Integration of deep learning and extended reality technologies in construction engineering and management: a mixed review method

Yee Sye Lee, Ali Rashidi, Amin Talei, Mehrdad Arashpour, Farzad Pour Rahimian

Research output: Contribution to journalReview ArticleResearchpeer-review

4 Citations (Scopus)

Abstract

Purpose: In recent years, deep learning and extended reality (XR) technologies have gained popularity in the built environment, especially in construction engineering and management. A significant amount of research efforts has been thus dedicated to the automation of construction-related activities and visualization of the construction process. The purpose of this study is to investigate potential research opportunities in the integration of deep learning and XR technologies in construction engineering and management. Design/methodology/approach: This study presents a literature review of 164 research articles published in Scopus from 2006 to 2021, based on strict data acquisition criteria. A mixed review method, consisting of a scientometric analysis and systematic review, is conducted in this study to identify research gaps and propose future research directions. Findings: The proposed research directions can be categorized into four areas, including realism of training simulations; integration of visual and audio-based classification; automated hazard detection in head-mounted displays (HMDs); and context awareness in HMDs. Originality/value: This study contributes to the body of knowledge by identifying the necessity of integrating deep learning and XR technologies in facilitating the construction engineering and management process.

Original languageEnglish
Pages (from-to)671-701
Number of pages31
JournalConstruction Innovation
Volume22
Issue number3
DOIs
Publication statusPublished - 8 Jun 2022

Keywords

  • Augmented reality
  • Autonomous construction
  • Construction engineering
  • Deep learning
  • Digital transformation
  • Extended reality
  • Virtual reality

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