An economic loss model for failure of sewer pipelines
Bibliographic record
Abstract
Estimating the costs of failure for sewer pipelines is usually accompanied with uncertainties because of the difficulty in capturing the relationship between the physical and economical characteristics of failed pipelines. To reduce such uncertainties economic loss models are usually used to evaluate the consequences of failure. This paper presents a methodology to estimate economic loss as a result of sewer pipelines’ failure using cost benefit analysis approach. Costs of sewer pipelines’ failure in addition to costs resulting from avoiding such failures are identified and analysed. To validate the proposed methodology, actual costs from a real failure incident were compared with the proposed model outputs. The model could estimate the direct and indirect costs with a deviation ranging between 10–12% and 22–30%, respectively. By implementing the proposed methodology on two case studies, it was found that the indirect costs as a result of sewer pipelines’ failure represent a significant portion ranging between 89 and 94% of the total costs of failure. Also, it was found that costs related to environment, delays to work and traffic disruptions contribute by 12–35% to the indirect costs.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".