In Situ Monitoring of Environmental Conditions for Stress Corrosion Cracking
Bibliographic record
Abstract
TransCanada Pipelines have been involved with monitoring environmental conditions at pipe depth for almost 10 years. The purpose of this monitoring is to understand the mechanism of SCC as it occurs in the field and to assist in the development of site-selection models for identifying locations where SCC might be occurring. Monitoring can either be done at many locations along the right-of-way at a given time or continuously at discrete locations in order to observe seasonal variations. A range of environmental parameters can be monitored. Early work focussed on parameters relevant to corrosion, such as soil resistivity, redox potential, temperature, pH, and on- and off-potentials. More recently, parameters relevant to SCC have also been monitored, such as soil CO2 and permeable hydrogen concentrations. In addition, the extent to which these parameters change seasonally has been monitored to determine if the environment is conducive to SCC continuously or whether cracking might only occur at certain times of the year. In terms of implementation, the results of in situ monitoring can be used as part of a larger integrity management program to decide where and when to mitigate SCC. Correlations between soil parameters measured using the portable probe and known SCC sites can be used to identify other susceptible locations or to prioritize different lines for inspection. Seasonal variations at a particular location can be used to derive effective crack growth rates from accelerated laboratory testing in order to determine re-inspection intervals. Examples are provided of both portable and permanent NOVAProbe measurements for low-pH and high-pH SCC.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".