Risk-based integrity assessment and failure probabilities of a residential single wall steel aboveground fuel oil storage tanks
Bibliographic record
Abstract
Integrity assessment of residential engineering equipments such as fuel oil storage tanks is not done during its service life. In the absence of the incremental deterioration data over the service life, the deterioration rates can only be characterized using failure data. This is quite opposite to the process industry practice of inservice inspections and integrity assessment. In process facility inspections are done to assess the on going deterioration processes and to ensure safe operations during the service life of equipments. Limited work is reported on the estimation of the corrosion rates based on the component failure durations as compared to the work based on in-life inspection data. The current work develops a methodology that can establish corrosion rate estimates based on the failure data (data collected at the realization of the failure). A Bayesian approach to model the corrosion rate for a residential single wall aboveground fuel oil storage tank, is proposed here. The corrosion failure data utilized in this study are obtained from the database of Technical Standard and Safety Authority, Ontario. The posterior density function is used to quantify epistemic uncertainty in the corrosion rate parameter. A probabilistic model is, then, employed to account for the overall uncertainty associated with the corrosion rate variable. The corrosion rate estimate is utilized as an input to a stochastic deterioration process to assess the failure probabilities of the equipment.
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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 | 0.002 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".