Pipe Failure Prediction with Consideration of Climate Change
Bibliographic record
Abstract
Abstract Rapidly changing climate combined with aging and deterioration of water supply system pipes present a significant challenge to most water utilities. Water transmission and distribution pipes are most often susceptible to the risk of failure because of their spatial diversity and inaccessibility with limited information available to understand their deterioration process. This article provides a modeling approach, Bayesian Model Averaging (BMA), for using climate projections in conjunction with other operational, physical, and environmental factors to predict/forecast cast iron (CI) pipe failure rates in the city of Calgary. The data from the city of Calgary, which constitutes operational factors, pipe physical attributes, and soil properties, and a statistically downscaled climate data from the Coupled Model Intercomparison Project phase 5 (CMIP5) are used for the demonstration of the developed BMA model. The result of parameter identification shows that the climatic parameters are found to be less sensitive; however, a significant change in sensitivity over time was exhibited during 1956–2014. The model results showed that there is a decrease in the failure rate for all CI pipes. However, the highest failure rate is expected for smaller‐diameter pipes compared to medium‐ and larger‐diameter pipes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".