The Level of Utilizing Water Pipeline Condition Assessment Tools by Public Owners: A Structured Survey
Bibliographic record
Abstract
Proactive maintenance is an essential first step toward healthy infrastructure and sustainable capital spending. Condition assessment tools are utilized to collect data that helps asset owners to assess the structural capacity or detect leaks in water pipelines. While there are various condition assessment tools, in some jurisdictions, such proactive maintenance is not so popular due to funding constraints. Generally, asset owners mainly rely on age to estimate service life concepts in making capital investment decisions. Despite age being a major consideration, the accuracy level is considerably lower compared to advanced field inspections. Currently, the existing literature lacks detailed information on owner preferences on condition assessment programs. Therefore, the main objective of this paper is to collect data from public owners, to help understand the level of utilization of condition assessment tools and other data that assesses the maturity of any condition assessment program. The data is collected and analyzed through a structured survey sent to water pipeline owners. Once completed, the study will help understand the maturity level of condition assessment programs established in many jurisdictions and aid as a tool for future benchmarking activities while recommending continuous improvements.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".