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

_id	ecaade2018_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	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	caadria2018_332
id	caadria2018_332
authors	van Ameijde, Jeroen and Song, Yutao
year	2018
title	Data-Driven Urban Porosity - Incorporating Parameters of Public Space into a Generative Urban Design Process
doi	https://doi.org/10.52842/conf.caadria.2018.1.173
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. 173-182
summary	This paper presents an urban design project for a new city district, using generative design processes in architecture and urbanism developed over several years within academic research and practice work. The paper discusses the opportunities and challenges found when using a data-driven urban design methodology in relation to the complex logistical, social and economical networks of new urban centers.
keywords	Design Methods and Information Processing; Generative System; Simulation & Optimization; Urban Planning and Design; Public Space Design
series	CAADRIA
email
last changed	2022/06/07 07:58

_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	sigradi2018_1329
id	sigradi2018_1329
authors	Campos Fialho, Beatriz; A. Costa, Heliara; Logsdon, Louise; Minto Fabrício, Márcio
year	2018
title	CAD and BIM tools in Teaching of Graphic Representation for Engineering
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. 961-968
summary	BIM technology has represented an advance and a break of the design process’ paradigm, impacting both academia and construction market. Reporting a didactic experience in the Civil Engineering graduation, this article aims to understand the teaching and learning process of graphic representation, by using CAD and BIM tools. The research included Literature Review and Empirical Study, whose data collection was based on the application of questionnaires, practical exercises and theoretical test with the students. As a contribution, we highline the complementary nature of the tools and the potentialities of BIM for teaching graphic representation.
keywords	Graphic Representation; CAD System Education; CAE System Education. BIM
series	SIGRADI
email
last changed	2021/03/28 19:58

_id	caadria2018_245
id	caadria2018_245
authors	Chowdhury, Shuva and Schnabel, Marc Aurel
year	2018
title	An Algorithmic Methodology to Predict Urban Form - An Instrument for Urban Design
doi	https://doi.org/10.52842/conf.caadria.2018.2.401
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. 401-410
summary	We question the recent practices of conventional and participatory urban design approaches and offer a middle approach by exploring computational design tools in the design system. On the one hand, the top-down urban planning approaches investigate urban form as a holistic matter which only can be calibrated by urban professionals. These approaches are not able to offer enough information to the end users to predict the urban form. On the other hand, the bottom-up urban design approaches cannot visualise predicted urban scenarios, and most often the design decisions stay as general assumptions. We developed and tested a parametric design platform combines both approaches where all the stakeholders can participate and visualise multiple urban scenarios in real-time feedback. Parametric design along with CIM modelling system has influenced urban designers for a new endeavour in urban design. This paper presents a methodology to generate and visualise urban form. We present a novel decision-making platform that combines city level and local neighbourhood data to aid participatory urban design decisions. The platform allows for stakeholder collaboration and engagement in complex urban design processes.
keywords	knowledge-based system; algorithmic methodology ; design decision tool; urban form;
series	CAADRIA
email
last changed	2022/06/07 07:56

_id	ecaade2018_301
id	ecaade2018_301
authors	Cocho-Bermejo, Ana, Birgonul, Zeynep and Navarro-Mateu, Diego
year	2018
title	Adaptive & Morphogenetic City Research Laboratory
doi	https://doi.org/10.52842/conf.ecaade.2018.2.659
source	Kepczynska-Walczak, A, Bialkowski, S (eds.), Computing for a better tomorrow - Proceedings of the 36th eCAADe Conference - Volume 2, Lodz University of Technology, Lodz, Poland, 19-21 September 2018, pp. 659-668
summary	"Smart City" business model is guiding the development of future metropolises. Software industry sales to town halls for city management services efficiency improvement are, these days, a very pro?table business. Being the model decided by the industry, it can develop into a dangerous situation in which the basis of the new city design methodologies is decided by agents outside academia expertise. Drawing on complex science, social physics, urban economics, transportation theory, regional science and urban geography, the Lab is dedicated to the systematic analysis of, and theoretical speculation on, the recently coined "Science of Cities" discipline. On the research agenda there are questions arising from the synthesis of architecture, urban design, computer science and sociology. Collaboration with citizens through inclusion and empowerment, and, relationships "City-Data-Planner-Citizen" and "Citizen-Design-Science", configure Lab's methodology provoking a dynamic responsive process of design that is yet missing on the path towards the real responsive city.
keywords	Smart City; Morphogenetic Urban Design; Internet of Things; Building Information Modelling; Evolutionary Algorithms; Machine Learning & Artificial Intelligence
series	eCAADe
email
last changed	2022/06/07 07:56

