Translation of pipe inspection results into condition rating using fuzzy synthetic valuation technique
Bibliographic record
Abstract
An important step towards the assessment and management of failure risk in large-diameter (transmission) water mains is to observe distress indicators through scheduled inspections (using non-destructive or visual techniques) and translate these into condition ratings. Condition rating reflects an aggregate state of the pipe's health.Distress indicators are physical manifestations of the ageing process. The type (or form) and location of observed distress indicators in large-diameter mains are dependent on the pipe material and its surrounding environment. The physicochemical processes that promote ageing are often not understood well enough to merit an adequate physicochemical (based on mechanics or electrochemistry or microbiology) model. Further, the encoding of distress indicators into condition rating is inherently imprecise and involves subjective judgment. Fuzzy logic-based tools enable the use of engineering judgment, experience and scarce field data to translate the level of distress to condition ratings.
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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 | 0.000 | 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 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".