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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Hits 1 to 20 of 676

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

_id	ijac202220402
id	ijac202220402
authors	Orozco, Luis; Anna Krtschil; Lior Skoury; Jan Knippers; Achim Menges
year	2022
title	Arrangement of reinforcement in variable density timber slab systems for multi-story construction
source	International Journal of Architectural Computing 2022, Vol. 20 - no. 4, pp. 707–727
summary	The arrangement of columns and their spacing in multi-story timber construction is restricted to rectangular grids by the production and shipping sizes of floor assemblies. This is particularly true for hollow box floor systems, for which the punctual supports must be placed at the reinforced edges of the hollow boxes. The arrangement of the columns and their spacing is thereby restricted by the production and shipping sizes of the box ceilings to rectangular grids. To overcome these design limits a new wooden box building system is developed that allows for irregular column layouts through a tailored slab interior design. This development allows for the increased applicability of timber floor systems regardless of site shape or architectural design intent. The slab interior design is dependent on occurring forces and fabrication requirements. Three methods for the internal slab layout are developed and compared: a sequential method, a structurally informed agent-based method, and a geometrically informed agent-based method that uses both a sequential and agent-based approach. The structural performance of each method is compared through the analysis of three reinforcement layouts an architectural testing setup.
keywords	Agent-based modeling, integrative design, structural analysis, computational design, timber building system
series	journal
last changed	2024/04/17 14:30

_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
doi	https://doi.org/10.52842/conf.caadria.2022.2.335
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
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_102
id	ecaade2022_102
authors	Casalnuovo, Gianluca and Erioli, Alessio
year	2022
title	Deep Trails - Coupling of structural optimization and self-organization processes for the computational design of composite surface tectonics
doi	https://doi.org/10.52842/conf.ecaade.2022.2.085
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. 85–94
summary	This research explores the constructive and expressive capabilities of stigmergic-based creasing patterns integrating structural and ornamental conditions in fibre-composite surface tectonics, generated by the reciprocal influence of multi-agent systems and Non- Linear Time History (NLHT) dynamic structural simulation. Building upon precedents on the use of agent bodies and behavioural tectonics such as the work of Roland Snooks, our approach employs NLTH simulation for the dynamical assessment of the structural failure modes to provide information for agents behaviour and a comparative assessment of the bodies pattern contribution. Considering the obtained results, insights gained on the structural behaviour of multi-agent composite surface tectonics while attempting to explore its embedded architectural, morphological and expressive qualities are discussed.
keywords	Computational Design, Multi-Agent System, Ornament, Structural Optimization, Fibre-Composite Materials, Stigmergy, Non-Linear Time History
series	eCAADe
email
last changed	2024/04/22 07:10

_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
doi	https://doi.org/10.52842/conf.ecaade.2022.2.631
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
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_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
doi	https://doi.org/10.52842/conf.ecaade.2022.1.501
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
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	ascaad2022_105
id	ascaad2022_105
authors	Morsi, Nihal; Kamel, Shaimaa; Sabry, Hanan; Assem, Ayman
year	2022
title	Computational Design for Architectural Space Planning of Commercial Exhibitions: A Framework for Visitors Interaction using Parametric Design and Agent-based Modeling
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. 361-376
summary	Using computational tools for evaluating spatial layouts of commercial exhibitions provides an opportunity for assessment of performance before execution. However, most evaluation techniques take into consideration only the physical qualities of the built environment, excluding important factors such as crowds. Crowds are essentially dynamic obstacles that hinder visibility and can induce flight response, but they are also a sign of good exposure when in reasonable amounts. This is mostly due to the challenge of quantifying spatial qualities such as users’ interaction and movement for computational representations. This paper proposes a framework using agent-based modeling for simulating user interaction in commercial exhibition spaces combined with a parametric representation of the built environment. The framework is then evaluated by applying it to a case-study of three layout scenarios in a generic exhibition hall. The simulation results show that layouts with vertical aisles, and less horizontal aisles have better footfall distribution.
series	ASCAAD
email
last changed	2024/02/16 13:38

