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Quantifying Differences Between Architects’ and Non-architects’ Visual Perception of Originality of Tower Typology Using Deep 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))

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Abstract

The paper presents a computational methodology to quantify the differences in visual perception of originality of the rotating tower typology between architects and non-architects. A parametric definition of the Absolute Tower Building D with twelve variables is used to generate 250 design variants. Subsequently, sixty architects and sixty non-architects were asked to rate the design variants, in comparison to the original design, on a Likert scale of ‘Plagiarised’ to ‘Original’. With the crowd-sourced evaluation data, two neural networks - one each for architects and non-architects - were trained to predict the originality score of 15,000 design variants. The results indicate that architects are more lenient at seeing design variants as original. The average originality score by architects is 27.74% higher than the average originality score by non-architects. Compared to a non-architect, an architect is 1.93 times likelier to see a design variant as original. In 92.01% of the cases, architects’ originality score is higher than non-architects’. The methodology can be used to quantify and predict any subjective opinion.

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Authors and Affiliations

  1. WEsearch lab, New Delhi, 110019, India

    Joy Mondal

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  1. Joy Mondal
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Correspondence to Joy Mondal .

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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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Mondal, J. (2022). Quantifying Differences Between Architects’ and Non-architects’ Visual Perception of Originality of Tower Typology Using Deep 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_13

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  • DOI: https://doi.org/10.1007/978-981-19-1280-1_13

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-1279-5

  • Online ISBN: 978-981-19-1280-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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