Hindering factors to the utilisation of UAVs for construction projects in South Africa
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
As the designs of construction projects become more complex, there is a corresponding increase in the difficulty encountered in project monitoring. Hence, it is advisable to integrate innovative technologies such as the use of an unmanned aerial vehicle (UAV) to abate some of the problems encountered in the delivery of construction projects. This paper aims to evaluate the barriers to the usage of UAVs in construction project delivery in South Africa. Adopting a quantitative method for the study, data was collected with the aid of a questionnaire from construction professionals in Gauteng province, South Africa. Findings from the study indicate that the most significant factors hindering the deployment of drones in the South African construction industry are lack of training by institutions and lack of investment in new technologies by organisations. Conclusively, the paper recommends measures that would propel the espousal of drone technologies for effective and efficient construction project delivery in the South African construction industry.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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