Condition assessment of sewer pipelines using multi attribute utility theory (MAUT)
Bibliographic record
Abstract
Many municipalities rely on the interpretation of Closed Circuit Television (CCTV) inspection reports to arrive to a condition assessment grade for the inspected sewer pipelines. The grades stand as a key for decision makers in their maintenance and rehabilitation plans. The paper will propose a condition assessment for sewer pipelines using Multi Attribute Utility Theory (MAUT). The condition assessment model utilizes MAUT to generate utility functions for four sewer pipeline defects: deformation, settled deposits, infiltration and surface damage. Minimum and maximum values were adopted, where applicable, from the Water Research center (WRc) to build the utility functions. The grades are changed to 0 to 10 utility scale to plot the points considered. The deformation defect utility curve was polynomial of degree two and the coefficient of correlation (R2) was exactly 1. However, the settled deposits defect utility curve was polynomial of degree three with R2 of 0.9993. The proposed model 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 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 | 0.001 |
| 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".