A Network Learning Approach for Asset Management in Water Distribution Infrastructure
Bibliographic record
Abstract
The combination of factors including the aging of water distribution infrastructure, growth in water demands, and limited operating budgets have created interest in more robust and rigorous methodologies to prioritize rehabilitation and renewal decisions for water distribution infrastructure.One such procedure, probabilistic network modeling, can be used to investigate issues of water infrastructure failure and to develop inclusive and dynamic analyses.Decomposable Markov networks (DMNs) and machine learning techniques in the domain of water distribution systems are developed herein.A framework is described that assesses causality or correlation between pipe breaks and relevant factors based on data from the Greater Toronto Area (GTA).The role of factors such as cement mortar lining (CML), soil type, pipe material and dimension are employed.The framework can be used to assist decision-making by estimating probabilities of future pipe breakage and identifying rehabilitation options to decrease breakage probabilities.The DMN-based machine learning approach provides an excellent way of combining engineering knowledge and available data in a robust and formal statistical manner.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".