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	caadria2022_205
id	caadria2022_205
authors	Bielski, Jessica, Langenhan, Christoph, Ziegler, Christoph, Eisenstadt, Viktor, Dengel, Andreas and Althoff, Klaus-Dieter
year	2022
title	Quantifying the Intangible, A Tool for Retrospective Protocol Studies of Sketching During the Early Conceptual Design of Architecture
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. 403-411
doi	https://doi.org/10.52842/conf.caadria.2022.1.403
summary	Sketching is a craft supporting the development of ideas and design intentions, as well as an effective tool for communication during the early architectural design stages by making them tangible. Even though sketch-based interaction is a promising approach for Computer-Aided Architectural Design (CAAD) systems, it remains a challenge for computers to recognise information in a sketch. Design protocol studies conducted to deconstruct the sketch and sketching process collect solely qualitative data so far. However, the 'metis' projects aim to create an intelligent design assistant, using an artificial neural network (ANN), in the manner of Negroponte‚s Architecture Machine. By assimilating to the user's idiosyncrasies, the system suggests further design steps to the architect to improve the design decision making process for economic growth, qualitative self-education through the dialogue and reducing stress. For training such ANN quantitative data is needed. In order to produce quantifiable results from such a study, we propose our open-source web-tool ‚Sketch Protocol Analyser‚. By correlating different parameters (i.e. video, transcript and sketch built) through the same labels and their timestamps, we create quantitative data for further use.
keywords	Design Protocol Studies, Sketching, Data Collection, Architectural Design Process, ANN, SDG 3, SDG 4, SDG 8, SDG 9
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	caadria2022_208
id	caadria2022_208
authors	Bielski, Jessica, Langenhan, Christoph, Ziegler, Christoph, Eisenstadt, Viktor, Petzold, Frank, Dengel, Andreas and Althoff, Klaus-Dieter
year	2022
title	The What, Why, What-If and How-To for Designing Architecture, Explainability for Auto-Completion of Computer-Aided Architectural Design of Floor Plan Layouting During the Early Design Stages
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. 435-444
doi	https://doi.org/10.52842/conf.caadria.2022.2.435
summary	In the next thirty years, the world's population is expected to increase to ten billion people, posing major challenges for the construction industry. To meet the growing demands for residential housing in the future, architects need to work faster, more efficiently, and more sustainably, while increasing architectural quality. The hypothetical intelligent design assistant WHITE BRIDGE, based on the methods of the 'metis' projects, suggests further design steps to support the architectural design decision-making processes of the early design phases. This facilitates faster and better decisions early in the process for a more responsible resource consumption, better mental well-being, and ultimately economic growth. Through a case study we investigate if additional information supports the understanding of these suggestions to reduce the cognitive workload of architectural design decisions on the backdrop of their respective representation. The paper contributes an approach for visualising explanations of an intelligent design assistant, their integration into paper prototypes for case studies, and a workflow for data collection and analysis. The results suggest that the cognitive horizon of the architects is broadened by the explanations, while the visualisation methods significantly influence the usefulness and use of the conveyed information within the explanations.
keywords	Explainability, Artificial intelligence, XAI, SDG 3, SDG 8, SDG 12
series	CAADRIA
email
last changed	2022/07/22 07:34

_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	caadria2022_166
id	caadria2022_166
authors	Eisenstadt, Viktor, Bielski, Jessica, Mete, Burak, Langenhan, Christoph, Althoff, Klaus-Dieter and Dengel, Andreas
year	2022
title	Autocompletion of Floor Plans for the Early Design Phase in Architecture: Foundations, Existing Methods, and Research Outlook
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. 323-332
doi	https://doi.org/10.52842/conf.caadria.2022.1.323
summary	This paper contributes the current research state and possible future developments of AI-based autocompletion of architectural floor plans and shows demand for its establishment in computer-aided architectural design to facilitate decent work, economic growth through accelerating the design process to meet the future workload. Foundations of data representations together with the autocompletion contexts are defined, existing methods described and evaluated in the integrated literature review, and criteria for qualitative and sustainable autocompletion are proposed. Subsequently, we contribute three unique deep learning-based autocompletion methods currently in development for the research project metis-II. They are described in detail from a technical point of view on the backdrop of how they adhere to the proposed criteria for creating our novel AI.
keywords	Artificial Intelligence, Architectural Design, Floor Plan, Autocompletion, SDG 8, SDG 9
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	sigradi2022_104
id	sigradi2022_104
authors	Bielski, Jessica; Eisenstadt, Viktor; Langenhan, Christoph; Petzold, Frank
year	2022
title	Lost in architectural designing - Possible cognitive biases of architects during the early design phases
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. 557–568
summary	In order to meet the housing demands of the future, architects need to work faster and more efficiently while improving architectural quality. The metis projects aim to create an intelligent design assistant supporting architects during the early design stages through suggesting further design steps for spatial layouting, based on the best practice of reference buildings. By enhancing suggestions with explainability, the system offers insight to improve Human-System-Interaction (HSI), bridging the ‘black box’ problem. The explanations aim to either support the reasoning process or mitigate possible biases of architects, which can be rooted in the heuristic ‘System 1’, as well as the analytical ‘System 2’, drawing from the ‘dual process model’. Within this paper, we propose our approach to clarify the four main heuristic biases and the logical errors of architects, when using reference buildings, and their respective representation during the architectural design decision-making process.
keywords	Decision Making, Biases, Explainability, XAI, Human System Interaction
series	SIGraDi
email
last changed	2023/05/16 16:56

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