_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	ecaade2022_113
id	ecaade2022_113
authors	van Son, Nicholas A. and Prado, Marshall
year	2022
title	Computational Schematic Design Utilizing Self-Organizing Programmatic Agents - A novel approach to visualizing and organizing urban and architectural data
doi	https://doi.org/10.52842/conf.ecaade.2022.2.095
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. 95–104
summary	Architectural design requires the negotiation of a wide variety of often conflicting constraints and conditions. This puts a tremendous burden on designers to understand and evaluate all the design and site parameters in the conceptual phase of the project. Design methodologies that utilize conventional means of representation such as site diagrams, maps, or other orthographic projections may not be adequate to produce truly integrative design solutions. They often simplify conditions for user clarity or eliminate volumetric and temporal data entirely. As computational design tools develop and the mapping of georeferenced urban data becomes more commonplace, it becomes possible to integrate spatial information into design strategies and evaluate various relationships more effectively. Taking clues from medical imaging, voxel data is used to represent volumetric gradients in material properties and densities of spatial conditions. This method can be used to generate morphogenic spatial analysis of an existing site. The research presented here explores how self-organizing programmatic agents can use this analysis and embedded behaviors to visualize performative schematic design scenarios. These agents, which represent a variety of functional spaces, programmatic requirements, design constraints, and value sets, can negotiate the myriad of environmental and socio- economic site conditions as well as interact with other adaptive programmatic spaces. Each agent can iteratively search for the space that best suits the desired conditions of its program. Various agents compete for space so the overall performance of the spatial arrangement is maximized. This self-organizing spatial system presents a novel and viable means for designers to more effectively implement both urban data and computational design methods into architectural design scenarios.
keywords	Agent-based Modeling, Voxels, Generative Design, Self-Organizing, Urban Data Mapping, Optimization, Spatial Analysis
series	eCAADe
email
last changed	2024/04/22 07:10

_id	cdrf2022_314
id	cdrf2022_314
authors	Yuqian Li, Weiguo Xu, and Xingchen Liu
year	2022
title	Research on Architectural Generation Design of Specific Architect's Sketch Based on Image-To-Image Translation
doi	https://doi.org/https://doi.org/10.1007/978-981-19-8637-6_28
source	Proceedings of the 2022 DigitalFUTURES The 4st International Conference on Computational Design and Robotic Fabrication (CDRF 2022)
summary	Sketch is a way for architects to communicate with others. Architects record their own ideas through rapid drawing. However, sketches are abstract, vague, and even ambiguous. To this end, architects need to spend a lot of time, through modeling and other means, to present the architectural plan that can be understood by people. However, this method is time-consuming and laborious. Due to the development of deep learning technology, especially convolutional neural networks (CNN) and generative adversarial networks (GAN), they have shown great advantages in the field of image recognition and generation. With the help of these technologies, ambiguous architectural sketches can be directly transformed into architectural scheme drawings, and architects’ creative intentions can be continuously improved and developed, It will be very convenient and efficient. Therefore, based on the image-to-image translation, this paper realizes the mapping from architectural sketches to architectural scheme drawings with the help of CycleGAN. Through the analysis of the architectural generation design results of Frank Gehry's and Alberto Campo Baeza's architectural sketches, firstly, the feasibility of this method is verified. Secondly, it is found that this method can well complete the identification of sketch boundaries. In the generated scheme drawings, it can not only reflect the volume and lighting changes of the building, but also reflect the architect's creative intention and style to a large extent, The side reflects the cognitive ability of this method to architectural design.
series	cdrf
email
last changed	2024/05/29 14:02

_id	ecaade2022_153
id	ecaade2022_153
authors	Zhong, Ximing, Fricker, Pia, Yu, Fujia, Tan, Chuheng and Pan, Yuzhe
year	2022
title	A Discussion on an Urban Layout Workflow Utilizing Generative Adversarial Network (GAN) - With a focus on automatized labeling and dataset acquisition
doi	https://doi.org/10.52842/conf.ecaade.2022.2.583
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. 583–592
summary	Deep Learning (DL) has recently gained widespread attention in the automation of urban layout processes. This study proposes a rule-based and Generative Adversarial Network (GAN) workflow to automatically select and label urban datasets to train customized GAN models for the generation of urban layout proposals. The developed workflow automatically collects and labels urban typology samples from open-source maps. Furthermore, it controls the results of the GAN process with labels and provides real-time urban layout suggestions based on a co-design process. The conducted case study shows that the average value of the GAN results, trained from an automatically generated dataset, meets the site's requirements. The developed co-design strategy allows the architect to control the GAN process and perform iterations on urban layouts. The research addresses the research gap in GAN applications in the field of urban design and planning. Many studies have demonstrated that training the (GAN) model by labeling enables machines to learn urban morphological features and urban layout logic. However, two research gaps remain: (1) The manual filtering of GAN urban sample datasets to fit site-specific design requirements is very time-consuming. (2) Without a suitable data labeling method, it is difficult to manage the GAN process in such a manner to facilitate the meeting of overriding design requirements.
keywords	Deep Learning, Generative Adversarial Network (GAN), Urban Layout Process, Automatic Dataset Construction, Co-design
series	eCAADe
email
last changed	2024/04/22 07:10

