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 ecaade2018_111
id ecaade2018_111
authors Khean, Nariddh, Fabbri, Alessandra and Haeusler, M. Hank
year 2018
title Learning Machine Learning as an Architect, How to? - Presenting and evaluating a Grasshopper based platform to teach architecture students machine learning
source Kepczynska-Walczak, A, Bialkowski, S (eds.), Computing for a better tomorrow - Proceedings of the 36th eCAADe Conference - Volume 1, Lodz University of Technology, Lodz, Poland, 19-21 September 2018, pp. 95-102
doi https://doi.org/10.52842/conf.ecaade.2018.1.095
summary Machine learning algorithms have become widely embedded in many aspects of modern society. They have come to enhance systems, such as individualised marketing, social media services, and search engines. However, contrasting its growing ubiquity, the architectural industry has been comparatively resistant in its adoption; objectively one of the slowest industries to integrate with machine learning. Machine learning expertise can be separate from professionals in other fields; however, this separation can be a major hinderance in architecture, where interaction between the designer and the design facilitates the production of favourable outcomes. To bridge this knowledge gap, this research suggests that the solution lies with architectural education. Through the development of a novel educative framework, the research aims to teach architecture students how to implement machine learning. Exploration of student-centred pedagogical strategies was used to inform the conceptualisation of the educative module, which was subsequently implemented into an undergraduate computational design studio, and finally evaluated on its ability to effectively teach designers machine learning. The developed educative module represents a step towards greater technological adoption in the architecture industry.
keywords Artificial Intelligence; Machine Learning; Neural Networks; Student-Centred Learning; Educative Framework
series eCAADe
email
last changed 2022/06/07 07:52

_id 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 ecaade2018_164
id ecaade2018_164
authors Chang, Mei-Chih, Buš, Peter, Tartar, Ayça, Chirkin, Artem and Schmitt, Gerhard
year 2018
title Big-Data Informed Citizen Participatory Urban Identity Design
source Kepczynska-Walczak, A, Bialkowski, S (eds.), Computing for a better tomorrow - Proceedings of the 36th eCAADe Conference - Volume 2, Lodz University of Technology, Lodz, Poland, 19-21 September 2018, pp. 669-678
doi https://doi.org/10.52842/conf.ecaade.2018.2.669
summary The identity of an urban environment is important because it contributes to self-identity, a sense of community, and a sense of place. However, under present-day conditions, the identities of expanding cities are rapidly deteriorating and vanishing, especially in the case of Asian cities. Therefore, cities need to build their urban identity, which includes the past and points to the future. At the same time, cities need to add new features to improve their livability, sustainability, and resilience. In this paper, using data mining technologies for various types of geo-referenced big data and combine them with the space syntax analysis for observing and learning about the socioeconomic behavior and the quality of space. The observed and learned features are identified as the urban identity. The numeric features obtained from data mining are transformed into catalogued levels for designers to understand, which will allow them to propose proper designs that will complement or improve the local traditional features. A workshop in Taiwan, which focuses on a traditional area, demonstrates the result of the proposed methodology and how to transform a traditional area into a livable area. At the same time, we introduce a website platform, Quick Urban Analysis Kit (qua-kit), as a tool for citizens to participate in designs. After the workshop, citizens can view, comment, and vote on different design proposals to provide city authorities and stakeholders with their ideas in a more convenient and responsive way. Therefore, the citizens may deliver their opinions, knowledge, and suggestions for improvements to the investigated neighborhood from their own design perspective.
keywords Urban identity; unsupervised machine learning; Principal Component Analysis (PCA); citizen participated design; space syntax
series eCAADe
email
last changed 2022/06/07 07:56

_id caadria2018_085
id caadria2018_085
authors Chung, Chia-Chun and Jeng, Tay-Sheng
year 2018
title Information Extraction Methodology by Web Scraping for Smart Cities - Using Machine Learning to Train Air Quality Monitor for Smart Cities
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. 515-524
doi https://doi.org/10.52842/conf.caadria.2018.2.515
summary This paper presents an opportunistic sensing system for air quality monitoring to forecast the implicit factors of air pollution. Opportunistic sensing is performed by web scraping in the social network service to extract information. The data source for the air quality analysis combines two types of information: explicit and implicit information. The objective is to develop the information extraction methodology by web scraping for smart cities. The application development methodology has potential for solving real-world problems such as air pollution by data comparison between social activity observing and data collecting in sensor network.
keywords smart city; open data; web scraping; social media; machine learning
series CAADRIA
email
last changed 2022/06/07 07:56

