Cumincad : CUMINCAD Papers : Search Results

CumInCAD is a Cumulative Index about publications in Computer Aided Architectural Design
supported by the sibling associations ACADIA, CAADRIA, eCAADe, SIGraDi, ASCAAD and CAAD futures

PDF papers
References

Hits 1 to 20 of 658

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

_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	ecaade2022_411
id	ecaade2022_411
authors	Cesar Rodrigues, Ricardo, Rubio Koga, Renan, Hitomi Hirota, Ercilia and Bertola Duarte, Rovenir
year	2022
title	Mapping Space Allocation with Artificial Intelligence - An approach towards mass customized housing units
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. 631–640
doi	https://doi.org/10.52842/conf.ecaade.2022.2.631
summary	Artificial Intelligence represents a substantial part of the available tools on architectural design, especially for Space Layout Planning (SLP). At the same time, the challenge of Mass Customization (MC) is to increase the product variety while maintaining a good cost-benefit ratio. Thus, this research aims to identify new, valid, and easily understandable data patterns through human-machine interaction in an attempt to deal with the challenges of MC during the early phases of SLP. The Design Science Research method was adopted to develop a digital artifact based on deep generative models and a reverse image search engine. The results indicate that the artifact can deliver a series of design alternatives and enhance the navigation process in the solution space, besides giving key insights on dataset design for further research.
keywords	Floor plans, Generative Adversarial Networks, Mass Customization
series	eCAADe
email
last changed	2024/04/22 07:10

_id	ecaade2022_78
id	ecaade2022_78
authors	Eroglu, Ruºen and Gül, Leman Figen
year	2022
title	Architectural Form Explorations through Generative Adversarial Networks - Predicting the potentials of StyleGAN
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. 575–582
doi	https://doi.org/10.52842/conf.ecaade.2022.2.575
summary	In recent years, generative models have been rapidly transforming into a broad field of research, and artificial intelligence (AI) works are increasing. Since deep learning technologies such as Generative Adversarial Networks (GANs) providing synthesized new images are becoming more accessible, researchers in the design and related fields are very much interested in adapting GANs into practice. Especially, StyleGAN has a strong capability for image learning, reconstruction simulation, and absorbing the pixel characteristics of images in the input dataset. StyleGAN also produces similar imitation outputs and summarizes all the input data into one "average output". The study aims to reveal the potential of these outputs that can be employed as a visual inspiration aid for designers. This article will discuss the outputs of the experiments, findings, and prospects of StyleGAN.
keywords	Artificial Intelligence, Machine Learning, Generative Adversarial Networks, StyleGAN
series	eCAADe
email
last changed	2024/04/22 07:10

