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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	acadia20_688
id	acadia20_688
authors	del Campo, Matias; Carlson, Alexandra; Manninger, Sandra
year	2020
title	3D Graph Convolutional Neural Networks in Architecture Design
doi	https://doi.org/10.52842/conf.acadia.2020.1.688
source	ACADIA 2020: Distributed Proximities / Volume I: Technical Papers [Proceedings of the 40th Annual Conference of the Association of Computer Aided Design in Architecture (ACADIA) ISBN 978-0-578-95213-0]. Online and Global. 24-30 October 2020. edited by B. Slocum, V. Ago, S. Doyle, A. Marcus, M. Yablonina, and M. del Campo. 688-696.
summary	The nature of the architectural design process can be described along the lines of the following representational devices: the plan and the model. Plans can be considered one of the oldest methods to represent spatial and aesthetic information in an abstract, 2D space. However, to be used in the design process of 3D architectural solutions, these representations are inherently limited by the loss of rich information that occurs when compressing the three-dimensional world into a two-dimensional representation. During the first Digital Turn (Carpo 2013), the sheer amount and availability of models increased dramatically, as it became viable to create vast amounts of model variations to explore project alternatives among a much larger range of different physical and creative dimensions. 3D models show how the design object appears in real life, and can include a wider array of object information that is more easily understandable by nonexperts, as exemplified in techniques such as building information modeling and parametric modeling. Therefore, the ground condition of this paper considers that the inherent nature of architectural design and sensibility lies in the negotiation of 3D space coupled with the organization of voids and spatial components resulting in spatial sequences based on programmatic relationships, resulting in an assemblage (DeLanda 2016). These conditions constitute objects representing a material culture (the built environment) embedded in a symbolic and aesthetic culture (DeLanda 2016) that is created by the designer and captures their sensibilities.
series	ACADIA
type	paper
email
last changed	2023/10/22 12:06

_id	ecaade2016_163
id	ecaade2016_163
authors	Harding, John
year	2016
title	Evolving Parametric Models using Genetic Programming with Artificial Selection
doi	https://doi.org/10.52842/conf.ecaade.2016.1.423
source	Herneoja, Aulikki; Toni Österlund and Piia Markkanen (eds.), Complexity & Simplicity - Proceedings of the 34th eCAADe Conference - Volume 1, University of Oulu, Oulu, Finland, 22-26 August 2016, pp. 423-432
summary	Evolutionary methods with artificial selection have been shown to be an effective human-computer technique for exploring design spaces with unknown goals. This paper investigates an interactive evolution of visual programs currently used in popular parametric modelling software. Although parametric models provide a useful cognitive artifact for designers to interact with, they are often bound by their topological structure with the designer left to adjusting (or optimising) metric variables as part of a design search. By allowing the topological structure of the graph to be evolved as well as the parameters, artificial selection can be employed to explore a wider design space more suited to the early design stage.
wos	WOS:000402063700047
keywords	genetic programming; parametric design; artificial selection; evolutionary design; design exploration
series	eCAADe
email
last changed	2022/06/07 07:49

_id	ecaade2016_119
id	ecaade2016_119
authors	Koenig, Reinhard and Varoudis, Tasos
year	2016
title	Spatial Optimisations - Merging depthmapX, spatial graph networks and evolutionary design in Grasshopper
doi	https://doi.org/10.52842/conf.ecaade.2016.2.249
source	Herneoja, Aulikki; Toni Österlund and Piia Markkanen (eds.), Complexity & Simplicity - Proceedings of the 34th eCAADe Conference - Volume 2, University of Oulu, Oulu, Finland, 22-26 August 2016, pp. 249-254
summary	In the Space Syntax community, the standard tool for computing all kinds of spatial graph network measures is depthmapX (Varoudis, 2012). The process of evaluating many design variants of networks is relatively complicated, since they need to be drawn in a separated CAD system, exported and imported in depthmapX via dxf file format. This procedure disables a continuous integration into a design process. Furthermore, the standalone character of depthmapX makes it impossible to use its network centrality calculation for optimization processes. To overcome this limitations, we present in this paper the first steps of experimenting with a Grasshopper component (Varoudis, 2016) that can access the functions of depthmapX and integrate them into Grasshopper/Rhino3D. Here the component is implemented in a way that it can be used directly for an evolutionary algorithm (EA) implemented in a Python scripting component in Grasshopper.
wos	WOS:000402064400024
keywords	Space Syntax; Evolutionary Algorithm; Grasshopper; Python; DepthmapX; Optimization
series	eCAADe
email
last changed	2022/06/07 07:51

