Exploring Cost Factors hindering Augmented Reality Adoption for Construction Worker Protection
Bibliographic record
Abstract
This paper seeks to identify cost factors associated with implementing augmented reality (AR) in construction worker protection in South Africa and explore strategies to enhance cost-effectiveness and affordability of AR Implementation in South African construction industry.The paper highlights the need for comprehensive cost-benefit analyses to assess AR's long-term financial impact.Some studies suggest that AR could reduce accidents and increase productivity over time, offsetting the initial investment.However, these benefits are speculative and require robust empirical support.Worker protection remains a paramount concern in the construction industry, characterized by dynamic work environments fraught with inherent risks and hazards.Despite the recognized benefits of AR technology in enhancing safety, its widespread adoption in construction has been hindered by various challenges, chief among them being cost factors.The decision to adopt AR solutions entails substantial financial investments encompassing initial acquisition costs, implementation expenses, and ongoing maintenance expenditures, which can pose significant barriers for construction firms, particularly smaller enterprises with limited resources.This study employed a systematic literature review approach to identify and analyse cost factors hindering the adoption of Augmented Reality (AR) for construction worker protection.The study found that cost is identified as a significant barrier to the effective deployment of digital technologies in the VM process in construction, including the high cost of acquiring and maintaining these technologies.In conclusion, creating awareness among VM experts and gaining client financial support are highlighted as important factors in overcoming cost-related challenges.The paper has identified specific cost factors associated with implementing augmented reality technology for construction worker protection in South Africa, including initial investment costs, maintenance expenses, and operational expenditures.It has also identified strategies that can be employed to enhance the costeffectiveness and affordability of implementing augmented reality technology for construction worker protection in South Africa, considering the cost factors.
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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 | 0.013 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".