_id	caadria2022_239
id	caadria2022_239
authors	Huang, Chenyu, Zhang, Gengjia, Yin, Minggang and Yao, Jiawei
year	2022
title	Energy-driven Intelligent Generative Urban Design, Based on Deep Reinforcement Learning Method With a Nested Deep Q-R Network
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. 233-242
doi	https://doi.org/10.52842/conf.caadria.2022.1.233
summary	To attain "carbon neutrality," lowering urban energy use and increasing the use of renewable resources have become critical concerns for urban planning and architectural design. Traditional energy consumption evaluation tools have a high operational threshold, requiring specific parameter settings and cross-disciplinary knowledge of building physics. As a result, it is difficult for architects to manage energy issues through 'trial and error' in the design process. The purpose of this study is to develop an automated workflow capable of providing urban configurations that minimizing the energy use while maximizing rooftop photovoltaic power potential. Based on shape grammar, parametric meta models of three different urban forms were developed and batch simulated for its energy performance. Deep reinforcement learning (DRL) is introduced to find the optimal solution of the urban geometry. A neural network was created to fit a real-time mapping of urban form indicators to energy performance and was utilized to predict reward for the DRL process, namely a Deep R-Network, while nested within a Deep Q-Network. The workflow proposed in this paper promotes efficiency in optimizing the energy performance of solutions in the early stages of design, as well as facilitating a collaborative design process with human-machine interaction.
keywords	energy-driven urban design, intelligent generative design, rooftop photovoltaic power, deep reinforcement learning, SDG 11, SDG 12
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	ecaade2022_203
id	ecaade2022_203
authors	Kim, Frederick Chando and Huang, Jeffrey
year	2022
title	Perspectival GAN - Architectural form-making through dimensional transformation
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. 341–350
doi	https://doi.org/10.52842/conf.ecaade.2022.1.341
summary	With the ascendance of Generative Adversarial Networks (GAN), promising prospects have arisen from the abilities of machines to learn and recognize patterns in 2D datasets and generate new results as an inspirational tool in architectural design. Insofar as the majority of ML experiments in architecture are conducted with imagery based on readily available 2D data, architects and designers are faced with the challenge of transforming machine-generated images into 3D. On the other hand, GAN-generated images are found to be able to learn the 3D information out of 2D perspectival images. To facilitate such transformation from 2D and 3D data in the framework of deep learning in architecture, this paper explores making new architectural forms from flat GAN images by employing traditional tools of projective geometry. The experiments draw on Brook Taylor’s 19th- century theorem of inverse projection system for creating architectural form from perspectival information learned from GAN images of Swiss alpine architecture. The research develops a parametric tool that automates the dimensional transformation of 2D images into 3D architectural forms. This research identifies potential synergic interactions between traditional tools and techniques of architects and deep learning algorithms to achieve collective intelligence in designing and representing creative architecture forms between humans and machines.
keywords	Machine Learning, GAN, Architectural Form, Perspective Projection, Inverse Perspective, Digital Representation
series	eCAADe
email
last changed	2024/04/22 07:10

_id	caadria2022_172
id	caadria2022_172
authors	Xiao, Yahan, Hotta, Akito, Fuji, Takaaki, Kikuzato, Naoto and Hotta, Kensuke
year	2022
title	Urban Scale 3 Dimensional CFD Approximation Based on Deep Learning A Quick Air Flow Prediction for Volume Study in Architecture Early Design Stage
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. 303-312
doi	https://doi.org/10.52842/conf.caadria.2022.1.303
summary	The CFD generated by an object and its surroundings is critical during architectural design. The most common method of CFD calculation is to discretize the spatial region into small cells to form a three-dimensional grid or grid point and then apply a suitable algorithm to solve the equation iteratively until the steady state, which usually takes a significant amount of time before it converges to the exact solution of the problem. Deep learning is a subset of a Machine Learning algorithm that uses multiple layers of neural networks to perform in processing data and computations on a large amount of data. This paper presents a deep learning model CNN architecture to provide a quick and approximated 3-dimensional solution for the CFD. Our network speeds up 45 times compared to the standard CFD solver. Moreover, our network is able to predict a CFD in which the wind inlet and outlet appear at the same surface of a wind tunnel.
keywords	Urban Microclimate, Machine Learning, 3D Unet, Residual Block, 3 Dimensional CFD Prediction, SDG 11
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	ijac202220203
id	ijac202220203
authors	Dzieduszyñski, Tomasz
year	2022
title	Machine learning and complex compositional principles in architecture: Application of convolutional neural networks for generation of context-dependent spatial compositions
source	International Journal of Architectural Computing 2022, Vol. 20 - no. 2, pp. 196–215
summary	A substantial part of architectural and urban design involves processing of compositional interdependenciesand contexts. This article attempts to isolate the problem of spatial composition from the broader category ofsynthetic image processing. The capacity of deep convolutional neural networks for recognition and utilization of complex compositional principles has been demonstrated and evaluated under three scenariosvarying in scope and approach. The proposed method reaches 95.1%–98.5% efficiency in the generation ofcontext-fitting spatial composition. The technique can be applied for the extraction of compositionalprinciples from the architectural, urban, or artistic contexts and may facilitate the design-related decisionmaking by complementing the required expert analysis
keywords	Spatial composition, architecture, convolutional neural network, ordering principles, machine learning, image generation, design, CAAD
series	journal
last changed	2024/04/17 14:29

