Unmanned aerial vehicles usage on south african construction projects: perceived benefits
Bibliographic record
Abstract
In recent years, unmanned aerial vehicles (UAVs) are being employed in various parts of the engineering industries for project development, project management, surveying, among others. UAVs can also be adopted in construction for pre-planning, proper surveying of the given area, checking or inspecting site safety and quality monitoring. Based on these envisaged uses, this study is set to assess the benefits of UAVs usage in the construction industry. This was achieved through a detailed literature review combined with empirical data analysis. Data was retrieved through questionnaire survey distributed to professionals randomly in the South African construction industry. The retrieved data was analysed using descriptive and inferential data analysis methods. Findings revealed that UAVs adoption in the construction industry will lead to reduction in worker’s injury as it will be implemented for monitoring of workers activities on site. It was also revealed that UAVs are useful in on-site asset tracking which allows stakeholders to have real-time information on the construction project from anywhere. The study concluded that the efficiency in the performance of the construction industry can be achieved through the adoption of UAVs in the different stages of 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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".