summary	This paper proposes an environment prototype learning from people's Electroencephalogram (EEG) feedback in real-time. Instead of the widely adopted supervised learning method, a recently published affordable reinforcement learning model (PPO) is adopted to avoid bias from designers and to base the interaction on the subject and intelligent agent rather than between the designer and subject. In this way, development of interaction method towards a specific target is substantially accelerated. The target of this prototype is to keep the subject's alpha wave stable or decline, which indicated a more calming state, by intelligent decision of illumination state according to subject's EEG. The result is promising, a decent trained model could be gained within 500,000 steps facing this mid-complex environment. The target of keeping the alpha wave of subjects on a low or stable level purely by decision from computer agents is successfully reached.
keywords	Brain–computer interface; Reinforcement learning; Adaptive environment; Electroencephalogram; Mindfulness training
series	CAADRIA
email
last changed	2022/06/07 07:57

_id	ecaadesigradi2019_478
id	ecaadesigradi2019_478
authors	Nardelli, Eduardo Sampaio
year	2019
title	BIM training in Brazil - Preparing professionals for BIM adoption by public administration
doi	https://doi.org/10.52842/conf.ecaade.2019.2.305
source	Sousa, JP, Xavier, JP and Castro Henriques, G (eds.), Architecture in the Age of the 4th Industrial Revolution - Proceedings of the 37th eCAADe and 23rd SIGraDi Conference - Volume 2, University of Porto, Porto, Portugal, 11-13 September 2019, pp. 305-314
summary	On May 2018 the Brazilian federal government published the Decree 9.377 setting a National Strategy for Information and Dissemination of Building Information Modelling - BIM to enable its adoption by public administration. This strategy has nine targets and among them the task of training professionals in BIM to support the demand that should be generated. A period between 2018 and 2021 has been planned to establish learning objectives and develop model disciplines, a process that, however, should not start from scratch because there are already some BIM training initiatives being performed in the country since the early 2000s. This paper has done an overview on this production highlighting some relevant conceptual contributions to this debate aiming to address challenges and possible ways to support the expected Architectural and Engineering courses restructuring.
keywords	BIM, Education, Architecture, Engineering and Construction
series	eCAADeSIGraDi
email
last changed	2022/06/07 07:58

_id	acadia18_216
id	acadia18_216
authors	Ahrens, Chandler; Chamberlain, Roger; Mitchell, Scott; Barnstorff, Adam
year	2018
title	Catoptric Surface
doi	https://doi.org/10.52842/conf.acadia.2018.216
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. 216-225
summary	The Catoptric Surface research project explores methods of reflecting daylight through a building envelope to form an image-based pattern of light on the interior environment. This research investigates the generation of atmospheric effects from daylighting projected onto architectural surfaces within a built environment in an attempt to amplify or reduce spatial perception. The mapping of variable organizations of light onto existing or new surfaces creates a condition where the perception of space does not rely on form alone. This condition creates a visual effect of a formless atmosphere and affects the way people use the space. Often the desired quantity and quality of daylight varies due to factors such as physiological differences due to age or the types of tasks people perform (Lechner 2009). Yet the dominant mode of thought toward the use of daylighting tends to promote a homogeneous environment, in that the resulting lighting level is the same throughout a space. This research project questions the desire for uniform lighting levels in favor of variegated and heterogeneous conditions. The main objective of this research is the production of a unique facade system that is capable of dynamically redirecting daylight to key locations deep within a building. Mirrors in a vertical array are individually adjusted via stepper motors in order to reflect more or less intense daylight into the interior space according to sun position and an image-based map. The image-based approach provides a way to specifically target lighting conditions, atmospheric effects, and the perception of space.
keywords	full paper, non-production robotics, representation + perception, performance + simulation, building technologies
series	ACADIA
type	paper
email
last changed	2022/06/07 07:54

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

_id	ijac201816407
id	ijac201816407
authors	Mahankali, Ranjeeth; Brian R. Johnson and Alex T. Anderson
year	2018
title	Deep learning in design workflows: The elusive design pixel
source	International Journal of Architectural Computing vol. 16 - no. 4, 328-340
summary	The recent wave of developments and research in the field of deep learning and artificial intelligence is causing the border between the intuitive and deterministic domains to be redrawn, especially in computer vision and natural language processing. As designers frequently invoke vision and language in the context of design, this article takes a step back to ask if deep learning’s capabilities might be applied to design workflows, especially in architecture. In addition to addressing this general question, the article discusses one of several prototypes, BIMToVec, developed to examine the use of deep learning in design. It employs techniques like those used in natural language processing to interpret building information models. The article also proposes a homogeneous data format, provisionally called a design pixel, which can store design information as spatial-semantic maps. This would make designers’ intuitive thoughts more accessible to deep learning algorithms while also allowing designers to communicate abstractly with design software.
keywords	Associative logic, creative processes, deep learning, embedding vectors, BIMToVec, homogeneous design data format, design pixel, idea persistence
series	journal
email
last changed	2019/08/07 14:04

_id	ecaade2018_w12
id	ecaade2018_w12
authors	Rahbar, Morteza
year	2018
title	Application of Artificial Intelligence in Architectural Generative Design
doi	https://doi.org/10.52842/conf.ecaade.2018.1.071
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. 71-72
summary	In this workshop, data-driven models will be discussed and how they could change the way architects think, design and analyse. Both supervised and unsupervised learning models will be discussed and different projects will be referred as examples. Deep learning models are the third part of the workshop and more specifically, Generative Adversarial Networks will be mentioned in more detail. The GAN's open a new field of generative models in design which is based on data-driven process and we will go into detail with GANs, their branches and how we could test a sample architecture generative problem with GANs.
keywords	Artificial Intelligence; Machine Learning; Generative Design; Knowledge based Design; GAN
series	eCAADe
email
last changed	2022/06/07 08:00