_id	ecaade2022_222
id	ecaade2022_222
authors	Eisenstadt, Viktor, Bielski, Jessica, Langenhan, Christoph, Althoff, Klaus-Dieter and Dengel, Andreas
year	2022
title	Autocompletion of Design Data in Semantic Building Models using Link Prediction and Graph Neural Networks
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. 501–510
doi	https://doi.org/10.52842/conf.ecaade.2022.1.501
summary	This paper presents an approach for AI-based autocompletion of graph-based spatial configurations using deep learning in the form of link prediction through graph neural networks. The main goal of the research presented is to estimate the probability of connections between the rooms of the spatial configuration graph at hand using the available semantic information. In the context of early design stages, deep learning-based prediction of spatial connections helps to make the design process more efficient and sustainable using the past experiences collected in a training dataset. Using the techniques of transfer learning, we adapted methods available in the modern graph-based deep learning frameworks in order to apply them for our autocompletion purposes to suggest possible further design steps. The results of training, testing, and evaluation showed very good results and justified application of these methods.
keywords	Spatial Configuration, Autocompletion, Link Prediction, Deep Learning
series	eCAADe
email
last changed	2024/04/22 07:10

_id	ecaade2022_399
id	ecaade2022_399
authors	Johanes, Mikhael and Huang, Jeffrey
year	2022
title	Deep Learning Spatial Signature - Inverted GANs for Isovist representation in architectural floorplan
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. 621–629
doi	https://doi.org/10.52842/conf.ecaade.2022.2.621
summary	The advances of Generative Adversarial Networks (GANs) have provided a new experimental ground for creative architecture processes. However, the analytical potential of the latent representation of GANs is yet to be explored for architectural spatial analysis. Furthermore, most research on GANs for floorplan learning in architecture uses images as its main representation medium. This paper presents an experimental framework that uses one-dimensional periodic isovist samples and GANs inversion to recover its latent representation. Access to GANs’ latent space will open up a possibility for discriminative tasks such as classification and clustering analysis. The resulting latent representation will be investigated to discover its analytical capacity in extracting isovist spatial patterns from thousands of floorplans data. In this experiment, we hypothetically conclude that the spatial signature of the architectural floor plan could be derived from the degree of regularity of isovist samples in the latent space structure. The finding of this research will enable a new data-driven strategy to measure spatial quality using isovist and provide a new way for indexing architectural floorplan.
keywords	Machine Learning, Isovist, Latent Representation, GANs Inversion, Spatial Signature
series	eCAADe
email
last changed	2024/04/22 07:10

_id	sigradi2022_168
id	sigradi2022_168
authors	Koh, Immanuel
year	2022
title	Palette2Interior Architecture: From Syntactic and Semantic Colour Palettes to Generative Interiors with Deep Learning
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. 187–198
summary	Colour palettes have long played a significant role in not only capturing design ambience (e.g., as mood boards), but more significantly, in translating an abstract intuition into an explicit ordering mechanism for design representation and synthesis, whether it is in the discipline of graphic design, interior design or architectural design. Might this difficult process of design synthesis from a low-dimensional colour input domain to a high-dimensional spatial design output domain be computationally mapped? Using today’s generative adversarial networks (GANs), the paper aims to investigate this plausibility, and in doing so, hoping to envision an AI-augmented design workflow and tooling. Newly-created datasets are made procedurally and used to train three different types of deep learning models in the specific context of generating living room interior layouts. The results suggest that a combination of syntactic and semantic generative processes is necessary for a critical appropriation of such AI models
keywords	Machine Learning, Artificial Intelligence, Deep Neural Networks, Colour Palette, Interior Design
series	SIGraDi
email
last changed	2023/05/16 16:55

