Augmented reality application areas for the architecture, engineering, and construction industry
Bibliographic record
Abstract
Augmented reality (AR) is among the technologies that have the potential to advance the Architecture, Engineering, and Construction (AEC) industry. Yet, studies show that there remain challenges in applying AR in AEC. According to the literature, the use of AR is focused on the construction phase to address performance, supervisory, and safety-related concerns. However, other phases of AEC projects could also benefit from this technology. Accordingly, this chapter provides an application-centric study to assess the state-of-the-art applications areas of AR in the AEC industry. Various applications have been identified as visualization and simulation; in-situ experience; real-time information retrieval; maintenance, inspection, and repair; project documentation; heavy equipment operation; educational training; health and safety; site navigation; and automated measurements. To further explore these application areas, a case study was conducted using the AR solution of Trimble XR10 with HoloLens 2 in a precast construction context. The results show that existing AR technologies and systems for simulation/visualization and construction quality control are still immature. The study highlighted the current use cases, the potential for technology improvements, and the obstacles that hinder the widespread AR implementation in the AEC industry. Considering these factors, further directions and future research paths for innovators are proposed.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.044 | 0.015 |
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".