_id	acadia16_140
id	acadia16_140
authors	Nejur, Andrei; Steinfeld, Kyle
year	2016
title	Ivy: Bringing a Weighted-Mesh Representations to Bear on Generative Architectural Design Applications
doi	https://doi.org/10.52842/conf.acadia.2016.140
source	ACADIA // 2016: POSTHUMAN FRONTIERS: Data, Designers, and Cognitive Machines [Proceedings of the 36th Annual Conference of the Association for Computer Aided Design in Architecture (ACADIA) ISBN 978-0-692-77095-5] Ann Arbor 27-29 October, 2016, pp. 140-151
summary	Mesh segmentation has become an important and well-researched topic in computational geometry in recent years (Agathos et al. 2008). As a result, a number of new approaches have been developed that have led to innovations in a diverse set of problems in computer graphics (CG) (Sharmir 2008). Specifically, a range of effective methods for the division of a mesh have recently been proposed, including by K-means (Shlafman et al. 2002), graph cuts (Golovinskiy and Funkhouser 2008; Katz and Tal 2003), hierarchical clustering (Garland et al. 2001; Gelfand and Guibas 2004; Golovinskiy and Funkhouser 2008), primitive fitting (Athene et al. 2004), random walks (Lai et al.), core extraction (Katz et al.) tubular multi-scale analysis (Mortara et al. 2004), spectral clustering (Liu and Zhang 2004), and critical point analysis (Lin et al. 20070, all of which depend upon a weighted graph representation, typically the dual of a given mesh (Sharmir 2008). While these approaches have been proven effective within the narrowly defined domains of application for which they have been developed (Chen 2009), they have not been brought to bear on wider classes of problems in fields outside of CG, specifically on problems relevant to generative architectural design. Given the widespread use of meshes and the utility of segmentation in GAD, by surveying the relevant and recently matured approaches to mesh segmentation in CG that share a common representation of the mesh dual, this paper identifies and takes steps to address a heretofore unrealized transfer of technology that would resolve a missed opportunity for both subject areas. Meshes are often employed by architectural designers for purposes that are distinct from and present a unique set of requirements in relation to similar applications that have enjoyed more focused study in computer science. This paper presents a survey of similar applications, including thin-sheet fabrication (Mitani and Suzuki 2004), rendering optimization (Garland et al. 2001), 3D mesh compression (Taubin et al. 1998), morphin (Shapira et al. 2008) and mesh simplification (Kalvin and Taylor 1996), and distinguish the requirements of these applications from those presented by GAD, including non-refinement in advance of the constraining of mesh geometry to planar-quad faces, and the ability to address a diversity of mesh features that may or may not be preserved. Following this survey of existing approaches and unmet needs, the authors assert that if a generalized framework for working with graph representations of meshes is developed, allowing for the interactive adjustment of edge weights, then the recent developments in mesh segmentation may be better brought to bear on GAD problems. This paper presents work toward the development of just such a framework, implemented as a plug-in for the visual programming environment Grasshopper.
keywords	tool-building, design simulation, fabrication, computation, megalith
series	ACADIA
type	paper
email
last changed	2022/06/07 07:58

_id	acadia17_446
id	acadia17_446
authors	Nejur, Andrei; Steinfeld, Kyle
year	2017
title	Ivy: Progress in Developing Practical Applications for a Weighted-Mesh Representation for Use in Generative Architectural Design
doi	https://doi.org/10.52842/conf.acadia.2017.446
source	ACADIA 2017: DISCIPLINES & DISRUPTION [Proceedings of the 37th Annual Conference of the Association for Computer Aided Design in Architecture (ACADIA) ISBN 978-0-692-96506-1] Cambridge, MA 2-4 November, 2017), pp. 446- 455
summary	This paper presents progress in the development of practical applications for graph representations of meshes for a variety of problems relevant to generative architectural design (GAD). In previous work (Nejur and Steinfeld 2016), the authors demonstrated that while approaches to marrying mesh and graph representations drawn from computer graphics (CG) can be effective within the domains of applications for which they have been developed, they have not adequately addressed wider classes of problems in GAD. There, the authors asserted that a generalized framework for working with graph representations of meshes can effectively bring recent advances in mesh segmentation to bear on GAD problems, a utility demonstrated through the development of a plug-in for the visual programming environment Grasshopper. Here, we describe a number of implemented solutions to mesh segmentation and transformation problems, articulated as a series of additional features developed as a part of this same software. Included are problems of mesh segmentation approached through the creation of acyclic connected graphs (trees); problems of mesh transformations, such as those that unfold a segmented mesh in anticipation of fabrication; and problems of geometry generation in relation to a segmented mesh, as demonstrated through a generalized approach to mesh weaving. We present these features in the context of their potential applications in GAD and provide a limited set of examples for their use.
keywords	design methods; information processing
series	ACADIA
email
last changed	2022/06/07 07:58

_id	ecaade2016_019
id	ecaade2016_019
authors	Thurow, Torsten, Langenhan, Christoph and Petzold, Frank
year	2016
title	Assisting Early Architectural Planning Using a Geometry-Based Graph Search
doi	https://doi.org/10.52842/conf.ecaade.2016.2.199
source	Herneoja, Aulikki; Toni Österlund and Piia Markkanen (eds.), Complexity & Simplicity - Proceedings of the 34th eCAADe Conference - Volume 2, University of Oulu, Oulu, Finland, 22-26 August 2016, pp. 199-207
summary	In early design phases of architecture ideas exist mostly on a vague level concerning the expectations for the building plan and the respective design parameters. One established method is to examine and develop ideas through existing designs, and to use these to clarify design parameters and be further inspired. Thus, the aim is a computer-based system like sketch-based query approach to show similar floor plans using semantic building fingerprints.During the search floor plans are compared in form of graphs, which means that the sketch-based floor plans are converted to graphs together with the existing floor plans. Herewith, a gradual condensation of the request is possible. The entry is condensed continuously through the repetitive process of entry and search. The challenges with this approach lie in the following mathematical model behind similar floor plans, Queries that satisfy complexity of the data and optimal way for the user to engage in search process.
wos	WOS:000402064400019
keywords	Semantic fingerprints; early architectural planning; geometry-based graph search; adjustment theory
series	eCAADe
email
last changed	2022/06/07 07:58

