A Fuzzy Expert System for Prioritizing Rehabilitation of Sewer Networks
Bibliographic record
Abstract
Abstract: Rehabilitation of sewer networks is a huge and very costly global problem that has often been treated on a crisis-based approach. The development of a rehabilitation program requires models and tools for assessing the condition and performance of sewers. The original contribution of this study is the development of a ranking scheme for sewer rehabilitation priorities. A fuzzy expert system was applied with inputs from a combined assessment of hydraulic, structural performance and potential failure consequences. The fuzzy structural system computes the global structural performance index for each pipe using internal condition, surrounding condition, and site vulnerability (SV) as inputs. The fuzzy hydraulic system uses hydraulic performance index (HPI), hydraulic performance impact, and SV to compute the global HPI. Finally, the fuzzy global system uses all these factors to compute the global performance index for each pipe. This methodology was successfully applied to the sewer system of the City of Laval in Canada. The results show how the fuzzy inference system may be used to establish rehabilitation priorities for each pipe section. The fuzzy expert system provides more realistic results than the intuitive approaches that use structural and hydraulic performance maximum and mean.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".