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 ecaade2020_227
id ecaade2020_227
authors Bielski, Jessica, Langenhan, Christoph, Weyand, Babara, Neuber, Markus, Eisenstadt, Viktor and Althoff, Klaus-Dieter
year 2020
title Topological Queries and Analysis of School Buildings Based on Building Information Modeling (BIM) Using Parametric Design Tools and Visual Programming to Develop New Building Typologies
source Werner, L and Koering, D (eds.), Anthropologic: Architecture and Fabrication in the cognitive age - Proceedings of the 38th eCAADe Conference - Volume 2, TU Berlin, Berlin, Germany, 16-18 September 2020, pp. 279-288
summary School buildings are currently one of the largest portions of planning and building projects in Germany. In order to reflect the continuous developments in school building construction with constantly changing spatial requirements, an approach to analyse, derive and combine patterns of schools is proposed to adapt school typologies accordingly. Therefore, the topology is analysed, concerning interconnection methods, such as adjacency, accessibility, depth, and flow. The geometric analysis of e.g. room sizes or spatial proportions is enhanced by including grouping of rooms, estimated room clusters, or room shapes. Furthermore, text-matching is used to determine e.g. room program fulfilment, or assigning functional room descriptions to predefined room types, revealing huge differences of terms throughout time and architects. First results of the analyses show a relevant correlation between spatial proportion and room types.
keywords school building typologies; building information modeling (BIM); artificial intelligence (AI); topology; spatial analysis; digital semantic model
series eCAADe
email bielski.jessica@gmail.com
last changed 2020/09/09 09:54

_id ecaadesigradi2019_648
id ecaadesigradi2019_648
authors Eisenstadt, Viktor, Langenhan, Christoph and Althoff, Klaus-Dieter
year 2019
title Generation of Floor Plan Variations with Convolutional Neural Networks and Case-based Reasoning - An approach for transformative adaptation of room configurations within a framework for support of early conceptual design phases
source Sousa, JP, Xavier, JP and Castro Henriques, G (eds.), Architecture in the Age of the 4th Industrial Revolution - Proceedings of the 37th eCAADe and 23rd SIGraDi Conference - Volume 2, University of Porto, Porto, Portugal, 11-13 September 2019, pp. 79-84
summary We present an approach for computer-aided generation of different variations of floor plans during the early phases of conceptual design in architecture. The early design phases are mostly characterized by the processes of inspiration gaining and search for contextual help in order to improve the building design at hand. The generation method described in this work uses the novel as well as established artificial intelligence methods, namely, generative adversarial nets and case-based reasoning, for creation of possible evolutions of the current design based on the most similar previous designs. The main goal of this approach is to provide the designer with information on how the current floor plan can evolve over time in order to influence the direction of the design process. The work described in this paper is part of the methodology FLEA (Find, Learn, Explain, Adapt) whose task is to provide a holistic structure for support of the early conceptual phases in architecture. The approach is implemented as the adaptation component of the framework MetisCBR that is based on FLEA.
keywords room configuration; adaptation; case-based reasoning; convolutional neural networks; conceptual design
series eCAADeSIGraDi
email ayzensht@uni-hildesheim.de
last changed 2019/08/26 20:26

_id cf2019_005
id cf2019_005
authors Eisenstadt, Viktor; Klaus-Dieter Althoff and Christoph Langenhan
year 2019
title Supporting Architectural Design Process with FLEA A Distributed AI Methodology for Retrieval, Suggestion, Adaptation, and Explanation of Room Configurations
source Ji-Hyun Lee (Eds.) "Hello, Culture!"  [18th International Conference, CAAD Futures 2019, Proceedings / ISBN 978-89-89453-05-5] Daejeon, Korea, p. 24
summary The artificial intelligence methods, such as case-based reasoning and artificial neural networks were already applied to the task of architectural design support in a multitude of specific approaches and tools. However, modern AI trends, such as Explainable AI (XAI), and additional features, such as providing contextual suggestions for the next step of the design process, were rarely considered an integral part of these approaches or simply not available. In this paper, we present an application of a distributed AI-based methodology FLEA (Find, Learn, Explain, Adapt) to the task of room configuration during the early conceptual phases of architectural design. The implementation of the methodology in the framework MetisCBR applies CBR-based methods for retrieval of similar floor plans to suggest possibly inspirational designs and to explain the returned results with specific explanation patterns. Furthermore, it makes use of a farm of recurrent neural networks to suggest contextually suitable next configuration steps and to present design variations that show how the designs may evolve in the future. The flexibility of FLEA allows for variational use of its components in order to activate the currently required modules only. The methodology was initialized during the basic research project Metis (funded by German Research Foundation) during which the architectural semantic search patterns and a family of corresponding floor plan representations were developed. FLEA uses these patterns and representations as the base for its semantic search, explanation, next step suggestion, and adaptation components. The methodology implementation was iteratively tested during quantitative evaluations and user studies with multiple floor plan datasets.
keywords Room con?guration, Distributed AI, Case-based reasoning, Neural networks, Explainable AI
series CAAD Futures
type normal paper
email ayzensht@uni-hildesheim.de
last changed 2019/07/29 12:11

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