Quantifying effects of accidents by fuzzy-logic- and simulation-based analysis
Bibliographic record
Abstract
This study quantifies the effects of accidents by defining one of the indirect costs, the productive time lost owing to accidents in utility trenching operations. The probability of accidents, estimated by fuzzy-logic-based analysis of the performance of the factors (training, supervision, and preplanning) affecting safety in utility trenching operations, was used to quantify, based on simulation analysis, the productivity loss due to process delays resulting from accidents during excavation and pipe installation. It was determined that the productivity loss resulting from accidents during excavation is greater than that resulting from accidents during pipe installation. During excavation, the "very poor" condition of preplanning is most critical to productivity loss due to accidents, whereas during pipe installation, the condition of training and supervision affects the productivity loss more than that of preplanning. This paper provides insights into the relationship between the condition of the safety factors and the possible productivity loss by concomitant probability of accidents to quantify the effects of the accidents.Key words: effect of accidents, probability of accidents, productivity, fuzzy logic, simulation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".