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

_id	sigradi2018_1619
id	sigradi2018_1619
authors	Agirbas, Asli
year	2018
title	Creating Non-standard Spaces via 3D Modeling and Simulation: A Case Study
source	SIGraDi 2018 [Proceedings of the 22nd Conference of the Iberoamerican Society of Digital Graphics - ISSN: 2318-6968] Brazil, São Carlos 7 - 9 November 2018, pp. 1051-1058
summary	Especially in the film industry, architectural spaces away from Euclidean geometry are brought to foreground. The best environment in which such spaces can be designed, is undoubtedly the 3D modeling environment. In this study, an experimental study was carried out on the creation of alternative spaces with undergraduate architectural students. Via using 3D modeling and various simulation techniques in the Maya software, students created spaces, which were away from the traditional architectural spaces. Thus, in addition to learning the 3D modeling software, architectural students learned to use animation and simulation as a part of design, not just as a presentation tool, and opening up new horizons for non-standard spaces was provided.
keywords	3D Modeling; Simulation; Animation; CAAD; Maya; Non-standard spaces
series	SIGRADI
email
last changed	2021/03/28 19:58

_id	caadria2018_029
id	caadria2018_029
authors	Ayoub, Mohammed
year	2018
title	Adaptive Façades:An Evaluation of Cellular Automata Controlled Dynamic Shading System Using New Hourly-Based Metrics
doi	https://doi.org/10.52842/conf.caadria.2018.2.083
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. 83-92
summary	This research explores utilizing Cellular Automata patterns as climate-adaptive dynamic shading systems to mitigate the undesirable impacts by excessive solar penetration in cooling-dominant climates. The methodological procedure is realized through two main phases. The first evaluates all 256 Elementary Cellular Automata possible rules to elect the ones with good visual and random patterns, to ensure an equitable distribution of the natural daylight in internal spaces. Based on the newly developed hourly-based metrics, simulations are conducted in the second phase to evaluate the Cellular Automata controlled dynamic shadings performance, and formalize the adaptive façade variation logic that maximizes daylighting and minimizes energy demand.
keywords	Adaptive Façade; Dynamic Shading; Cellular Automata; Hourly-Based Metric; Performance Evaluation
series	CAADRIA
email
last changed	2022/06/07 07:54

_id	caadria2018_342
id	caadria2018_342
authors	Bhagat, Nikita, Rybkowski, Zofia, Kalantar, Negar, Dixit, Manish, Bryant, John and Mansoori, Maryam
year	2018
title	Modulating Natural Ventilation to Enhance Resilience Through Modifying Nozzle Profiles - Exploring Rapid Prototyping Through 3D-Printing
doi	https://doi.org/10.52842/conf.caadria.2018.2.185
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. 185-194
summary	The study aimed to develop and test an environmentally friendly, easily deployable, and affordable solution for socio-economically challenged populations of the world. 3D-printing (additive manufacturing) was used as a rapid prototyping tool to develop and test a façade system that would modulate air velocity through modifying nozzle profiles to utilize natural cross ventilation techniques in order to improve human comfort in buildings. Constrained by seasonal weather and interior partitions which block the ability to cross ventilate, buildings can be equipped to perform at reduced energy loads and improved internal human comfort by using a façade system composed of retractable nozzles developed through this empirical research. This paper outlines the various stages of development and results obtained from physically testing different profiles of nozzle-forms that would populate the façade system. In addition to optimizing nozzle profiles, the team investigated the potential of collapsible tube systems to permit precise placement of natural ventilation directed at occupants of the built space.
keywords	Natural ventilation; Wind velocity; Rapid prototyping; 3D-printing; Nozzle profiles
series	CAADRIA
email
last changed	2022/06/07 07:52

_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
doi	https://doi.org/10.52842/conf.acadia.2018.176
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
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	caadria2018_125
id	caadria2018_125
authors	Bungbrakearti, Narissa, Cooper-Wooley, Ben, Odolphi, Jorke, Doherty, Ben, Fabbri, Alessandra, Gardner, Nicole and Haeusler, M. Hank
year	2018
title	HOLOSYNC - A Comparative Study on Mixed Reality and Contemporary Communication Methods in a Building Design Context
doi	https://doi.org/10.52842/conf.caadria.2018.1.401
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. 401-410
summary	The integration of technology into the design process has enabled us to communicate through various modes of virtuality, while more traditional face-to-face collaborations are becoming less frequent, specifically for large scale companies. Both modes of communication have benefits and disadvantages - virtual communication enables us to connect over large distances, however can often lead to miscommunication, while face-to-face communication builds stronger relationship, however may be problematic for geographically dispersed teams. Mixed Reality is argued to be a hybrid of face-to-face and virtual communication, and is yet to be integrated into the building design process. Despite its current limitations, such as field of view, Mixed Reality is an effective tool that generates high levels of nonverbal and verbal communication, and encourages a high and equal level of participation in comparison to virtual and face-to-face communication. Being a powerful communication tool for complex visualisations, it would be best implemented in the later stages of the building design process where teams can present designs to clients or where multiple designers can collaborate over final details.
keywords	Mixed Reality; Communication; Hololens; Collaboration; Virtual
series	CAADRIA
email
last changed	2022/06/07 07:54

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