Review on the Inclusion of Climate Factors in Water Main Failure Models
Bibliographic record
Abstract
Failure of water mains can disrupt essential services, increase costs, and pose risk to public safety and health; therefore, accurate predictions of failure are important to infrastructure management. This study focuses on climate factors due to their impact on water main failures, which is less explored compared to other factors. It systematically reviews existing literature related to water main failure prediction models with the objective of gaining a better understating of the effect of climatic factors (a subcategory of environmental factors) on water main failures. More than 300 papers related to water main prediction models were identified and screened for climatic factors. Of these 300 research papers, 15 studies related to climatic factors were identified and reviewed in detail to evaluate the effect of climatic subfactors on the water main failures. The climatic parameters considered as parameters in water main failure models include temperature, rain deficit, drought, freezing index, and precipitation. The findings indicate that temperature and precipitation are the primary climatic factors influencing water main failures. Cold conditions were found to elevate failure rates in CI, PVC, and DI pipes, while warm conditions led to increased failure rates in steel and AC pipes. Additionally, pipes of various materials and sizes were found to have higher failure rates during seasons with low precipitation.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".