Management of failure risk in large-diameter buried pipes using fuzzy-based techniques
Bibliographic record
Abstract
Effective management of failure risk of large-diameter water mains requires knowledge of their current condition, their rate of deterioration, the expected consequences of failure and the owner's risk tolerance. By far the greatest obstacle to formulating an effective strategy is the lack of sufficient historical data on the deterioration of these buried pipes. The National Research Council of Canada (NRC), with the support of the American Water Works Association Research Foundation (AwwaRF) is developing a new approach, which is largely based on fuzzy-based techniques. Fuzzy-based techniques seem to be particularly suited to modeling the deterioration of buried infrastructure assets, for which data are scarce, cause-effect knowledge is imprecise and observations and criteria are often expressed in vague (linguistic) terms (e.g., 'good', 'fair' 'poor' condition, etc.). The use of fuzzy sets and fuzzy-based techniques helps to incorporate the inherent imprecision, uncertainty and subjectivity of available data, as well as to propagate these attributes throughout the model, yielding more realistic results. Earlier publications, reporting on the same research effort, introduced two new concepts: (a) modeling the deterioration of a buried pipe as a fuzzy Markov process, and (b) combining the possibility of failure with the fuzzy consequences to obtain fuzzy risk of failure throughout the life of the pipe. In this paper a method is presented to use the fuzzy deterioration model and the fuzzy risk for the effective management of failure risk. These decisions include when to renew a deteriorated pipe, or alternatively, when to schedule the next inspection and condition assessment, and if renewal is required, what renewal alternative should be selected.
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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".