Condition assessment model for sewer pipelines using fuzzy-based evidential reasoning
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A condition assessment model for gravity and pressurised sewer pipelines using Fuzzy Set Theory (FST), and Evidential Reasoning (ER) with the aid of Fuzzy Analytical Network Process (FANP) integrated with Monte-Carlo Simulation is presented in this paper. Seventeen factors were considered for gravity pipelines in addition to the operating pressure for pressurised pipelines. The model was developed using relative weights for the different factors affecting pipelines condition which were obtained using FANP integrated with Monte-Carlo Simulation based on the results of a questionnaire that was distributed to experts working in the field of infrastructures. FST was used to set thresholds for the different effect values of factors on the pipelines’ condition, whereas ER was used to determine the final condition assessment index for the pipeline by aggregating both the relative weights and effect values for the different affecting factors.
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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