_id	sigradi2018_1879
id	sigradi2018_1879
authors	Danesh Zand, Foroozan; Baghi, Ali; Kalantari, Saleh
year	2018
title	Digitally Fabricating Expandable Steel Structures Using Kirigami Patterns
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. 724-731
summary	This article presents a computational approach to generating architectural forms for large spanning structures based on a “paper-cutting” technique. In this traditional artform, a flat sheet is cut and scored in such a way that a small application of force prompts it to expand into a three-dimensional structure. To make these types of expandable structures feasible at an architectural scale, four challenges had to be met during the research. The first was to map the kinetic properties of a paper-cut model, investigating formative parameters such as the width and frequency of cuts to determine how they affect the resulting structure. The second challenge was to computationally simulate the paper-cut structure in an accurate fashion. We accomplished this task using finite element analysis in the Ansys software platform. The third challenge was to create a prediction model that could precisely forecast the characteristics of a paper-cutting pattern. We made significant strides in this demanding task by using a data-mining approach and regression analysis through 400 simulations of various cutting patterns. The final challenge was to verify the efficiency and accuracy of our prediction model, which we accomplished through a series of physical prototypes. Our resulting computational paper-cutting system can be used to estimate optimal cutting patterns and to predict the resulting structural characteristics, thereby providing greater rigor to what has previously been an ad-hoc and experimental design approach.
keywords	Transformable Paper-cut; Design method; Prediction Model; Regression analysis; Physical prototype
series	SIGRADI
email
last changed	2021/03/28 19:58

_id	ecaade2018_255
id	ecaade2018_255
authors	Danesh, Foroozan, Baghi, Ali and Kalantari, Saleh
year	2018
title	Programmable Paper Cutting - A Method to Digitally Fabricate Transformable, Complex Structural Geometry
doi	https://doi.org/10.52842/conf.ecaade.2018.2.489
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. 489-498
summary	This paper presents a computational approach to generating architectural forms for large spanning structures based on a "paper-cutting" technique. Using this approach, a flat sheet is cut and scored in such a way that a small application of force prompts it to expand into a three-dimensional structure. Our computational system can be used to estimate optimal cutting patterns and to predict the resulting structural characteristics, thereby providing greater rigor to what has previously been an ad-hoc and experimental design approach. To develop the model, we analyzed paper-cutting techniques, extracted the relevant formative parameters, and created a simulation using finite element analysis. We then used a data-mining approach through 400 simulations and applied a regression analysis to create a prediction model. Given a small number of input variables from the designer, this model can rapidly and precisely predict the transformation volume of a paper-cutting pattern. Additional structural characteristics will be modelled in future work. The use of this tool makes paper-cut design approaches more practical by changing a non-systematic, labor-intensive design process into a more precise and efficient one.
keywords	Paper-cut?; Transformable geometry; Design method; Model prediction; Data mining; Regression analysis
series	eCAADe
email
last changed	2022/06/07 07:55

_id	caadria2018_010
id	caadria2018_010
authors	Han, Lu and Cardoso Llach, Daniel
year	2018
title	Ludi: A Concurrent Physical and Digital Modeling Environment
doi	https://doi.org/10.52842/conf.caadria.2018.1.515
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. 515-523
summary	This paper explores the potential of a concurrent physical and digital modeling environment. We describe a prototype for a novel design modeling interface where users can take advantage of the affordances of both physical and digital modeling environments, and work back and forth between the two. Using Processing, along with the Kinect depth sensor, the system uses depth data read from a physical modeling space to produce an enhanced digital representation in real time. Users can design by moving and stacking wooden blocks in a physical space, which is represented (and enhanced) digitally as a "voxel space," which can in turn be edited digitally. The result is a proof-of-concept concurrent physical and digital modeling environment combining design affordances specific to each media: the physical space offers tactile and embodied forms of design inter-action, and the digital space offers parametric editing capabilities, along with the capacity to view the modeling space from different perspectives, and perform basic analyses on designs. Following a brief review of experimental computational and tangible interaction design interfaces, the paper discusses the system's implementation, its limitations, and future steps.
keywords	Computational Design; Processing; Concurrent Modeling Environment; Tangible Interaction
series	CAADRIA
email
last changed	2022/06/07 07:50

