Modeling Failure Risk in Buried Pipes Using Fuzzy Markov Deterioration Process
Why this work is in the frame
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Bibliographic record
Abstract
Numerous models have been proposed in the last two decades for the deterioration of buried pipes. The most prominent approach has been the Markovian deterioration processes (MDP), which requires that the condition of the deteriorating system be encoded as an ordinal condition state. This encoding is based on numerous distress indicators obtained possibly from direct and indirect observations, as well as from non-destructive tests. To date, few buried pipes have been inspected and their condition assessed. In addition, the encoding of distress indicators into condition states is inherently imprecise and involves subjective judgment. Furthermore, the consequences of failure for buried pipes are often difficult to quantify precisely due to lack of data. In this paper, a new approach is presented to model the deterioration of buried pipes using a fuzzy rule-based, non-homogeneous Markov process. This deterioration model yields possibility of failure at every point along the life of the pipe. The possibility of failure, expressed as a fuzzy number, is coupled with the failure consequence (also expressed as a fuzzy number) to obtain the failure risk as a function of the pipe age. The use of fuzzy sets and fuzzy techniques help to incorporate the inherent imprecision and subjectivity of the data, as well as to propagate these attributes throughout the model, yielding more realistic results. At the time of submission, adequate and sufficient data to validate the model were not available.
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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 | 0.000 | 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 it