Machining and machine learning

Extending architectural digital fabrication through AI

Authored by: Paul Nicholas

The Routledge Companion to Artificial Intelligence in Architecture

Print publication date:  May  2021
Online publication date:  May  2021

Print ISBN: 9780367424589
eBook ISBN: 9780367824259
Adobe ISBN:

10.4324/9780367824259-25

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Abstract

This chapter details how machine learning can help in the translation between an architectural element’s description and its making. With a specific focus on digital fabrication and deep learning, the chapter identifies new integrative workflows and datasets enabled by machine learning that can enable architects to rethink critical parameters of design, materiality, and fabrication. Three particular trajectories are identified—new opportunities for making fabrication information, new opportunities for material complexity, and new opportunities for interaction between humans and machines. Two case studies then exemplify the use of machine learning within the digital chain to (A) introduce flexibility and simplicity to the making of fabrication information and (B) capture complex interdependencies between material and fabrication parameters.

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