The Effect of Augmented Reality on Performance, Task Loading, and Situational Awareness in Construction Inspection Tasks
Bibliographic record
Abstract
The construction industry is characterized by the need to perform detail-oriented tasks in complex environments – requiring tools and systems that prioritize precision, efficiency and safety. While Augmented Reality (AR) has emerged as a potential avenue for these tools, its effectiveness and impact on performance and situation awareness, as well as the challenges it may introduce, are yet to be fully understood. This research aims to investigate the efficacy of AR’s use in this domain through the representative task of inspecting prefabricated concrete panel casts, using studies complete with visual and auditory distraction simulations to explore two new AR schematic visualization systems. This work employs a dual-task user study (N = 18) to measure the impact of the AR on Situation Awareness, Task Loading, and Task Performance when compared to the conventional standard of paper blueprints. We find that AR solutions can lower perceived mental and temporal demands without negatively affecting situation awareness. Further, the AR solutions reduced the rate of false negatives and required less time than paper blueprints, suggesting that AR holds promise for improving construction workflows through increased performance and speed without impacting the safety provided by maintaining situation awareness.
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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 | 0.001 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".