_id	acadia18_156
id	acadia18_156
authors	Huang, Weixin; Zheng, Hao
year	2018
title	Architectural Drawings Recognition and Generation through Machine Learning
doi	https://doi.org/10.52842/conf.acadia.2018.156
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
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	ecaade2018_w02
id	ecaade2018_w02
authors	Jabi, Wassim and Aish, Robert
year	2018
title	Non-manifold Topology for Architectural and Engineering Modelling
doi	https://doi.org/10.52842/conf.ecaade.2018.1.057
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. 57-60
summary	Non-manifold topology (NMT) allows the user to construct light-weight conceptual spatial architectural models which define the overall enclosure and the internal cellular division within that enclosure. The objective of this workshop is to give participants hands-on opportunities with a new software library that we have been developing under a research grant from the Leverhulme Trust. On the first day, the concepts of non-manifold topology will be introduced, including non-regular modelling operations. On the second day, we will introduce two plug-ins, which have been interfaced to our NMT tools: a) building energy simulation using OpenStudio and EnergyPlus and b) structural analysis software.
keywords	Non-manifold topology; Visual data flow programming; Building performance simulation; Computational design
series	eCAADe
email
last changed	2022/06/07 07:50

_id	acadia18_118
id	acadia18_118
authors	Kalantari, Saleh; Contreras-Vidal, Jose Luis; Smith, Joshua Stanton; Cruz-Garza, Jesus; Banner, Pamela
year	2018
title	Evaluating Educational Settings through Biometric Data and Virtual Response Testing
doi	https://doi.org/10.52842/conf.acadia.2018.118
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. 118-125
summary	The physical design of the learning environment has been shown to contribute significantly to student performance and educational outcomes. However, the existing literature on this topic relies primarily on generalized observations rather than on rigorous empirical testing. Broad trends in environmental impacts have been noted, but there is a lack of detailed evidence about how specific design variables can affect learning performance. The goal of this study was to apply a new approach in examining classroom design innovations. We developed a protocol to evaluate the effectiveness of classroom designs by measuring the physical responses of study participants as they interacted with different designs using a virtual reality platform. Our hypothesis was that virtual “test runs” can help designers to identify potential problems and successes in their work prior to its being physically constructed. The results of our initial pilot study indicated that this approach could yield important results about human responses to classroom design, and that the virtual environment seemed to be a reliable testing substitute when compared against real classroom environments. In addition to leading toward practical conclusions about specific classroom design variables, this project provides a new kind of research method and toolset to test the potential human impacts of a wide variety of architectural innovations.
keywords	work in progress, signal processing, eeg, virtual reality, big data, learning performance
series	ACADIA
type	paper
email
last changed	2022/06/07 07:52

_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_018
id	caadria2018_018
authors	Lin, Yuming and Huang, Weixin
year	2018
title	Social Behavior Analysis in Innovation Incubator Based on Wi-Fi Data - A Case Study on Yan Jing Lane Community
doi	https://doi.org/10.52842/conf.caadria.2018.2.197
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. 197-206
summary	Innovation incubator is an emerging kind of office space which focuses on promoting social interaction in the space. From the perspective of environmental behavior, the complex relationship between a particular space form and the social interactions is well worth exploring. Based on Wi-Fi positioning data, this paper examined the spatial and temporal behavior in innovation incubators. Using the interdisciplinary social networks analysis, this paper further analyzed the social interactions in this space, mining out social structures such as gathering and community, and analyzing the relationship between these structures and spaces. The result shows that human behavior in innovation incubators has some interesting characteristics, and the social structures are closely linked with the functional area of innovation incubator. This paper provides a new perspective and introduces interdisciplinary approaches to study the social behaviors in a particular space form, which has great potential in future research.
keywords	environmental behavior study; social behavior analysis; innovation incubator; Wi-Fi IPS; social network
series	CAADRIA
email
last changed	2022/06/07 07:59