_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	sigradi2022_53
id	sigradi2022_53
authors	Stuart-Smith, Robert; Danahy, Patrick
year	2022
title	3D Generative Design for Non-Experts: Multiview Perceptual Similarity with Agent-Based Reinforcement Learning
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. 115–126
summary	Advances in additive manufacturing allow architectural elements to be fabricated with increasingly complex geometrical designs, however, corresponding 3D design software requires substantial knowledge and skill to operate, limiting adoption by non-experts or people with disabilities. Established non-expert approaches typically constrain geometry, topology, or character to a pre-established configuration, rather than aligning to figural and aesthetic characteristics defined by a user. A methodology is proposed that enables a user to develop multi-manifold designs from sketches or images in several 3d camera projections. An agent-based design approach responds to computer vision analysis (CVA) and Deep Reinforcement Learning (RL) to design outcomes with perceptual similarity to user input images evaluated by Structural Similarity Indexing (SSIM). Several CVA and RL ratios were explored in training models and tested on untrained images to evaluate their effectiveness. Results demonstrate a combination of CVA and RL motion behavior can produce meshes with perceptual similarity to image content.
keywords	Generative Design, Machine Learning, Agent-Based Systems, Non-Expert Design
series	SIGraDi
email
last changed	2023/05/16 16:55

_id	caadria2022_411
id	caadria2022_411
authors	Yang, Xuyou, Bao, Ding Wen, Yan, Xin and Zhao, Yucheng
year	2022
title	OptiGAN: Topological Optimization in Design Form-Finding With Conditional GANs
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. 121-130
doi	https://doi.org/10.52842/conf.caadria.2022.1.121
summary	With the rapid development of computers and technology in the 20th century, the topological optimisation (TO) method has spread worldwide in various fields. This novel structural optimisation approach has been applied in many disciplines, including architectural form-finding. Especially Bi-directional Evolutionary Structural Optimisation (BESO), which was proposed in the 1990s, is widely used by thousands of engineers and architects worldwide to design innovative and iconic buildings. To integrate topological optimisation with artificial intelligence (AI) algorithms and to leverage its power to improve the diversity and efficiency of the BESO topological optimisation method, this research explores a non-iterative approach to accelerate the topology optimisation process of structures in architectural form-finding via conditional generative adversarial networks (GANs), which is named as OptiGAN. Trained with topological optimisation results generated through Ameba software, OptiGAN is able to predict a wide range of optimised architectural and structural designs under defined conditions.
keywords	BESO (bi-directional evolutionary structural optimisation), Artificial Intelligence, Deep Learning, Topological Optimisation, Form-Finding, GAN (Generative Adversarial Networks), SDG 12, SDG 9
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	caadria2022_503
id	caadria2022_503
authors	Yousif, Shermeen and Vermisso, Emmanouil
year	2022
title	Towards AI-Assisted Design Workflows for an Expanded Design Space
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. 335-344
doi	https://doi.org/10.52842/conf.caadria.2022.2.335
summary	The scope of this paper is to formulate and evaluate the structure of a viable design workflow that combines a variety of computational tools and uses artificial intelligence (AI) to enhance the designer‚s capacity to explore design options within an expanded design space. In light of the autonomous and progressively post-anthropocentric generative capability of recent AI strategies for architectural design, we are interested in investigating some of the challenges involved in the insertion of such AI strategies into a new generative design system, involving data curation and the placement of any AI-assisted model in the overall workflow, as well as its (AI‚s) reciprocity with other computational methods such as discrete assembly and agent-based modeling. The paper presents our interrogation of the proposed AI-assisted framework, demonstrated in experiments of formulating multiple design workflows following different strategies. The workflow strategies show that integrating AI networks into a framework with other computational tools affords a different kind of design exploration than other methods; the prospect of novel solutions is heavily dependent on the interconnectedness of such methods and the dataset curation process. Collectively, the work contributes to innovation in architectural education and practice through enhancing scientific research (in line with UN Sustainable Development Goal 9).
keywords	Artificial Intelligence, Deep Learning, AI-assisted Design Workflows, Design Space Exploration, Generative Systems, SDG 9
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	ecaade2022_202
id	ecaade2022_202
authors	Acican, Oyku and Luyten, Laurens
year	2022
title	Experiential Learning of Structural Systems - Comparison of design-based and experiment-based pedagogies
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. 535–544
doi	https://doi.org/10.52842/conf.ecaade.2022.2.535
summary	This research aims to compare two experiential learning methods’ effectiveness for (1) a deeper understanding of structural behaviour, and (2) skills to design architectural forms that are structurally informed. A course was planned to investigate the effect of the type and order of the two teaching units: (1) guided experiments on a parametric design model, and (2) parametric design of a tower and custom experiments using Grasshopper and Karamba. Results indicate that the group that started with the experiments learned to ask the relevant questions by experimenting with the appropriate parameters that helped them to find the structural principles and apply them during their design phase. The group that started with the design were lost in the structural concepts and in identifying the meaningful parameters to test for. However, after the experiment was completed, this group could make a knowledge transfer. Acquisition of structures knowledge may require the experience of multiple situations while the application of this knowledge may involve selecting the relevant structural experience with the architectural form-finding process. In the future, a proposed experiential learning method will be compared with an instructive learning approach of structural systems for architecture students.
keywords	Structures Education, Experiential Learning, Parametric Structural Analysis, Comparative Pedagogy
series	eCAADe
email
last changed	2024/04/22 07:10

