Implementing data-driven parametric building design with a flexible toolbox

Nathan C. Brown, Violetta Jusiega, Caitlin T. Mueller

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

Designers in architecture and engineering are increasingly employing parametric models linked to performance simulations to assist in early building design decisions. This context presents a clear opportunity to integrate advanced functionality for engaging with quantitative design objectives directly into computational design environments. This paper presents a toolbox for data-driven design, which draws from data science and optimization methods to enable customized workflows for early design space exploration. It then applies these approaches to a multi-objective conceptual design problem involving structural and energy performance for a long span roof with complex geometry and considerable design freedom. The case study moves from initial brainstorming through design refinement while demonstrating the advantages of flexible workflows for managing design data. Through investigation of a realistic early design prompt, this paper reveals strengths, limitations, potential pitfalls, and future opportunities for data-driven parametric design.

Original languageEnglish (US)
Article number103252
JournalAutomation in Construction
Volume118
DOIs
StatePublished - Oct 2020

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Civil and Structural Engineering
  • Building and Construction

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