Critical Review of Virtual Reality Applications in Offsite Construction Research
Bibliographic record
Abstract
Offsite construction (OSC) techniques are argued to provide superior quality and shorter schedules compared with traditional techniques. Nonetheless, the pace of OSC implementation has been slow due to the influence of several barriers. In recent years, virtual reality (VR) applications have been used to address many of these barriers and promote the implementation of OSC projects. However, a comprehensive and coherent literature review that establishes the current state and categorizes VR applications in OSC projects is still lacking. To address this research gap, this study provides a state-of-the-art review of VR applications in OSC (VR–OSC) using the scientometric and systematic review methods. This study characterizes the synthesis between VR and OSC and identifies research trends and gaps that can be studied in future VR–OSC research. The scientometric review focuses on identifying the main topics of both research domains separately and combined based on the collected articles. The systematic review, meanwhile, qualitatively evaluates these articles, highlighting the existing research gaps and anticipating future research frontiers. The scientometric results indicate that VR applications in OSC can be organized into a number of clusters, such as Crane Operations and Onsite Planning, Educational Applications, Safety and Ergonomics, and Evaluation of Design Alternatives. The qualitative analysis identifies several future research directions to advance the field of VR–OSC, including (1) multiuser VR models in crane operation planning, (2) consideration of the role of human emotions in VR safety training by adopting biometric sensors, (3) decentralized web-VR platforms for remote OSC planning, and (4) VR-solutions for modeling robotic movements in OSC factories. This study can serve as a useful point of reference for VR–OSC researchers and provides a sound foundation for future research on VR–OSC.
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.
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 | 0.035 | 0.140 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.034 | 0.023 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".