_id	ecaade2022_221
id	ecaade2022_221
authors	Delikanli, Burak and Gül, Leman Figen
year	2022
title	Towards to the Hyperautomation - An integrated framework for Construction 4.0: a case of Hookbot as a distributed reconfigurable robotic assembly system
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. 389–398
doi	https://doi.org/10.52842/conf.ecaade.2022.2.389
summary	Almost every technological and industrial concept changes the built environment around us and our understanding of the architectural practice. Recently, Hyperautomation, an all-encompassing digital transformation with the help of advanced techniques, has been presented as a game-changing concept that can affect any industry. Despite this promising concept, the Architecture, Engineering, and Construction (AEC) industry seems far behind the latest technological breakthroughs and automation of processes compared to other industries. Therefore, this study provides a better understanding of adopting the novel Hyperautomation paradigm in the AEC industry by focusing on Industry 4.0. In this context, the first section introduces the Construction 4.0 concept, its counterpart in the AEC industry, briefly mentions fundamental approaches and indicates the need for a framework. The second section introduces an integrated framework throughout the entire building life-cycle for design and construction processes and exemplifies the stages in an autonomous system and their interrelationships. The third section presents a hypothetical case, a distributed reconfigurable robotic assembly system, and the assembler ‘HookBot’ to understand the relationships in an autonomous system better. The last section discusses the place of the Hyperautomation paradigm in architecture.
keywords	Autonomy, Autonomous Systems, Construction 4.0, Assembly Robotics
series	eCAADe
email
last changed	2024/04/22 07:10

_id	ecaade2022_378
id	ecaade2022_378
authors	Dokonal, Wolfgang, Mosler, Pascal, Gehring, Maximilian and Rüppel, Uwe
year	2022
title	On the Road towards? - Developing a toolset for a low-cost VR-enhanced design approach
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. 163–169
doi	https://doi.org/10.52842/conf.ecaade.2022.1.163
summary	For several years, we have been experimenting with Head Mounted Displays (HMD) being used as Virtual Reality (VR) interfaces. We tried to develop easy to use workflows for these devices so that they can be integrated into the architectural design process. Additionally, we were able to upgrade those systems with sensor boxes and designed new systems for movement control, collision detection, and additional effects for an increased feeling of immersion. Our systems focused on the use of the ultra-low-cost HMD devices and the intention was to clarify how much benefit within the design process we can achieve already at an early design phase in using this workflow without having extremely detailed models available. We experienced with our students in the past that the change from analogue design methods towards software-supported design reduced their understanding of space and scale and was therefore a negative factor in the design process. In this paper, we will focus on scripts for the game engine Unity with new functionalities that we tested with the students in two workshops.
keywords	Sense of Space, Virtual Reality, Unity Toolset
series	eCAADe
email
last changed	2024/04/22 07:10