_id	ijac201816403
id	ijac201816403
authors	Pantazis, Evangelos and David Gerber
year	2018
title	A framework for generating and evaluating façade designs using a multi-agent system approach
source	International Journal of Architectural Computing vol. 16 - no. 4, 248-270
summary	Digital design paradigms in architecture have been rooted in representational models which are geometry centered and therefore fail to capture building complexity holistically. Due to a lack of computational design methodologies, existing digital design workflows do little in predicting design performance in the early design stage and in most cases analysis and design optimization are done after a design is fixed. This work proposes a new computational design methodology, intended for use in the area of conceptual design of building design. The proposed methodology is implemented into a multi-agent system design toolkit which facilitates the generation of design alternatives using stochastic algorithms and their evaluation using multiple environmental performance metrics. The method allows the user to probabilistically explore the solution space by modeling the design parameters’ architectural design components (i.e. façade panel) into modular programming blocks (agents) which interact in a bottom-up fashion. Different problem requirements (i.e. level of daylight inside a space, openings) described into agents’ behavior allow for the coupling of data from different engineering fields (environmental design, structural design) into the a priori formation of architectural geometry. In the presented design experiment, a façade panel is modeled into an agent-based fashion and the multi-agent system toolkit is used to generate and evolve alternative façade panel configurations based on environmental parameters (daylight, energy consumption). The designer can develop the façade panel geometry, design behaviors, and performance criteria to evaluate the design alternatives. The toolkit relies on modular and functionally specific programming modules (agents), which provide a platform for façade design exploration by combining existing three-dimensional modeling and analysis software.
keywords	Generative design, multi-agent systems, façade design, agent-based modeling, stochastic search
series	journal
email
last changed	2019/08/07 14:04

_id	ecaade2023_10
id	ecaade2023_10
authors	Sepúlveda, Abel, Eslamirad, Nasim and De Luca, Francesco
year	2023
title	Machine Learning Approach versus Prediction Formulas to Design Healthy Dwellings in a Cold Climate
doi	https://doi.org/10.52842/conf.ecaade.2023.2.359
source	Dokonal, W, Hirschberg, U and Wurzer, G (eds.), Digital Design Reconsidered - Proceedings of the 41st Conference on Education and Research in Computer Aided Architectural Design in Europe (eCAADe 2023) - Volume 2, Graz, 20-22 September 2023, pp. 359–368
summary	This paper presents a study about the prediction accuracy of daylight provision and overheating levels in dwellings when considering different methods (machine learning vs prediction formulas), training, and validation data sets. An existing high-rise building located in Tallinn, Estonia was considered to compare the best ML predictive method with novel prediction formulas. The quantification of daylight provision was conducted according to the European daylight standard EN 17037:2018 (based on minimum Daylight Factor (minDF)) and overheating level in terms of the degree-hour (DH) metric included in local regulations. The features included in the dataset are the minDF and DH values related to different combinations of design parameters: window-to-floor ratio, level of obstruction, g-value, and visible transmittance of the glazing system. Different training and validation data sets were obtained from a main data set of 5120 minDF values and 40960 DH values obtained through simulation with Radiance and EnergyPlus, respectively. For each combination of training and validation dataset, the accuracy of the ML model was quantified and compared with the accuracy of the prediction formulas. According to our results, the ML model could provide more accurate minDF/DH predictions than by using the prediction formulas for the same design parameters. However, the amount of room combinations needed to train the machine-learning model is larger than for the calibration of the prediction formulas. The paper discuss in detail the method to use in practice, depending on time and accuracy concerns.
keywords	Optimization, Daylight, Thermal Comfort, Overheating, Machine Learning, Predictive Model, Dwellings, Cold Climates
series	eCAADe
email
last changed	2023/12/10 10:49

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

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

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