Causal Analysis of Accidents During Wind Power Engineering Construction Using Bayesian Networks
Bibliographic record
Abstract
To investigate the causes of accidents during the construction phase of wind power projects and to prevent their occurrence, this study draws on accident investigation reports and the "2-4"Model to summarize unsafe behaviors and actions.It selects causative variables from four aspects: human, equipment, environment, and management.Integrating fault tree analysis, a Bayesian network (BN) model for analyzing the causes of accidents during wind power engineering construction is constructed using the BN software, GeNie.The model undergoes structural and parameter learning, calculating the conditional probability distribution and posterior probabilities of each node.Through variable sensitivity and analysis of the most significant causative chains of accidents, the key factor paths leading to accidents are identified, contributing to reducing the accident rate during the construction phase of wind power projects.The results indicate that inadequate personal protection and violations of regulations are prevalent among human factors.In terms of management factors, insufficient safety management and supervision are the main contributors to accidents, with a probability value exceeding 70%.Geological conditions, road conditions, limited workspace, exceptional environmental changes, proximity to energized machines, and safety protection equipment failures are significant factors in accidents during the construction phase.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".