Role of Cathodic Protection on Physical Condition and Pipe Break Linkage
Bibliographic record
Abstract
Cathodic protection plays a crucial role in preventing the corrosion of cast and ductile iron pipes. However, less attention has been given to the long-term chemical reactions affecting crack growth and failures of pipes with cathodic protection. The present study, therefore, attempted to fill this gap by developing a model where cathodic protection is positioned as the moderating variable in the relationship between physical condition and pipe break frequency. Data from 426 pipes installed between 1955 and 1974 were obtained from a large-scale municipality in Ontario, Canada. The results showed a direct effect of physical condition on the pipe break frequency (β=0.78, p<0.01), demonstrating that as the physical condition worsens, the rate of pipe breakages increases. The findings further support the long-term moderating role of cathodic protection on physical condition and pipe breakage linkage (β=0.42, p<0.01), indicating a lower likelihood of failure for pipes with cathodic protection than their counterpart with similar physical conditions. The predictive model for the likelihood of failure, expressed as the expected number of breaks within 10 years, is then presented as a function of the pipe condition and the cathodic protection. The study’s findings emphasized the significance of implementing cathodic protection for pipes in both aggressive and nonaggressive environments.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".