_id ecaade2018_399
id ecaade2018_399
authors Cutellic, Pierre
year 2018
title UCHRON - An Event-Based Generative Design Software Implementing Fast Discriminative Cognitive Responses from Visual ERP BCI
source Kepczynska-Walczak, A, Bialkowski, S (eds.), Computing for a better tomorrow - Proceedings of the 36th eCAADe Conference - Volume 2, Lodz University of Technology, Lodz, Poland, 19-21 September 2018, pp. 131-138
doi https://doi.org/10.52842/conf.ecaade.2018.2.131
summary This research aims at investigating BCI technologies in the broad scope of CAAD applications exploiting early visual cognition in computational design. More precisely, this paper will describe the investigation of key BCI and ML components for the implementation and development of a software supporting this research : Uchron. It will be organised as follows. Firstly, it will introduce the pursued interest and contribution that visual-ERP EEG based BCI application for Generative Design may provide through a synthetic review of precedents and BCI technology. Secondly, selected BCI components will be described and a methodology will be presented to provide an appropriate framework for a CAAD software approach. This section main focus is on the processing component of the BCI. It distinguishes two key aspects of discrimination and generation in its design and proposes a new model based on GAN for modulated adversarial design. Emphasis will be made on the explicit use of inference loops integrating fast human cognitive responses and its individual capitalisation through time in order to reflect towards the generation of design and architectural features.
keywords Human Computer Interaction; Neurodesign; Generative Design; Design Computing and Cognition; Machine Learning
series eCAADe
email
last changed 2022/06/07 07:56

_id ecaade2018_176
id ecaade2018_176
authors Fisher-Gewirtzman, Dafna and Polak, Nir
year 2018
title Integrating Crowdsourcing & Gamification in an Automatic Architectural Synthesis Process
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. 439-444
doi https://doi.org/10.52842/conf.ecaade.2018.1.439
summary This work covers the methodological approach that is used to gather information from the wisdom of crowd, to be utilized in a machine learning process for the automatic generation of minimal apartment units. The flexibility in the synthesis process enables the generation of apartment units that seem to be random and some are unsuitable for dwelling. Thus, the synthesis process is required to classify units based on their suitability. The classification is deduced from opinions of human participants on previously generated units. As the definition of "suitability" may be subjective, this work offers a crowdsourcing method in order to reach a large number of participants, that as a whole would allow to produce an objective classification. Gaming elements have been adopted to make the crowdsourcing process more intuitive and inviting for external participants.
keywords crowdsourcing and gamification; urban density; optimization; automated architecture synthesis; minimum apartments; visual openness
series eCAADe
email
last changed 2022/06/07 07:51

_id acadia18_156
id acadia18_156
authors Huang, Weixin; Zheng, Hao
year 2018
title Architectural Drawings Recognition and Generation through Machine Learning
source ACADIA // 2018: Recalibration. On imprecisionand infidelity. [Proceedings of the 38th Annual Conference of the Association for Computer Aided Design in Architecture (ACADIA) ISBN 978-0-692-17729-7] Mexico City, Mexico 18-20 October, 2018, pp. 156-165
doi https://doi.org/10.52842/conf.acadia.2018.156
summary With the development of information technology, the ideas of programming and mass calculation were introduced into the design field, resulting in the growth of computer- aided design. With the idea of designing by data, we began to manipulate data directly, and interpret data through design works. Machine Learning as a decision making tool has been widely used in many fields. It can be used to analyze large amounts of data and predict future changes. Generative Adversarial Network (GAN) is a model framework in machine learning. It’s specially designed to learn and generate output data with similar or identical characteristics. Pix2pixHD is a modified version of GAN that learns image data in pairs and generates new images based on the input. The author applied pix2pixHD in recognizing and generating architectural drawings, marking rooms with different colors and then generating apartment plans through two convolutional neural networks. Next, in order to understand how these networks work, the author analyzed their framework, and provided an explanation of the three working principles of the networks, convolution layer, residual network layer and deconvolution layer. Lastly, in order to visualize the networks in architectural drawings, the author derived data from different layer and different training epochs, and visualized the findings as gray scale images. It was found that the features of the architectural plan drawings have been gradually learned and stored as parameters in the networks. As the networks get deeper and the training epoch increases, the features in the graph become more concise and clearer. This phenomenon may be inspiring in understanding the designing behavior of humans.
keywords full paper, design study, generative design, ai + machine learning, ai & machine learning
series ACADIA
type paper
email
last changed 2022/06/07 07:49

