Metaverse Applications in Construction Research: Are We There Yet?
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.052 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.030 | 0.044 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".