Augmented Reality (AR) in the Manufacturing Phases of Off-Site Construction Projects
Bibliographic record
Abstract
This paper investigates the integration of Augmented Reality (AR) technology into the manufacturing phases of off-site construction projects, with a specific focus on the Canadian construction industry.Through a comprehensive systematic literature review and semi-structured interviews, the study identifies the benefits and barriers associated with adopting AR to enhance visualization, collaboration, and decision-making during project execution.The survey involves key stakeholders, including construction managers, architects, engineers, contractors, and subcontractors, capturing their experiences and perceptions of AR technology.The findings highlight significant benefits such as improved real-time collaboration, enhanced safety, reduced errors and rework, increased project efficiency, and higher client satisfaction.AR's capability to overlay precise spatial information onto real-world environments is identified as a transformative advantage in off-site construction workflows.At the same time, the study addresses notable challenges to AR adoption, including high initial investment costs, a lack of skilled personnel, compatibility issues with existing software systems, and concerns about data security and privacy.Resistance to change and the evolving maturity of AR technology are also cited as key obstacles.This paper provides a balanced perspective on the current state of AR adoption in the Canadian off-site construction industry.The findings aim to inform stakeholders and guide future strategies for integrating AR technology to improve the efficiency, safety, and quality of off-site construction projects.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".