Risk Assessment for Water Mains Using Fuzzy Approach
Why this work is in the frame
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Bibliographic record
Abstract
The concept of responding to the risk of water pipelines failure has been undergoing through a great change from being active to being proactive to failure events by planning for rehabilitation plans that maintain the water main in good working conditions. This paper designs a framework to evaluate the risk of water main failure using hierarchal fuzzy expert system. There are sixteen risk-of-failure factors that represent both the probability of failure and the negative consequences of failure event and are categorized into four main risk-of-failure factors. A risk of failure model is built that evaluates the risk of pipelines failure using Fuzzy Expert System technique that accounts for the uncertainty usually encountered when evaluating the risk of failure. Some of the findings are that the pipe age gives a strong indication of the condition of the water mains, then, the pipe material and breakage rate come into play, and that the damage to surroundings/business disruption has the most negative impact of a failure event.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 it