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	ecaade2022_367
id	ecaade2022_367
authors	Doumpioti, Christina and Huang, Jeffrey
year	2022
title	Field Condition - Environmental sensibility of spatial configurations with the use of machine intelligence
source	Pak, B, Wurzer, G and Stouffs, R (eds.), Co-creating the Future: Inclusion in and through Design - Proceedings of the 40th Conference on Education and Research in Computer Aided Architectural Design in Europe (eCAADe 2022) - Volume 2, Ghent, 13-16 September 2022, pp. 67–74
doi	https://doi.org/10.52842/conf.ecaade.2022.2.067
summary	Within computational environmental design (CED), different Machine Learning (ML) models are gaining ground. They aim for time efficiency by automating simulation and speeding up environmental performance feedback. This study suggests an approach that enhances not the optimization but the generative aspect of environmentally driven ML processes in architectural design. We follow Stan Allen's (2009) idea of 'field conditions' as a bottom-up phenomenon according to which form and space emerge from local invisible and dynamic connections. By employing parametric modeling, environmental analysis data, and conditional Generative Adversarial Networks [cGAN] we introduce a generative approach in design that reverses the typical design process of going from formal interpretation to analysis and encourages the emergence of spatial configurations with embedded environmental intelligence. We call it Intensive-driven Environmental Design Computation [IEDC], and we employ it in a case study on a residential building typology encountered in the Mediterranean. The paper describes the process, emphasizing dataset preparation as the stage where the logic of field conditions is established. The proposed research differentiates from cGAN models that offer automatic environmental performance predictions to one that spatial predictions stem from dynamic fields.
keywords	Field Architecture, Environmental Design, Generative Design, Machine Learning, Residential Typologies
series	eCAADe
email
last changed	2024/04/22 07:10

_id	ijac202220310
id	ijac202220310
authors	Castro Henriques, Goncalo; Pedro Maciel Xavier; Victor de Luca Silva; Luca Rédua Bispo; Joao Victor Fraga
year	2022
title	Computation for Architecture, hybrid visual and textual language: Research developments and considerations about the implementation of structural imperative and object-oriented paradigms
source	International Journal of Architectural Computing 2022, Vol. 20 - no. 3, pp. 673–687
summary	In the fourth industrial revolution, programming promises to be a fundamental subject like mathematics, science, languages or the arts. Architects design more than buildings developing innovative methods and they are among the pioneers in visual programming development. However, after more than 10 years of visual programming in architecture, despite the fast-learning curve, visual programming presents considerable limitations to solve complex problems. To overcome limitations, the authors propose to associate the advantages of visual and textual languages in Python. The article addresses an ongoing research study to implement Computational Methods in Architectural Education. The authors began by describing the general goal of this project, and of this article in particular. This article focuses on the implementation of two disciplines ‘Computation for Architecture in Python’ I and II. The first discipline uses programming based on the construction of functions in the imperative language, implemented in the text editor, in visual programming, using Grasshopper methods. The second discipline, which is under development, intends to teach object-oriented programming. The results of the first discipline are encouraging; despite reported difficulties in programming fundamentals, such as lists, loops and recursion. The development of the second discipline, in object-oriented programming, deals with the concepts of classes and objects, and more abstract principles such abstraction, inheritance, polymorphism or encapsulation. This paradigm allows building robust programs, but requires a more in-depth syntax. The article reports this ongoing research on this new paradigm of object-oriented language, expanding the application of a hybrid visual-textual language in Architecture
keywords	computation, textual programming, visual programming, imperative programming, object oriented programming
series	journal
last changed	2024/04/17 14:30