_id	ascaad2016_012
id	ascaad2016_012
authors	Veloso, Pedro; Ramesh Krishnamurti
year	2016
title	On Slime Molds and Corridors - The application of network design algorithms to connect architectural arrangements
source	Parametricism Vs. Materialism: Evolution of Digital Technologies for Development [8th ASCAAD Conference Proceedings ISBN 978-0-9955691-0-2] London (United Kingdom) 7-8 November 2016, pp. 95-104
summary	The use of adjacency graphs to represent and generate architectural arrangements tends to favor direct connections between contiguous rooms. These disregard specialized circulatory systems (such as corridors), which consider connections between non-contiguous spatial units or accesses. This paper addresses two specific issues: (1) how to represent a circulation network for a specific access/adjacency graph embedding; and (2) how to design good circulatory solutions for the arrangement that optimizes this network. To represent a complete circulation network, we propose a scheme, an adapted straight skeleton, based on the boundaries of the spatial units. To design possible circulation alternatives, we adopt the Slime Mold model (Tero et al., 2006; 2007). Using this model, we develop an original method, termed Adjacency Graph Selection (AGS), to generate circulation solutions for arrangements. As an initial test case for AGS, we use floor plan of the Louvre Abu Dhabi, designed by the French architect Jean Nouvel.
series	ASCAAD
email
last changed	2017/05/25 13:31

_id	ecaade2016_018
id	ecaade2016_018
authors	Wurzer, Gabriel and Lorenz, Wolfgang E.
year	2016
title	SpaceBook - A Case Study of Social Network Analysis in Adjacency Graphs
doi	https://doi.org/10.52842/conf.ecaade.2016.2.229
source	Herneoja, Aulikki; Toni Österlund and Piia Markkanen (eds.), Complexity & Simplicity - Proceedings of the 34th eCAADe Conference - Volume 2, University of Oulu, Oulu, Finland, 22-26 August 2016, pp. 229-238
summary	In this paper, we have adopted methods from Social Network Analysis in order to analyze adjacency graphs. Our intent was to uncover as much hidden structures as possible so as to improve adjacency requirements before they are used further on during the design process. To that end, we have conducted a case study using two readily available software packages (Gephi, Pajek), concluding that these could benefit from being more transparent about the underlying algorithms and more geared towards the problem domain 'adjacency analysis' when it comes to data entry and visualization. As a matter of fact, we produced an open-source prototype called SpaceBook, which customizes computation and visualization in the aforementioned spirit.
wos	WOS:000402064400022
keywords	Adjacency Graph; Social Network Analysis
series	eCAADe
email
last changed	2022/06/07 07:57

_id	acadia20_238
id	acadia20_238
authors	Zhang, Hang
year	2020
title	Text-to-Form
doi	https://doi.org/10.52842/conf.acadia.2020.1.238
source	ACADIA 2020: Distributed Proximities / Volume I: Technical Papers [Proceedings of the 40th Annual Conference of the Association of Computer Aided Design in Architecture (ACADIA) ISBN 978-0-578-95213-0]. Online and Global. 24-30 October 2020. edited by B. Slocum, V. Ago, S. Doyle, A. Marcus, M. Yablonina, and M. del Campo. 238-247.
summary	Traditionally, architects express their thoughts on the design of 3D architectural forms via perspective renderings and standardized 2D drawings. However, as architectural design is always multidimensional and intricate, it is difficult to make others understand the design intention, concrete form, and even spatial layout through simple language descriptions. Benefiting from the fast development of machine learning, especially natural language processing and convolutional neural networks, this paper proposes a Linguistics-based Architectural Form Generative Model (LAFGM) that could be trained to make 3D architectural form predictions based simply on language input. Several related works exist that focus on learning text-to-image generation, while others have taken a further step by generating simple shapes from the descriptions. However, the text parsing and output of these works still remain either at the 2D stage or confined to a single geometry. On the basis of these works, this paper used both Stanford Scene Graph Parser (Sebastian et al. 2015) and graph convolutional networks (Kipf and Welling 2016) to compile the analytic semantic structure for the input texts, then generated the 3D architectural form expressed by the language descriptions, which is also aided by several optimization algorithms. To a certain extent, the training results approached the 3D form intended in the textual description, not only indicating the tremendous potential of LAFGM from linguistic input to 3D architectural form, but also innovating design expression and communication regarding 3D spatial information.
series	ACADIA
type	paper
email
last changed	2023/10/22 12:06

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