A review of augmented reality applied to underground construction
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
Unintentional striking of underground utilities from construction activities often results in high economic consequences. Advanced technology and sophisticated visualization techniques such as augmented reality (AR) has the potential to play a significant role in mitigating such devastating consequences. To better understand the state-of-the-art technology of AR applications in the underground construction industry, it is important to identify challenges and barriers. This paper provides a systematic literature review of applications in the construction industry in general in which journal articles were reviewed, analysed, and summarized. Through this method, the main challenges associated with AR were revealed and feasible solutions were suggested. Issues were found with 1) data collection; 2) modelling and alignment barriers; 3) hardware limitations; 4) tracking; and 5) managing data. This research examined an efficient solution to the problems of AR by proposing a framework for future implementation with main applications in the United States, Canada, and Australia.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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