_id	sigradi2022_235
id	sigradi2022_235
authors	Costa de Jesus, Christian; Chokyu, Margaret; Gomes, Rafael
year	2022
title	School Grammar: An Exploration on Computational Processes in Architecture
source	Herrera, PC, Dreifuss-Serrano, C, Gómez, P, Arris-Calderon, LF, Critical Appropriations - Proceedings of the XXVI Conference of the Iberoamerican Society of Digital Graphics (SIGraDi 2022), Universidad Peruana de Ciencias Aplicadas, Lima, 7-11 November 2022 , pp. 359–370
summary	Standard design for schools in underdeveloped countries seems to be the key to lowering the cost of the whole building process, from design to construction. But since it might not be suitable for different situations, the range of each design is limited. This paper presents a parametric algorithm intended to provide mass customized Architectural solutions for school buildings. A Shape Grammar based methodology for customized school designs is proposed. A set of rules is defined based on chosen characteristics in a corpus of analysis and then is implemented in an open-source modeling software. The algorithm proposed is able to provide solutions for different lots and number of students assisted.
keywords	Shape Grammars, School Architecture, Mass Customized Design, Design Methods, Open-Source Software
series	SIGraDi
email
last changed	2023/05/16 16:55

_id	ascaad2022_065
id	ascaad2022_065
authors	David, Joao; Leitao, Antonio
year	2022
title	Getting a Handle on Floor Plan Analysis: Door Classification in Floor Plans and a Survey on Existing Datasets
source	Hybrid Spaces of the Metaverse - Architecture in the Age of the Metaverse: Opportunities and Potentials [10th ASCAAD Conference Proceedings] Debbieh (Lebanon) [Virtual Conference] 12-13 October 2022, pp. 221-236
summary	Floor plan interpretation and reconstruction is crucial to enable the transformation of drawings to 3D models or different digital formats. It has recently taken advantage of neural-based architectures, especially in the semantic segmentation field. These techniques perform better than traditional methods, but the results depend mainly on the data used to train the networks, which is often crafted for the specific task being performed, making it hard to reuse for different purposes. In this paper, we conduct a literature survey on the existing datasets for floor plan analysis, and we explore how information regarding door placement and orientation can be recovered without having to change the initial data or model. We propose a two-step recognition method based on image segmentation followed by classification of cropped zones to allow data augmentation during training. In the process, we generate a dataset consisting of 35000 annotated door images extracted from an existing dataset.
series	ASCAAD
email
last changed	2024/02/16 13:29

_id	acadia23_v1_116
id	acadia23_v1_116
authors	Derme, Tiziano; Mitterberger, Daniela
year	2023
title	Sylva: An Autonomous Hydroponic Garden Cared for by Two Robots
source	ACADIA 2023: Habits of the Anthropocene: Scarcity and Abundance in a Post-Material Economy [Volume 1: Projects Catalog of the 43rd Annual Conference of the Association of Computer Aided Design in Architecture (ACADIA) ISBN 979-8-9860805-8-1]. Denver. 26-28 October 2023. edited by A. Crawford, N. Diniz, R. Beckett, J. Vanucchi, M. Swackhamer 116-121.
summary	Sylva is an autonomous hydroponic garden, hanging in mid-air and maintained by two robots. Sylva was built for the Princesa of Asturias Foundation in Oviedo, Spain (Figure 1). The aim of the installation was to visualize a live and growing data set in architectural and geometric terms. For this, we designed a robotic garden that changes and grows according to this dataset. This indoor habitat combined various disciplines, including botany, computer vision, robotics, and architectural design. The synthesis of these interdisciplinary components into a cohesive landscape reflects current debates on ecology and the relationship between nature, machines, and automation (Figure 2). It further investigates the role of gardens and nature in the architecture domain. Currently, such a relationship has often been associated with practices of exploitation, domination, and taming of the natural (Blais 2022). Sylva, on the contrary, sees in the typology of the garden an opportunity to expand the term “ecology in architecture” towards new forms of technological mediation ( Derme and Mitterberger 2022).
series	ACADIA
type	project
email
last changed	2024/04/17 13:58