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

_id 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.
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
doi https://doi.org/10.52842/conf.ecaade.2018.2.085
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
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
doi https://doi.org/10.52842/conf.acadia.2018.166
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_190
id caadria2018_190
authors Lee, Ju Hyun, Gu, Ning, Taylor, Mark and Ostwald, Michael
year 2018
title Rethinking and Designing the Key Behaviours of Architectural Responsiveness in the Digital Age
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. 359-368
doi https://doi.org/10.52842/conf.caadria.2018.1.359
summary In the late 1960s the architect Nicholas Negroponte introduced that the physical environment could exhibit reflexive and simulated behaviours, an idea that has since been widely explored. Despite of this wider interest, there is not, however, a systematic approach to understanding architectural responsiveness in the digital age. This paper aims to provide a formal way to facilitate designing smart and interactive artificiality in the built environment. This paper presents a conceptual framework, through exploratory studies on recent architecture, highlighting four key behaviours: (1) tangible interaction, (2) embodied response, (3) ambient simulation, and (4) mixed reality. In addition, two essential enablers, collectiveness and immersion, are proposed to enhance these key behaviours. This framework can be used as a tool to systematically identify and characterise the responsiveness of "responsive architecture". The creative mixtures of the key behaviours will contribute to the development of unique responsive environments.
keywords Responsive architecture; Responsive behaviour; Interactive art; Negroponte
series CAADRIA
email
last changed 2022/06/07 07:52

_id caadria2018_057
id caadria2018_057
authors Nandavar, Anirudh, Petzold, Frank, Nassif, Jimmy and Schubert, Gerhard
year 2018
title Interactive Virtual Reality Tool for BIM Based on IFC - Development of OpenBIM and Game Engine Based Layout Planning Tool - A Novel Concept to Integrate BIM and VR with Bi-Directional Data Exchange
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. 453-462
doi https://doi.org/10.52842/conf.caadria.2018.1.453
summary With recent advancements in VR (Virtual Reality) technology in the past year, it has emerged as a new paradigm in visualization and immersive HMI (Human-machine Interface). On the other hand, in the past decades, BIM (Building Information Modelling) has emerged as the new standard of implementing construction projects and is quickly becoming a norm than just a co-ordination tool in the AEC industry.Visualization of the digital data in BIM plays an important role as it is the primary communication medium to the project participants, where VR can offer a new dimension of experiencing BIM and improving the collaboration of various stakeholders of a project. There are both open source and commercial solutions to extend visualization of a BIM project in VR, but so far, there are no complete solutions that offer a pure IFC format based solution, which makes the VR integration vendor neutral. This work endeavors to develop a concept for a vendor-neutral BIM-VR integration with bi-directional data exchange in order to extend VR as a collaboration tool than a mere visualization tool in the BIM ecosystem.
keywords BIM; VR; IFC; Unity; BIM-VR integration; HMI
series CAADRIA
email
last changed 2022/06/07 07:59

_id acadia18_146
id acadia18_146
authors Rossi, Gabriella; Nicholas, Paul
year 2018
title Re/Learning the Wheel. Methods to Utilize Neural Networks as Design Tools for Doubly Curved Metal Surfaces
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. 146-155
doi https://doi.org/10.52842/conf.acadia.2018.146
summary This paper introduces concepts and computational methodologies for utilizing neural networks as design tools for architecture and demonstrates their application in the making of doubly curved metal surfaces using a contemporary version of the English Wheel. The research adopts an interdisciplinary approach to develop a novel method to model complex geometric features using computational models that originate from the field of computer vision.

The paper contextualizes the approach with respect to the current state of the art of the usage of artificial neural networks both in architecture and beyond. It illustrates the cyber physical system that is at the core of this research, with a focus on the employed neural network–based computational method. Finally, the paper discusses the repercussions of these design tools on the contemporary design paradigm.

keywords full paper, ai & machine learning, digital craft, robotic production, computation
series ACADIA
type paper
email
last changed 2022/06/07 07:56

