Economics of Inspection and Condition Assessment of High-Consequence Water Pipeline and Assessing Its Remaining Life
Bibliographic record
Abstract
A probabilistic approach that considers the entire lifecycle cost of the water pipeline, accounting for deterioration rate, failure consequences, cost of rehabilitation, accuracy and cost of inspection/condition assessment, cost of emergency repair versus planned intervention and cost of total pipe replacement was introduced in an earlier paper. The approach allows us to combine expert-opinion (semi-informative assessment) with hard field data. New hard field data (e.g., failure events, inspection/condition assessment results) continually become available throughout the life of the pipeline. This incoming data stream can be incorporated into the analysis to provide robust, well-informed and reproducible assessment of deterioration rate and remaining life. The framework was implemented in an MS-Excel-based decision support tool, referred to as pipeline inspection decision analyzer (PIDA). This paper demonstrates the practical application of the proposed framework in the real world through comprehensive case studies, data for which were obtained from collaborating Canadian and US water utilities and pipeline owners. As is always the case in reality, most pipeline owners did not have all the required data to carry out a fully informed analysis. We illustrate how one might deal with missing data, how PIDA may be used to arrive at well-supported, rational decisions on when to deploy inspection and condition assessment, what techniques/technologies to select among competing ones and when it is time to stop assessing the pipeline condition and plan for replacement. Sensitivity analyses are also conducted to explore how various assumptions, necessitated by uncertainty, may impact analysis results.
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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 | 0.001 | 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".