_id	ecaade2022_175
id	ecaade2022_175
authors	Di Carlo, Raffaele, Mittal, Divyae and Vesely, Ondrej
year	2022
title	Generating 3D Building Volumes for a Given Urban Context using Pix2Pix GAN
source	Pak, B, Wurzer, G and Stouffs, R (eds.), Co-creating the Future: Inclusion in and through Design - Proceedings of the 40th Conference on Education and Research in Computer Aided Architectural Design in Europe (eCAADe 2022) - Volume 2, Ghent, 13-16 September 2022, pp. 287–295
doi	https://doi.org/10.52842/conf.ecaade.2022.2.287
summary	Our ability to delegate the most intellectually demanding tasks to machines improves with each passing day. Even in the fields of architecture and design, which were previously thought to be exclusive domain of human creativity and flare, we are moving the first steps towards developing models that can capture the patterns, invisible to the naked eye, embedded in the creative process. These patterns reflect ideas and traditions, imprinted in the collective mind over the course of history, that can be improved upon or serve as a cautionary tale for the new generation of designers in their work of designing an equitable, more inclusive future. Generative Adversarial Networks (GANs) give us the opportunity to turn style and design into learnable features that can be used to automatically generate blueprints and layouts. In this study, we attempt to apply this technology to urban design and to the task of generating a building footprint and volume that fits within the surrounding built environment. We do so by developing a Pix2Pix model composed of a ResNet-6 generator and a Patch discriminator, applying it to satellite views of neighborhoods from across the Netherlands, and then turning the resulting 2D generated building footprint into a reusable 3D model. The model is trained using the national cadastral data and TU Delft 3D BAG dataset. The results show that it is possible to predict a building shape compatible in style and height with the surroundings. Although the model can be used for different applications, we use it as an evaluation tool to compare the design alternatives fitting the desired contextual patterns.
keywords	Generative Adversarial Networks, Urban Design, Pix2Pix, Raster Vectorization, 3D Rendering
series	eCAADe
email
last changed	2024/04/22 07:10

_id	acadia22pr_88
id	acadia22pr_88
authors	Edelmann, Julian
year	2022
title	Voxel Cloud - Volumetric Scaffolding in 3D Pixel Space
source	ACADIA 2022: Hybrids and Haecceities [Projects Catalog of the 42nd Annual Conference of the Association of Computer Aided Design in Architecture (ACADIA) ISBN 979-8-9860805-7-4]. University of Pennsylvania Stuart Weitzman School of Design. 27-29 October 2022. edited by M. Akbarzadeh, D. Aviv, H. Jamelle, and R. Stuart-Smith. 88-93.
summary	The project confronts the exuberance of complex geometries generated by algorithms with the perception of humans, thus it questions the central role of the human within this process by attempting to blend nature and technology. The end result in form of a building application is not a proposal per se, but rather a speculation how data and computation can generate an architecture that can be build by machine and inhabited not just by humans, but also by micro to macro organisms in a post-anthropocentric environment.
series	ACADIA
type	project
email
last changed	2024/02/06 14:06

_id	ecaade2022_44
id	ecaade2022_44
authors	Güzelci, Orkan Zeynel
year	2022
title	Machine Learning in Predicting Section Drawings - Case of Anatolian Seljuk Kümbets
source	Pak, B, Wurzer, G and Stouffs, R (eds.), Co-creating the Future: Inclusion in and through Design - Proceedings of the 40th Conference on Education and Research in Computer Aided Architectural Design in Europe (eCAADe 2022) - Volume 2, Ghent, 13-16 September 2022, pp. 169–176
doi	https://doi.org/10.52842/conf.ecaade.2022.2.169
summary	Funerary structures called kümbet emerged as a unique typology during the Anatolian Seljuk period (1077-1307). This study introduces a machine learning (ML) based model to predict sections of kümbets to complete their missing parts. The proposed ML-based model employs the Pix2Pix method, which is a subset of conditional Generative Adversarial Networks (cGAN).The model is trained over a coupled dataset (interior space and exterior shell) of section drawings. Then, the model is validated by predicting overall shape (exterior shell) for a given input (interior space). The outcomes of the validation phase are evaluated objectively by using structural similarity method (SSIM). Initial findings of the implementation show that the proposed ML-based model has the potential to be used as a design decision support tool for further restitution and renovation works.
keywords	Anatolian Seljuk Architecture, Kümbet, Pix2Pix, Machine Learning, Section
series	eCAADe
email
last changed	2024/04/22 07:10