_id cf2019_003
id cf2019_003
authors Steinfeld, Kyle; Katherine Park, Adam Menges and Samantha Walker
year 2019
title Fresh Eyes A framework for the application of machine learning to generative architectural design, and a report of activities at Smartgeometry 2018
source Ji-Hyun Lee (Eds.) "Hello, Culture!"  [18th International Conference, CAAD Futures 2019, Proceedings / ISBN 978-89-89453-05-5] Daejeon, Korea, p. 22
summary This paper presents a framework for the application of Machine Learning (ML) to Generative Architectural Design (GAD), and illustrates this framework through a description of a series of projects completed at the Smart Geometry conference in May of 2018 (SG 2018) in Toronto. Proposed here is a modest modification of a 3-step process that is well-known in generative architectural design, and that proceeds as: generate, evaluate, iterate. In place of the typical approaches to the evaluation step, we propose to employ a machine learning process: a neural net trained to perform image classification. This modified process is different enough from traditional methods as to warrant an adjustment of the terms of GAD. Through the development of this framework, we seek to demonstrate that generative evaluation may be seen as a new locus of subjectivity in design.
keywords Machine Learning, Generative Design, Design Methods
series CAAD Futures
email
last changed 2019/07/29 14:08

_id ijac201816202
id ijac201816202
authors Tamke, Martin; Paul Nicholas and Mateusz Zwierzycki
year 2018
title Machine learning for architectural design: Practices and infrastructure
source International Journal of Architectural Computing vol. 16 - no. 2, 123-143
summary In this article, we propose that new architectural design practices might be based on machine learning approaches to better leverage data-rich environments and workflows. Through reference to recent architectural research, we describe how the application of machine learning can occur throughout the design and fabrication process, to develop varied relations between design, performance and learning. The impact of machine learning on architectural practices with performance-based design and fabrication is assessed in two cases by the authors. We then summarise what we perceive as current limits to a more widespread application and conclude by providing an outlook and direction for future research for machine learning in architectural design practice.
keywords Machine learning, robotic fabrication, design-integrated simulation, material behaviour, feedback, Complex Modelling
series journal
email
last changed 2019/08/07 14:03

_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
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
doi https://doi.org/10.52842/conf.acadia.2018.186
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_016
id caadria2018_016
authors Zahedi, Ata and Petzold, Frank
year 2018
title Utilization of Simulation Tools in Early Design Phases Through Adaptive Detailing Strategies
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. 11-20
doi https://doi.org/10.52842/conf.caadria.2018.2.011
summary Decisions taken at early stages of building design have a significant effect on the planning steps for the entire lifetime of the project as well as the performance of the building throughout its lifecycle (MacLeamy 2004). Building Information Modelling (BIM) could bring forward and enhance the planning and decision-making processes by enabling the direct reuse of data hold by the model for diverse analysis and simulation tasks (Borrmann et al. 2015). The architect today besides a couple of simplified simulation tools almost exclusively uses his know-how for evaluating and comparing design variants in the early stages of design. This paper focuses on finding new ways to facilitate the use of analytical and simulation tools during the important early phases of conceptual building design, where the models are partially incomplete. The necessary enrichment and proper detailing of the design model could be achieved by means of dialogue-based interaction concepts with analytical and simulation tools through adaptive detailing strategies. This concept is explained using an example scenario for design process. A generic description of the aimed dialog-based interface to various simulation tools will also be discussed in this paper using an example scenario.
keywords BIM; Early Design Stages; Adaptive Detailing ; Communication Protocols; Design Variants
series CAADRIA
email
last changed 2022/06/07 07:57

_id acadia18_196
id acadia18_196
authors Zhang, Yan; Grignard; Aubuchon, Alexander; Lyons, Keven; Larson, Kent
year 2018
title Machine Learning for Real-time Urban Metrics and Design Recommendations
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. 196-205
doi https://doi.org/10.52842/conf.acadia.2018.196
summary Cities are growing, becoming more complex, and changing rapidly. Currently, community engagement for urban decision-making is often ineffective, uninformed, and only occurs in projects’ later stages. To facilitate a more collaborative and evidence-based urban decision- making process for both experts and non-experts, real-time feedback and optimized suggestions are essential. However, most of the current tools for urban planning are neither capable of performing complex simulations in real time nor of providing guidance for better urban performance.

CityMatrix was introduced to address these challenges. Machine learning techniques were applied to achieve real-time prediction of multiple urban simulations, and thousands of city configurations were simulated. The simulation results were used to train a convolutional neural network (CNN) to predict the traffic and solar performance of unseen city configurations. The prediction with the CNN is thousands of times faster than the original simulations and maintains a high-quality representation of the results. This machine learning approach was applied as a versatile, quick, accurate, and computationally efficient method not only for real-time feedback, but also for optimized design recommendations. Users involved in the evaluation of this project had a better understanding of the embodied trade-offs of the city and achieved their goals in an efficient manner.

keywords full paper, optimization, collaboration, urban design & analysis, ai & machine learning
series ACADIA
type paper
email
last changed 2022/06/07 07:57

_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
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
doi https://doi.org/10.52842/conf.caadria.2018.2.083
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

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