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A Framework for the Application of Machine Learning to Generative Architectural Design, and a Report of Activities at Smartgeometry 2018
  • Kyle SteinfeldEmail author
  • Kat Park
  • Adam Menges
  • Samantha Walker
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1028)

Abstract

This paper presents a framework for the application of Machine Learning (ML) to Generative Architectural Design (GAD), and illustrates this framework through a description of a series of projects completed at the Smart Geometry conference in May of 2018 (SG 2018) in Toronto. Proposed here is a modest modification of a 3-step process that is well-known in generative architectural design, and that proceeds as: generate, evaluate, iterate. In place of the typical approaches to the evaluation step, we propose to employ a machine learning process: a neural net trained to perform image classification. This modified process is different enough from traditional methods as to warrant an adjustment of the terms of GAD. Through the development of this framework, we seek to demonstrate that generative evaluation may be seen as a new locus of subjectivity in design.

Keywords

Machine learning Generative design Design methods 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Kyle Steinfeld
    • 1
    Email author
  • Kat Park
    • 2
  • Adam Menges
    • 3
  • Samantha Walker
    • 2
  1. 1.University of California, BerkeleyBerkeleyUSA
  2. 2.Skidmore, Owings & Merrill LLPSan FranciscoUSA
  3. 3.Lobe Artificial Intelligence, Inc.San FranciscoUSA

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