_id	ijac202220201
id	ijac202220201
authors	Horvath; Anca-Simona
year	2022
title	How we talk(ed) about it: Ways of speaking about computational architecture
source	International Journal of Architectural Computing 2022, Vol. 20 - no. 2, pp. 150–175
summary	If we understand architecture as a three-part system formed by the building, its image, or drawings and imagesdescribing buildings, and the critical discourse around architecture, then the texts or ways of speaking aboutarchitecture play a key role in understanding the field and its development. By analysing a corpus of around 4.6million words from texts written between 2005 and 2020 that form a part of critical discourse in computational architecture (understood as the result of the intense digitalization of the field), this paper aims tomap ways of speaking about computational architecture. This contributes to architectural theory and mighthelp gain a better understanding of the evolution of the digitalization of construction in general. Findings showthat computational architecture is surrounded by a specific way of speaking, hybridized with words fromfields such as biology, neuroscience, arts and humanities, and engineering. While some topics such as‘sustainability’ or ‘biology’ come up consistently in the discourse, others, such as ‘people’ or ‘human’, haveperiods when they are more and less popular. After highlighting open research questions, the paperconcludes by presenting a map of periodic and recurring topics in ways of speaking about computationalarchitecture over the last 15 years, thus tracking and documenting long-term trends, and illuminating patternsin the broader field of digital construction.
keywords	Architectural design, computational architecture, design theory, digital architecture, digital construction, natural language processing
series	journal
last changed	2024/04/17 14:29

_id	caadria2022_231
id	caadria2022_231
authors	Kim, Frederick Chando and Huang, Jeffrey
year	2022
title	Deep Architectural Archiving (DAA), Towards a Machine Understanding of Architectural Form
source	Jeroen van Ameijde, Nicole Gardner, Kyung Hoon Hyun, Dan Luo, Urvi Sheth (eds.), POST-CARBON - Proceedings of the 27th CAADRIA Conference, Sydney, 9-15 April 2022, pp. 727-736
doi	https://doi.org/10.52842/conf.caadria.2022.1.727
summary	With the ‚digital turn‚, machines now have the intrinsic capacity to learn from big data in order to understand the intricacies of architectural form. This paper explores the research question: how can architectural form become machine computable? The research objective is to develop "Deep Architectural Archiving‚ (DAA), a new method devised to address this question. DAA consists of the combination of four distinct steps: (1) Data mining, (2) 3D Point cloud extraction, (3) Deep form learning, as well as (4) Form mapping and clustering. The paper discusses the DAA method using an extensive dataset of architecture competitions in Switzerland (with over 360+ architectural projects) as a case study resource. Machines learn the particularities of forms using 'architectural' point clouds as an opportune machine-learnable format. The result of this procedure is a multidimensional, spatialized, and machine-enabled clustering of forms that allows for the visualization of comparative relationships among form-correlated datasets that exceeds what the human eye can generally perceive. Such work is necessary to create a dedicated digital archive for enhancing the formal knowledge of architecture and enabling a better understanding of innovation, both of which provide architects a basis for developing effective architectural form in a post-carbon world.
keywords	artificial intelligence, deep learning, architectural form, architectural competitions, architectural archive, 3D dataset, SDG 11
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	acadia22pr_100
id	acadia22pr_100
authors	Lee, Yong Ju
year	2022
title	Versatile Bracketry - Contemporary Fabrication Techniques for Traditional Korean Architecture
source	ACADIA 2022: Hybrids and Haecceities [Projects Catalog of the 42nd Annual Conference of the Association of Computer Aided Design in Architecture (ACADIA) ISBN 979-8-9860805-7-4]. University of Pennsylvania Stuart Weitzman School of Design. 27-29 October 2022. edited by M. Akbarzadeh, D. Aviv, H. Jamelle, and R. Stuart-Smith. 100-105.
summary	Versatile Bracketry is an architectural experiment employing algorithmic design technology and 3D printing, manipulating Gong-po—a wooden bracket element found in traditional Korean architecture. Although there has been some recognition and reflection toward the inclusion of traditional forms in modern design, the mainstream in Korean architecture has been Western-oriented. However, advanced computation technology provides both a new perspective and approach in this field, and higher productivity and efficiency.
series	ACADIA
type	project
email
last changed	2024/02/06 14:06

