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Interactive Design Support for Architecture Projects During Early Phases Based on Recurrent Neural Networks

  • Johannes BayerEmail author
  • Syed Saqib Bukhari
  • Andreas Dengel
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11351)

Abstract

In the beginning of an architectural project, abstract design decisions have to be made according to the purpose of the later building. Based on these decisions, a rough floor plan layout is drafted (and subsequently redrafted in successively more refined versions). This entire process can be considered an iterative design algorithm, in which high-level ideas and requirements are transformed into a specific building description.

Nowadays, this process is usually carried out in a manual and labor-intensive manner. More precisely, concepts are usually drafted on semi-transparent paper with pencils so that a when a new sheet of paper is put on an existing one, the old concept may serve as a template for the next step in the design iteration.

In this paper, we present a semi-automatic approach to assist the developer by proposing suggestions for solving individual design steps automatically. These suggested designs can be modified between two successive automatic design steps, hence the developer remains in control of the overall design process. In the presented approach, floor plans are represented by graph structures and the developer’s behavior is modeled as a sequence of graph modifications. Based on these sequences we trained a recurrent neural network-based predictor that is used to generate the design suggestions. We assess the performance of our system in order to show its general applicability.

The paper at hand is a extended version of our ICPRAM 2018 conference paper [1], in which we address the different aspects of our proposed algorithm, challenges we faced during our research as well as intended work flow in greater detail.

Keywords

Interactive design support Early phase support Architecture project LSTM Archistant 

Notes

Acknowledgment

This work was partly funded by Deutsche Forschungs-Gemeinschaft.

References

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Johannes Bayer
    • 1
    Email author
  • Syed Saqib Bukhari
    • 1
  • Andreas Dengel
    • 1
  1. 1.German Research Center for Artificial IntelligenceKaiserslauternGermany

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