Towards Optimum Condition Assessment Policies for Water and Sewer Networks
Bibliographic record
Abstract
With ageing water and sewer infrastructure in North America, assessing the condition of these assets has received increased attention in the past few years. Condition assessment is an integral component in any asset management program. Determining the condition of buried infrastructure tends to be more cumbersome, costly and error-prone compared to other surface infrastructure like roads and buildings. For sewers, CCTV is considered the industry standard for condition assessment technologies. For pressurized water pipelines, technologies tend to be more costly and uncertain (e.g. electromagnetic, sonar, acoustic leak detection, infrared etc...). Faced with constrained budgets and the need to obtain reliable condition information to drive their asset management processes, infrastructure owners must balance the value of information obtained through condition assessments with the cost of obtaining this information. This paper presents an analytical framework and decision support system to evaluate the value of information for condition assessments of water and sewer infrastructure. The framework considers: 1) risks associated with operating the asset, 2) potential direct and indirect costs of infrastructure failure, and 3) customer expectations for the system's level of service. The decision support system utilizes Partially Observable Markov Decision Process (POMDP) to compare between the reliability and cost of the condition assessment technology. The system is demonstrated on the water and sewer networks for the City of Hamilton, Canada.
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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 | 0.000 |
| 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 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".