_id	ascaad2022_023
id	ascaad2022_023
authors	Leitao, Antonio; Castelo-Branco, Renata; Caetano, Ines
year	2022
title	Affordable Computation for Architecture
source	Hybrid Spaces of the Metaverse - Architecture in the Age of the Metaverse: Opportunities and Potentials [10th ASCAAD Conference Proceedings] Debbieh (Lebanon) [Virtual Conference] 12-13 October 2022, pp. 415-433
summary	Current architectural requirements prioritize the need to minimize the ecological footprint. By taking advantage of computational design approaches like Algorithmic Design (AD), architects can enhance their design processes with analysis, optimization, and visualization mechanisms, which are critical to explore design solutions that meet this need. However, these mechanisms are also highly time- and resource-consuming, often implying a quality tradeoff or the acquisition of High-Performance Computing (HPC) machines. The latter are not yet affordable for most design studios but, fortunately, they can be contracted as a service. This paper evaluates the impact of computation as a service in architecture and, more specifically, the remote use of HPC for AD, with the aim of reducing the time and costs associated with computationally expensive processes. A set of experiments were made involving analysis, optimization, and rendering of a selected case study. Results indicate that HPC services are advantageous, particularly when performing embarrassingly parallelizable tasks such as rendering. However, some challenges remain, namely the required expertise.
series	ASCAAD
email
last changed	2024/02/16 13:24

_id	ascaad2022_087
id	ascaad2022_087
authors	Mallasi, Zaki
year	2022
title	A Pixels-Based Design Approach for Parametric Thinking in Patterning Dynamic Facades
source	Hybrid Spaces of the Metaverse - Architecture in the Age of the Metaverse: Opportunities and Potentials [10th ASCAAD Conference Proceedings] Debbieh (Lebanon) [Virtual Conference] 12-13 October 2022, pp. 654-673
summary	In today’s Architectural design process, there has been considerable advancements in design computation tools that empowers designer to explore and configure the building façades schemes. However, one could formally argue that some processes are prescribed, lacks automation and are only for the purpose of visualizing the aesthetic design concepts. As a result, these design concept explorations are driven manually to exhibit variations between schemes. To overcome such limitations, the development presented here describes a proactive approach to incorporate parametric design thinking process and Building Information Modeling (BIM). This paper reports on an ongoing development in computational design and its potential application in exploring an interactive façade pattern. The objective is to present the developed approach for exploring façade patterns that responds parametrically to design-performance attractors. Examples of these attractors are solar exposure, interior privacy importance, and aesthetics. It introduces a paradigm-shift in the development of design tools and theory of parameterization in architecture. This work utilizes programming script to manipulate the logic behind placement of faced panels. The placement and sizes for the building facade 3D parametric panels react to variety of Analytical Image Data (AID) as a source for the design-performance data (e.g.: solar exposure, interior privacy importance, and aesthetics). Accordingly, this research developed the PatternGen(c) add-on in Autodesk ® Revit that utilizes a merge (or an overlay) of AID images as a source to dynamically pattern the building façade and generate the facade panels arrangement rules panels on the building exterior. This work concludes by a project case study assessment, that the methodology of applying AID would be an effective dynamic approach to patterning façades. A case-study design project is presented to show the use of the AID pixel-gradient range from Red, Green and Blue as information source value. In light of the general objectives in this study, this work highlights how future designers may shift to a hybrid design process.
series	ASCAAD
email
last changed	2024/02/16 13:29

