Pragmatic Design Decision Support for Additive Construction Using Formal Knowledge and Its Prospects for Synergy with a Feedback Mechanism

Chao Li, Ata Zahedi, Frank Petzold

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

The construction industry has long been labor-intensive, with slow productivity growth and a significant environmental impact. In this regard, the ever-increasing practices of additive manufacturing (AM) in construction have presented a variety of advantages and are deemed one of the critical technologies for the concept of Construction 4.0. Building information modeling (BIM) as an enabler for the digital transformation in the architecture, engineering, and construction (AEC) domain provides a framework for considering novel AM methods during the early stages of architectural design. It is known that decisions during early design stages significantly impact the subsequent planning and construction phases, whereas missing AM knowledge by architects and engineers could in turn impede the adoption of AM technologies when the early determination of appropriate manufacturing methods needs to be made. Meanwhile, the early stages of architectural design are characterized by vagueness, uncertainty, and incompleteness, which have to be clarified iteratively by both architects and domain experts. To this end, this paper introduces a knowledge-driven design decision support that prospectively incorporates an adaptive feedback mechanism under the BIM methodology. As such, architects can be assisted in choosing appropriate construction methods during the early stages of architectural design.

Original languageEnglish
Article number2072
JournalBuildings
Volume12
Issue number12
DOIs
StatePublished - Dec 2022

Keywords

  • BIM
  • additive manufacturing in construction
  • design decision support
  • semantic web

Fingerprint

Dive into the research topics of 'Pragmatic Design Decision Support for Additive Construction Using Formal Knowledge and Its Prospects for Synergy with a Feedback Mechanism'. Together they form a unique fingerprint.

Cite this