Metaverse Applications in Construction Research: Are We There Yet?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Despite the significant potential of extended reality (XR) technologies in construction research, achieving a collaborative virtual environment that allows real-time engagements among various geographically remote stakeholders is a challenge. Building upon XR technologies, metaverse technology was introduced recently in construction research as a possible solution to address this issue, as it allows multiple users to engage and communicate in virtual environments. Being introduced recently, the applications of the metaverse in construction research (ConVerse) remain immature, vague, and unexplored. Seasoned researchers interested in the ConVerse research still need a reference guide to better understand the potential of the metaverse and its current applications. As such, this paper introduces a comprehensive review of the ConVerse applications, highlighting the research themes and assessing the exploitation level of the metaverse technology based on a set of six defined criteria. It also explores the current deficiencies of the ConVerse applications and the needed measures to achieve a higher level of maturity. The results showed that the Design Review applications contribute most to the ConVerse literature, followed by Activities Planning and Safety applications. It was also revealed that no article in the ConVerse literature had considered the whole six criteria of the metaverse, while 76.2% overlooked at least two criteria, and only 23.8% missed only one criterion. Subsequently, the paper highlights four main future directions to leverage the use of metaverse technology in the ConVerse research. This paper serves as a helpful guide for ConVerse researchers and provides them with a sound foundation for future research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it