_id	ascaad2022_063
id	ascaad2022_063
authors	Ozman, Gizem; Selcuk, Semra
year	2022
title	Generating Mass Housing Plans through GANs: A case in TOKI, Turkey
source	Hybrid Spaces of the Metaverse - Architecture in the Age of the Metaverse: Opportunities and Potentials [10th ASCAAD Conference Proceedings] Debbieh (Lebanon) [Virtual Conference] 12-13 October 2022, pp. 17-29
summary	Nowadays, Machine Learning (ML) is frequently used in almost all disciplines having an intersection with technology. Recently, architects are using existing plan data sets in architecture through Deep Learning (DL) algorithms of big data to achieve generative and non-existent plan models by using ML. Especially, Generative Adversarial Neural Networks (GANs), one of the deep learning algorithms, have been in use in the creation of generative models for architectural studies. Within the scope of this paper, architectural drawings were generated by using GANs. This generation method allows for the training of spatial layout planning to networks and for the generation of plans that do not exist in the dataset. Architectural drawings of TOKI (Housing Development Administration of the Republic of Türkiye) mass housing projects were used as datasets. In line with studies already carried out, this study attempts to create a method for further processing of the research. In this study, the differences between the plan typologies generated with raster images and the reality relations in visual productions between graph-based plan layout productions were evaluated. In this context, 157 plan datasets were obtained by multiplying plans which were spatially correlated with the RGB settings of 21 plan typologies. As a result of this research, it has been determined that the spatial layout planning of the HouseGAN algorithm provides TOK?'s current plan typologies of generation together with bubble diagrams. HouseGAN was trained using its dataset and the outputs obtained were realistic background images.
series	ASCAAD
email
last changed	2024/02/16 13:29

_id	ecaade2022_217
id	ecaade2022_217
authors	Panagiotidou, Vasiliki and Koerner, Andreas
year	2022
title	From Intricate to Coarse and Back - A voxel-based workflow to approximate high-res geometries for digital environmental simulations
source	Pak, B, Wurzer, G and Stouffs, R (eds.), Co-creating the Future: Inclusion in and through Design - Proceedings of the 40th Conference on Education and Research in Computer Aided Architectural Design in Europe (eCAADe 2022) - Volume 1, Ghent, 13-16 September 2022, pp. 491–500
doi	https://doi.org/10.52842/conf.ecaade.2022.1.491
summary	Digital environmental simulations can present a computational bottleneck concerning the complexity of geometry. Therefore, a series of workarounds, ranging from cloud-based solutions to machine learning simulations as surrogate simulations are conventionally applied in practice. Concurrently, contemporary advances in procedural modelling in architecture result in design concepts with high polygon counts. This leads to an ever- increasing resolution discrepancy between design and analysis models. Responding to this problem, this research presents a step-by-step approximation workflow for handling and transferring high-resolution geometries between procedural modelling and environmental simulation software. The workflow is intended to allow designers to quickly assess a design’s interaction with environmental parameters such as airflow and solar radiation and further articulate them. A controllable voxelization procedure is applied to approximate the original geometry and therefore reduce the resolution. Controllable in this context refers to the user’s ability to locally adjust the voxel resolution to fit design needs. After export and simulation, 3d results are imported back into the design environment. The colour properties are re-mapped onto the original high- resolution geometry following a weighted proximity technique. The developed data transfer pipeline allows designers to integrate environmental analysis during initial design steps, which is essential for accessibility in the design profession. This can help to environmentally inform generative designs as well as to make simulation workflows more accessible when working with a wider range of geometries. In this, it reduces the perceived discrepancy between the concept and simulation model. This eases the use and allows a wider audience of users to develop co-creation processes between computation, architecture, and environment.
keywords	Simulation, Accessibility, Computation, Environmental Data, Workflow
series	eCAADe
email
last changed	2024/04/22 07:10