_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	ecaade2022_398
id	ecaade2022_398
authors	Dzurilla, Dalibor and Achten, Henri
year	2022
title	What’s Happening to Architectural Sketching? - Interviewing architects about transformation from traditional to digital architectural sketching as a communicational tool with clients
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. 389–398
doi	https://doi.org/10.52842/conf.ecaade.2022.1.389
summary	The paper discusses 23 interviewed architects in practice about the role of traditional and digital sketching (human-computer interaction) in communication with the client. They were selected from 1995 to 2018 (the interval of graduation) from three different countries: the Czech Republic (CR), Slovakia (SR), Netherland (NR). To realize three blending areas that impact the approach to sketching: (I) Traditional hand and physical model studies (1995-2003). (II)Transition form - designing by hand and PC (2004–2017). (III) Mainly digital and remote forms of designing (2018–now). Interviews helped transform 31 “parameters of tools use” from the previous theoretical framework narrowed down into six main areas: (1) Implementation; (2)Affordability; (3)Timesaving; (4) Drawing support; (5) Representativeness; (6) Transportability. Paper discusses findings from interviewees: (A) Implementation issues are above time and price. (B) Strongly different understanding of what digital sketching is. From drawing in Google Slides by mouse to sketching in Metaverse. (C) Substantial reduction of traditional sketching (down to a total of 3% of the time) at the expense of growing responsibilities. (D) 80% of respondents do not recommend sketching in front of the client. Also, other interesting findings are further described in the discussion.
keywords	Architectural Sketch, Digital Sketch, Effective Visual Communication
series	eCAADe
email
last changed	2024/04/22 07:10

_id	ecaade2022_396
id	ecaade2022_396
authors	Hamzaoglu, Begüm, Özkar, Mine and Aydin, Serdar
year	2022
title	Towards a Digital Practice of Historical Stone Carvings
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. 227–234
doi	https://doi.org/10.52842/conf.ecaade.2022.2.227
summary	Local traditional crafts in various parts of the world are being transformed by digitalization in tandem with broader social and economic changes. Mardin, a historical and cultural hub in southeast Anatolia, presents an exemplary case with its stone architecture. Whereas the number of skilled craftsmen is diminishing, digital fabrication ateliers are increasingly in demand in the city and rising in number. Training programs have already started integrating CNC milling-based techniques. However, despite the growing interest in adapting computational processes, how the craft knowledge is documented and conveyed to multiple actors for maintaining and even increasing the quality of workmanship is yet to be explored. We present a novel way to document carving procedures and to create an inventory of the 3D motifs using cross-sections as complements to front views. The research engages end-user participants of different backgrounds, such as stone cutting technologies and architecture, with little or no practical knowledge of digital manufacturing. The work focuses on a selection of motifs from the Syriac stone carving heritage in Mardin, the documentation of which is very limited. The proposed workflow begins with recording the surface depth and the variations in the cross-section using digital scans. In the second stage, we consider the potential subtractive transformations that result in the final form and reconstruct them as milling operations with a parametric and procedural modeling approach. Various milling processes are derived by relating the shapes to the available cutting tools and materials. The study contributes to creating the inventory of an engraving culture that has lasted for hundreds of years while developing a generally applicable and transferable knowledge base to increase its sharing and dissemination in the age of digitally supported production.
keywords	Cultural Heritage, Digital Fabrication, Craft Knowledge, Digital Craft, Analog-Digital
series	eCAADe
email
last changed	2024/04/22 07:10

For more results click below: