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Structural Design Recommendations in the Early Design Phase Using Machine Learning

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Computer-Aided Architectural Design. Design Imperatives: The Future is Now (CAAD Futures 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1465))

Abstract

Structural engineering knowledge can be of significant importance to the architectural design team during the early design phase. However, architects and engineers do not typically work together during the conceptual phase; in fact, structural engineers are often called late into the process. As a result, updates in the design are more difficult and time-consuming to complete. At the same time, there is a lost opportunity for better design exploration guided by structural feedback. In general, the earlier in the design process the iteration happens, the greater the benefits in cost efficiency and informed design exploration, which can lead to higher quality creative results.

In order to facilitate an informed exploration in the early design stage, we suggest the automation of fundamental structural engineering tasks and introduce ApproxiFramer, a Machine Learning-based system for the automatic generation of structural layouts from building plan sketches in real-time. The system aims to assist architects by presenting them with feasible structural solutions during the conceptual phase so that they proceed with their design with adequate knowledge of its structural implications.

In this paper, we describe the system and evaluate the performance of a proof-of-concept implementation in the domain of orthogonal, metal, rigid structures. We trained a Convolutional Neural Net to iteratively generate structural design solutions for sketch-level building plans using a synthetic dataset and achieved an average error of 2.2% in the predicted positions of the columns.

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Notes

  1. 1.

    The last iteration will add any number of columns between 0 and that fixed number, as needed.

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Acknowledgements

We would like to express our gratitude to Mohammad Keshavarzi for his help with the synthetic data preparation process.

Author information

Authors and Affiliations

  1. Harvard Graduate School of Design, Cambridge, MA, 02138, USA

    Spyridon Ampanavos

  2. Autodesk Research, San Francisco, CA, 94105, USA

    Mehdi Nourbakhsh & Chin-Yi Cheng

Authors
  1. Spyridon Ampanavos
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  2. Mehdi Nourbakhsh
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  3. Chin-Yi Cheng
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Corresponding author

Correspondence to Spyridon Ampanavos .

Editor information

Editors and Affiliations

  1. University of Southern California, Los Angeles, CA, USA

    David Gerber

  2. University of Southern California, Los Angeles, CA, USA

    Evangelos Pantazis

  3. Florida International University, Miami, FL, USA

    Biayna Bogosian

  4. University of Calgary, Calgary, Canada

    Alicia Nahmad

  5. Aalto University, Espoo, Finland

    Constantinos Miltiadis

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Ampanavos, S., Nourbakhsh, M., Cheng, CY. (2022). Structural Design Recommendations in the Early Design Phase Using Machine Learning. In: Gerber, D., Pantazis, E., Bogosian, B., Nahmad, A., Miltiadis, C. (eds) Computer-Aided Architectural Design. Design Imperatives: The Future is Now. CAAD Futures 2021. Communications in Computer and Information Science, vol 1465. Springer, Singapore. https://doi.org/10.1007/978-981-19-1280-1_12

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  • DOI: https://doi.org/10.1007/978-981-19-1280-1_12

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  • Online ISBN: 978-981-19-1280-1

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