_id	ascaad2022_110
id	ascaad2022_110
authors	Salem, Mona; Moussa, Ramy
year	2022
title	A Hybrid Approach Based on Building Physics and Machine Learning for Thermal Comfort Prediction in Smart Buildings
source	Hybrid Spaces of the Metaverse - Architecture in the Age of the Metaverse: Opportunities and Potentials [10th ASCAAD Conference Proceedings] Debbieh (Lebanon) [Virtual Conference] 12-13 October 2022, pp. 253-263
summary	One of the most important challenges facing the world is the application of modern technology in order to create smart buildings that achieve sustainable development goals (SDGs). Thermal comfort and reduction of energy consumption in buildings are considered important factors which, in turn, are reflected in creating a healthy environment and improving human productivity. Internet of Things (IoT) provides an ideal solution for collecting real-time data on the factors affecting indoor thermal comfort and energy consumption. However, comfort level is subjective and depends on many factors, which may not be learned by conventional models, an integrated model depending on thermal comfort factors is needed. In this work, a hybrid physics-based model incorporated with machine learning techniques is used for the prediction of thermal comfort inside buildings. XGBoost (eXtreme Gradient Boost) algorithm method was used due to its abilities to handle complex problems. A calculated dataset was extracted from the physics-based model gathered with the environmental variables data such as humidity, moisture, temperature, and air velocity collected from IoT devices. The results show an improvement in the prediction of the thermal comfort approach as compared with the conventional models. The XGBoost algorithm can exhibit an effective solution for eliminating deficiencies of traditional models and can be used when designing smart buildings, simulating, and evaluating the designed buildings, controlling energy consumption, and achieving thermal comfort.
series	ASCAAD
email
last changed	2024/02/16 13:38

_id	ascaad2022_099
id	ascaad2022_099
authors	Sencan, Inanc
year	2022
title	Progeny: A Grasshopper Plug-in that Augments Cellular Automata Algorithms for 3D Form Explorations
source	Hybrid Spaces of the Metaverse - Architecture in the Age of the Metaverse: Opportunities and Potentials [10th ASCAAD Conference Proceedings] Debbieh (Lebanon) [Virtual Conference] 12-13 October 2022, pp. 377-391
summary	Cellular automata (CA) is a well-known computation method introduced by John von Neumann and Stanislaw Ulam in the 1940s. Since then, it has been studied in various fields such as computer science, biology, physics, chemistry, and art. The Classic CA algorithm is a calculation of a grid of cells' binary states based on neighboring cells and a set of rules. With the variation of these parameters, the CA algorithm has evolved into alternative versions such as 3D CA, Multiple neighborhood CA, Multiple rules CA, and Stochastic CA (Url-1). As a rule-based generative algorithm, CA has been used as a bottom-up design approach in the architectural design process in the search for form (Frazer,1995; Dinçer et al., 2014), in simulating the displacement of individuals in space, and in revealing complex relations at the urban scale (Güzelci, 2013). There are implementations of CA tools in 3D design software for designers as additional scripts or plug-ins. However, these often have limited ability to create customized CA algorithms by the designer. This study aims to create a customizable framework for 3D CA algorithms to be used in 3D form explorations by designers. Grasshopper3D, which is a visual scripting environment in Rhinoceros 3D, is used to implement the framework. The main difference between this work and the current Grasshopper3D plug-ins for CA simulation is the customizability and the real-time control of the framework. The parameters that allow the CA algorithm to be customized are; the initial state of the 3D grid, neighborhood conditions, cell states and rules. CA algorithms are created for each customizable parameter using the framework. Those algorithms are evaluated based on the ability to generate form. A voxel-based approach is used to generate geometry from the points created by the 3D cellular automata. In future, forms generated using this framework can be used as a form generating tool for digital environments.
series	ASCAAD
email
last changed	2024/02/16 13:38