_id	architectural_intelligence2022_6
id	architectural_intelligence2022_6
authors	Achim Menges, Fabian Kannenberg & Christoph Zechmeister
year	2022
title	Computational co-design of fibrous architecture
doi	https://doi.org/https://doi.org/10.1007/s44223-022-00004-x
source	Architectural Intelligence Journal
summary	Fibrous architecture constitutes an alternative approach to conventional building systems and established construction methods. It shows the potential to converge architectural concerns such as spatial expression and structural elegance, with urgently required resource effectiveness and material efficiency, in a genuinely computational approach. Fundamental characteristics of fibre composite are shared with fibre structures in the natural world, enabling the transfer of design principles and providing a vast repertoire of inspiration. Robotic fabrication based on coreless filament winding, a technique to deposit resin impregnated fibre filaments with only minimal formwork, as well as integrative computational design methods are imperative to the development of complex fibrous building systems. Two projects, the BUGA Fibre Pavilion as an example for long-span structures, and Maison Fibre as an example of multi-storey architecture, showcase the application of those techniques in an architectural context and highlight areas of further research opportunities. The highly interrelated aesthetic, structural and fabrication characteristics of fibre nets are difficult to understand and go beyond a designer’s comprehension and intuition. An AI powered, self-learning agent system aims to extend and thoroughly explore the design space of fibre structures to unlock the full design potential coreless filament winding offers. In order to ensure feedback between all relevant design and performance criteria and enable interdisciplinary convergence, these novel design methods are embedded in a larger co-design framework. It formalizes the interaction of involved interdisciplinary domains and allows for interactive collaboration based on a central data model, serving as a base for design optimisation and exploration. To further advance research on fibre composites in architecture, bio-based materials are considered, continuing the journey of discovery of fibrous architecture to fundamentally rethinking design and construction towards a novel, computational material culture in architecture.
series	Architectural Intelligence
email
last changed	2025/01/09 15:00

_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
doi	https://doi.org/10.52842/conf.ecaade.2022.2.535
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
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	cdrf2022_304
id	cdrf2022_304
authors	Anni Dai
year	2022
title	Co-creation: Space Reconfiguration by Architect and Agent Simulation Based Machine Learning
doi	https://doi.org/https://doi.org/10.1007/978-981-19-8637-6_27
source	Proceedings of the 2022 DigitalFUTURES The 4st International Conference on Computational Design and Robotic Fabrication (CDRF 2022)
summary	This research is a manifestation of architectural co-creation between agent simulation based machine learning and an architect’s tacit knowledge. Instead of applying machine learning brains to agents, the author reversed the idea and applied machine learning to buildings. The project used agent simulation as a database, and trained the space to reconfigure itself based on its distance to the nearest agents. To overcome the limitations of machine learning model’s simplified solutions to complicated architectural environments, the author introduced a co-creation method, where an architect uses tacit knowledge to overwatch and have real-time control over the space reconfiguration process. This research combines both the strength of machine learning’s data-processing ability and an architect’s tacit knowledge. Through exploration of emerging technologies such as machine learning and agent simulation, the author highlights limitations in design automation. By combining an architect’s tacit knowledge with a new generation design method of agent simulation based machine learning, the author hopes to explore a new way for architects to co-create with machines.
series	cdrf
email
last changed	2024/05/29 14:02

