Multiattribute Utility Theory Deployment in Sewer Defects Assessment
Bibliographic record
Abstract
Assessing the condition of sewer pipelines is a backbone process to plan for rehabilitation and maintenance work. The closed circuit-television (CCTV) method is the widely adopted method to record the inner condition of the pipelines, which is then interpreted by a practitioner. This paper presents a condition assessment framework for sewer pipelines using multiattribute utility theory (MAUT). The condition assessment model utilizes MAUT to generate several utility functions for four sewer pipeline defects: deformation, settled deposits, infiltration, and surface damage. A proposed surface damage evaluation methodology is presented to assess the surface damage defect for three different materials: reinforced concrete, vitrified clay, and ductile iron. An aggregated condition index is computed based on the relative importance weights of the studied defects and tested with several rounding types. The rounding up type produced the optimum results, and the values were compared with the Concordia Sewer Protocol (CSP) suggested methodology yielding an average difference between the two approaches of 3.33%; and a mean absolute error (MAE) of 0.33. A sensitivity analysis was then carried out to check the impact of the change of the relative importance weights on the overall index. The proposed methodology aims to provide information for asset managers about the severity of some sewer defects existing in sewer pipelines. In addition, it reinforces their plans for rehabilitation and maintenance by suggesting the existing condition of the sewer pipelines.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".