_id	ascaad2022_060
id	ascaad2022_060
authors	Senem, Mehmet; Koc, Mustafa; Tuncay, Hayriye; As, Imdat
year	2022
title	Using Deep Learning to Generate Front and Backyards in Landscape Architecture
source	Hybrid Spaces of the Metaverse - Architecture in the Age of the Metaverse: Opportunities and Potentials [10th ASCAAD Conference Proceedings] Debbieh (Lebanon) [Virtual Conference] 12-13 October 2022, pp. 2-16
summary	The use of artificial intelligence (AI) engines in the design disciplines is a nascent field of research, which became very popular over the last decade. In particular, deep learning (DL) and related generative adversarial networks (GANs) proved to be very promising. While there are many research projects exploring AI in architecture and urban planning, e.g., in order to generate optimal floor layouts, massing models, evaluate image quality, etc., there are not many research projects in the area of landscape architecture - in particular the design of two-dimensional garden layouts. In this paper, we present our work using GANs to generate optimal front- and backyard layouts. We are exploring various GAN engines, e.g., DCGAN, that have been successfully used in other design disciplines. We used supervised and unsupervised learning utilizing a massive dataset of about 100,000 images of front- and backyard layouts, with qualitative and quantitative attributes, e.g., idea and beauty scores, as well as functional and structural evaluation scores. We present the results of our work, i.e., the generation of garden layouts, and their evaluation, and speculate on how this approach may help landscape architects in developing their designs. The outcome of the study may also be relevant to other design disciplines.
series	ASCAAD
email
last changed	2024/02/16 13:29

_id	acadia22_628
id	acadia22_628
authors	Sung, Woongki; Nagakura, Takehiko; Tsai, Daniel
year	2022
title	Design Contextualism by AI
source	ACADIA 2022: Hybrids and Haecceities [Proceedings of the 42nd Annual Conference of the Association of Computer Aided Design in Architecture (ACADIA) ISBN 979-8-9860805-8-1]. University of Pennsylvania Stuart Weitzman School of Design. 27-29 October 2022. edited by M. Akbarzadeh, D. Aviv, H. Jamelle, and R. Stuart-Smith. 628-637.
summary	This paper presents a data- driven method for encoding and representing the statistical information of an architectural site layout in the form of a Bayesian network. Given a set of simplified satellite photos and maps, the site layout model is formulated that consists of variables of interest. Structured learning is performed to find an optimal Bayesian network structure that best fits the dataset and is then trained to calculate its parameters.
series	ACADIA
type	paper
email
last changed	2024/02/06 14:04

_id	ascaad2022_047
id	ascaad2022_047
authors	Tu, Han; Yang, Chunfeng
year	2022
title	Mindful Space in Sentences: A Dataset of Virtual Emotions for Natural Language Classification
source	Hybrid Spaces of the Metaverse - Architecture in the Age of the Metaverse: Opportunities and Potentials [10th ASCAAD Conference Proceedings] Debbieh (Lebanon) [Virtual Conference] 12-13 October 2022, pp. 713-730
summary	Spatial emotions have played a critical role in visual-spatial environmental assessment, which can be assessed using bio-sensors and language description. However, information on virtual spatial emotion assessment with objective emotion labels and natural language processing (NLP) is insufficient in literature. Thus, designers’ ability to assess spatial design quantitatively and cost effectively is limited before the design is finalized. This research measures the emotions expressed using electroencephalograms (EEGs) and descriptions in virtual reality (VR) spaces with different parameters. First, 26 subjects experienced 10 designed virtual spaces with a VR headset (Quest 2 device) corresponding to the different space parameters of shape, height, width, and length. Simultaneously, the EEG measured the emotions of the subjects using four electrodes and the five brain waves. Second, two labels – calm and active – were produced using EEGs to describe these virtual reality spaces. Last, this labeled emotion dataset compared the differences among the virtual spaces, human feelings, and the language description of the participants in the VR spatial experience. Experimental results show that the parameter changes of VR spaces can arouse significant fluctuations in the five brain waves. The EEG brain wave signals, in turn, can label the virtual rooms with calm and active emotions. Specifically, in terms of VR spaces and emotions, the experiments find that more relative spatial height results in less active emotions, while round spaces arouse calmness in the human brain waves. Moreover, the precise connection among VR spaces, brain waves in emotion, and languages still needs further research. This research attempts to offer a useful emotion measurement tool in virtual architectural design and description using EEGs. This research identifies potentials for future applications combining physiological metrics and AI methods, i.e., machine learning for synthetic design generation and evaluation.
series	ASCAAD
email
last changed	2024/02/16 13:29

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