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

Eisenstadt, Viktor; Langenhan, Christoph; Althof, Klaus-Dieter

doi:10.5151/proceedings-ecaadesigradi2019_648

Cited by 13 publications

(7 citation statements)

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“…17 Generative Adversarial CNNs 18 were used to generate new, Islamic, geometric patterns based on provided examples 19 and a CNN was trained to distinguish architectural plans from sections. 20 The more specific, atomistic approaches include pixel-level classification 21 of architectural elements represented in architectural drawings, 22 generation of floor plan connectivity diagrams for the early phases of conceptual design, 23 and extraction and classification of the tectonic space types 24 based on the isovist representations. 25…”

Section: State Of the Artmentioning

confidence: 99%

Machine learning and complex compositional principles in architecture: Application of convolutional neural networks for generation of context-dependent spatial compositions

Dzieduszyński¹

2022

International Journal of Architectural Computing

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A substantial part of architectural and urban design involves processing of compositional interdependencies and contexts. This article attempts to isolate the problem of spatial composition from the broader category of synthetic image processing. The capacity of deep convolutional neural networks for recognition and utilization of complex compositional principles has been demonstrated and evaluated under three scenarios varying in scope and approach. The proposed method reaches 95.1%–98.5% efficiency in the generation of context-fitting spatial composition. The technique can be applied for the extraction of compositional principles from the architectural, urban, or artistic contexts and may facilitate the design-related decision making by complementing the required expert analysis.

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Section: State Of the Artmentioning

confidence: 99%

Machine learning and complex compositional principles in architecture: Application of convolutional neural networks for generation of context-dependent spatial compositions

Dzieduszyński¹

2022

International Journal of Architectural Computing

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“…Computer processors can process humongous data integrated and comprehensively with the appropriate algorithms. Moreover, they can also provide design solutions, as shown by several AI technologies [5,10]. It initiates opportunities for more integrated architectural applications, and in effect, leads to the emergence of computers as digital architects.…”

Section: Architecture Design Processmentioning

confidence: 99%

“…Many argue that the advantage of computers is only in their ability to process information faster. However, a study showed that generating floor plans with AI is possible [10]. Another study showed developing 3D building designs with the generative adversarial network was also attainable [5].…”

Section: Introductionmentioning

confidence: 99%

From Science Fiction to Science Facts

Satwiko¹,

Michelle²

2022

Advances in Social Science, Education and Humanities Research

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The current development of artificial intelligence (AI) may encourage the digital architect emergence. It is an artificial-intelligence-powered software that approaches the architectural design process from upstream to downstream in an integrated and comprehensive manner and works autonomously. This paper reviews research in the field of AI to discuss the development of the concept of the digital architect. The result explained the presence of digital architect software as a necessity to bring more efficient and holistic architectural design. Furthermore, we concluded that the final development of the digital architect might be only a matter of time, considering the advancements of intelligent robot technology.

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“…An increasing number of designers and computer scientists are working together to develop new ways of creating automated methods to generate spatial solutions (among others, Eisenstadt et al, 2019;Goodman, 2019;Kalervo, 2019;Liu, 2017;Nauata, 2020;Phelan et al, 2017;Sandelin, 2019;Zeng et al, 2019).…”

Section: Self-organizing Floor Plan At Workmentioning

confidence: 99%

Self-Organizing Floor Plans

Carta

2021

Harvard Data Science Review

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This article introduces and comments on some of the techniques currently used by designers to generate automatic building floor plans and spatial configurations in general, with emphasis on machine learning and neural networks models. This is a relatively new tendency in computational design that reflects a growing interest in advanced generative and optimization models by architects and building engineers.The first part of this work contextualizes self-organizing floor plans in architecture and computational design, highlighting their importance and potential for designers as well as software developers. The central part discusses some of the most common techniques with concrete examples, including Neuro Evolution of Augmenting Topologies (NEAT) and Generative Adversarial Networks (GAN). The final section of the article provides some general comments considering pitfalls and possible future developments, as well as speculating on the future of this trend.

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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

Cited by 13 publications

References 0 publications

Machine learning and complex compositional principles in architecture: Application of convolutional neural networks for generation of context-dependent spatial compositions

Machine learning and complex compositional principles in architecture: Application of convolutional neural networks for generation of context-dependent spatial compositions

From Science Fiction to Science Facts

Self-Organizing Floor Plans

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