_id	ecaade2022_218
id	ecaade2022_218
authors	Bank, Mathias, Sandor, Viktoria, Schinegger, Kristina and Rutzinger, Stefan
year	2022
title	Learning Spatiality - A GAN method for designing architectural models through labelled sections
doi	https://doi.org/10.52842/conf.ecaade.2022.2.611
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. 611–619
summary	Digital design processes are increasingly being explored through the use of 2D generative adversarial networks (GAN), due to their capability for assembling latent spaces from existing data. These infinite spaces of synthetic data have the potential to enhance architectural design processes by mapping adjacencies across multidimensional properties, giving new impulses for design. The paper outlines a teaching method that applies 2D GANs to explore spatial characteristics with architectural students based on a training data set of 3D models of material-labelled houses. To introduce a common interface between human and neural networks, the method uses vertical slices through the models as the primary medium for communication. The approach is tested in the framework of a design course.
keywords	AI, Architectural Design, Materiality, GAN, 3D, Form Finding
series	eCAADe
email
last changed	2024/04/22 07:10

_id	ijac202220101
id	ijac202220101
authors	Bao, Ding Wen; Xin Yan, Yi Min Xie
year	2022
title	Encoding topological optimisation logical structure rules into multi-agent system for architectural design and robotic fabrication
source	International Journal of Architectural Computing 2022, Vol. 20 - no. 1, pp. 7–17
summary	Natural phenomena have been explored as a source of architectural and structural design inspiration with different approaches undertaken within architecture and engineering. The research proposes a connection between two dichotomous principles: architectural complexity and structural efficiency through a hybrid of natural phenomena, topology optimisation and generative design. Both Bi-directional Evolutionary Structural Optimisation (BESO) and multi-agent algorithms are emerging technologies developed into new approaches that transform architectural and structural design, respectively, from the logic of topology optimisation and swarm intelligence. This research aims to explore a structural behaviour feedback loop in designing intricate functional forms through encoding BESO logical structure rules into the multi-agent algorithm. This research intends to study and evaluate the application of topology optimisation and multi-agent system in form-finding and later robotic fabrication through a series of prototypes. It reveals a supposition that the structural behaviour-based design method matches the beauty and function of natural appearance and structure. Thus, a new exploration of architectural design and fabrication strategy is introduced, which benefits the collab- oration among architects, engineers and manufacturers. There is the potential to seek the ornamental complexities in architectural forms and the most efficient use of material based on structural performance in the process of generating complex geometry of the building and its various elements.
keywords	Swarm intelligence, multi-agent, bi-directional evolutionary structural optimisation (BESO), intricate architectural form, efficient structure
series	journal
last changed	2024/04/17 14:29

_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
doi	https://doi.org/10.52842/conf.ecaade.2022.2.067
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
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	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
doi	https://doi.org/10.52842/conf.caadria.2022.1.323
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
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	ijac202220106
id	ijac202220106
authors	Förster, Nick; Ivan Bratoev, Jakob Fellner, Gerhard Schubert, Frank Petzold
year	2022
title	Collaborating with the crowd
source	International Journal of Architectural Computing 2022, Vol. 20 - no. 1, pp. 76–95
summary	Microscopic agent-based simulations promise the meaningful inclusion of crowd dynamics in planning processes. However, such complex urban issues depend on a multiplicity of criteria. Thus, an isolated model cannot represent the walk of pedestrians meaningfully in planning contexts. This paper reframes crowd simulation as collaborative experimentation and embeds it directly in the design process. Beyond the simulation algorithm, this perspective draws attention to user interactions, interfaces, and visualizations as crucial simulation elements. Through a prototype, we combine an agent-based pedestrian simulation with a hybrid physical–digital interface. Based on this configuration, we explore requirements of the early design stages and accordingly discuss concepts for interaction, simulation, and visualization. The prototype blends user inputs with intuitive design interactions, adapts the simulation process to qualitative and dynamic negotiations, and presents results immediately in the discussed context. Thus, it aligns crowd simulation with contingent collaborations and reveals its potential in the early design stages.
keywords	Urban design, architectural design, design decision support, pedestrian simulation, human–computer interaction, collaborative design, early design stages
series	journal
last changed	2024/04/17 14:29

_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
doi	https://doi.org/10.52842/conf.ecaade.2022.2.227
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
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
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last changed	2024/04/22 07:10

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