Application of the fuzzy performance measures to the City of London water supply system
Bibliographic record
Abstract
Most engineering systems are subject to a wide range of possible uncertain future conditions. The probabilistic reliability analysis usually fails to address the problems of human error, subjectivity, and lack of system performance history and records. This paper explores the utility of the following fuzzy performance measures for evaluating the performance of a complex water supply system: (i) combined reliability–vulnerability, (ii) robustness, and (iii) resiliency. The regional water supply system for the City of London, Ontario, Canada, is used as the case study. The computational requirements for the implementation of the fuzzy performance measures and their sensitivity to different shapes of fuzzy membership functions are investigated. The study illustrates the capability of the fuzzy performance measures to handle uncertainty and identify critical system components. This can be of value in identifying the optimal level of improvement that will increase the overall system performance.Key words: water supply, fuzzy sets, risk, performance indicators.
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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".