Evaluation of the barriers to and drivers of the incorporation of unmanned aerial vehicles into dam asset management
Bibliographic record
Abstract
Dam asset management entails the consistent evaluation of the state and usefulness of dams as tangible assets in terms of their expected lifespan, criticality, operational history, maintenance history, and long-term financing plan. This research aims to identify and evaluate the critical drivers and barriers associated with the use of unmanned aerial vehicles in dam asset management. This study uses rough decision-making trial and evaluation laboratory and interpretive structure modelling to analyze the interactions that take place between the barriers to and the drivers of unmanned aerial vehicles incorporation. To overcome the problem of vagueness, it develops rough set theory to determine the driving and dependence power of the drivers and the barriers, respectively. According to the findings, the most significant barrier to the incorporation of unmanned aerial vehicles into dam asset management is a lack of skilled operators, while the most significant driver of their incorporation is their cost-effectiveness. The findings of this study will be highly valuable to practitioners and government agencies working on the implementation of unmanned aerial vehicles in dam asset management, as they will enable them to correctly assess the different aspects of unmanned aerial vehicles incorporation.
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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 | 0.006 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".