A machine-learning model driven by geometry, material and structural performance data in architectural design process

Sevil Yazici*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Citations (Scopus)

Abstract

Artificial Intelligence (AI), based on interpretation of data, influences various professions including architectural design today. Although research on ntegrating conceptual design with Machine Learning (ML) algorithms as a subset of the AI has been investigated previously, there is not a framework towards integration of architectural geometry with material properties and structural performance data towards decision making in the early-design phase. Undertaking performance simulations require significant amount of computation power and time. The aim of this research is to integrate ML algorithms into design process to achieve time efficiency and improve design results. The proposed workflow consists of three stages, including generation of the parametric model; running structural performance simulations to collect the data, and operating the ML algorithms, including Artificial Neural Network (ANN), Non-Linear Regression (NLR) and Gaussian Mixture (GM) for undertaking different tasks. The results underlined that the system generates relatively fast solutions with accuracy. Additionally, ML algorithms can assist generative design processes.

Original languageEnglish
Title of host publicationAnthropologic - Architecture and Fabrication in the cognitive age
EditorsLiss C. Werner, Dietmar Koering
PublisherEducation and research in Computer Aided Architectural Design in Europe
Pages411-418
Number of pages8
ISBN (Print)9789491207204
Publication statusPublished - 2020
Event38th Conference on Education and Research in Computer Aided Architectural Design in Europe, eCAADe 2020 - Berlin, Germany
Duration: 16 Sept 202017 Sept 2020

Publication series

NameProceedings of the International Conference on Education and Research in Computer Aided Architectural Design in Europe
Volume1
ISSN (Print)2684-1843

Conference

Conference38th Conference on Education and Research in Computer Aided Architectural Design in Europe, eCAADe 2020
Country/TerritoryGermany
CityBerlin
Period16/09/2017/09/20

Bibliographical note

Publisher Copyright:
© 2020, Education and research in Computer Aided Architectural Design in Europe. All rights reserved.

Keywords

  • Data-driven design
  • Early-design phase
  • Machine-learning
  • Performance simulation

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