Factors Affecting Corrosion of Buried Cast Iron Pipes
Bibliographic record
Abstract
Although corrosion of metal in soils has been intensively investigated in the past, a review of the published literature shows that limited research has been undertaken to understand how soil properties affect the corrosion behavior of cast iron pipes, owing to the scarcity of the reported information on buried metal corrosion in its backfill soil condition. In this paper, a methodology is proposed to statistically analyze the effects of soil properties on corrosion behavior, and a comprehensive and long-term historical corrosion database of buried cast iron pipes is thoroughly interpreted. The corrosion is characterized by two time-independent parameters in each sample, that is, the proportionality (k) and exponent (n) factors of the power law model. It is found that the exponent factor n of power law model is closely associated with the level of soil aeration. It is also found that grouping corrosion data based on soil aeration produces stronger correlations between soil properties and corrosion rates compared with that when taking all soil samples as a whole. The authors conclude that an appropriate classification of soils can benefit the identification of key factors influencing corrosion of buried cast iron pipes at different exposure times. This research provides further knowledge for asset managers and engineers to accurately predict the failure of corrosion-affected cast iron pipes.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 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".