Machine learning in architecture

This paper explores the utilisation of machine learning in architecture, focusing on the addressed problems and commonly employed programming languages, software, platforms, libraries, and algorithms. Eight major academic search and publishing platforms were systematically reviewed, covering the period from 2007 to 2022, resulting in the selection of 60 relevant articles from a pool of 175. The articles were categorised based on their thematic focus, primarily centring around Computer-Aided Design (CAD), Computer-Aided Engineering (CAE), and Computer-Aided Manufacturing (CAM). By evaluating the current state of machine learning in architecture, this study provides valuable insights into its usage and identifies potential areas for future research. This paper contributes to a comprehensive understanding of the evolving landscape of machine learning in the field by investigating subfields within architecture and the specific tools used to tackle architectural challenges.