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 primary aim 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 1) Realism of training simulations; 2) Integration of visual and audio-based classification; 3) Automated hazard detection in head-mounted displays (HMDs); and 4) Context-awareness in head-mounted displays (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.
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 primary aim 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 1) Realism of training simulations; 2) Integration of visual and audio-based classification; 3) Automated hazard detection in head-mounted displays (HMDs); and 4) Context-awareness in head-mounted displays (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 language | English |
|---|---|
| Journal | Construction Innovation |
| Publication status | Accepted/In press - 29 Apr 2022 |