Bibliographic record
Abstract
According to the PHMSA data on reportable incidents, for the 20 years ranging from 1995 to 2014, excavation damage accounted for 16.4% of the incidents on 301,732 miles of gas transmission pipelines and 15.6% of the incidents on 199,210 miles of hazardous liquid pipelines. On the whole, excavation damage is a major cause of incidents, ranking third following incidents caused by material/weld/equipment failure and corrosion. For the purposes of this study, mechanical damage is separated into two categories, i.e. immediate failures and delayed failures. An immediate failure is one which occurs at the instant the damage is done to the pipeline. A puncture, for example, is an immediate failure. Delayed failures involve damage that is not sufficient to cause a leak or a rupture at the time it is inflicted. On average, 14.6% of the mechanical damage incidents in gas transmission pipelines and 13.3% of the mechanical damage incidents in hazardous liquid pipelines can be classified as delayed failures. The immediate failures are generally minimized through the preventative measure and design efforts. For instance, it is shown herein that the puncture probability can be calculated through the comparison between the likelihood of any given external load being imposed and inherent pipe resistance. While preventative measures serve to reduce the occurrences of delayed failures as well as the occurrences of immediate failures, delayed failures are largely mitigated through in-line inspection and timely remediation actions. The fact that the assessment methods for mechanical damage are generally not as robust as those for cracks and corrosion tends to limit the reliability of deterministic calculations of response times. Therefore, in the study described herein, risk-based approaches to minimizing delayed failures were developed. Three different approaches to deciding which dents need to be excavated after an ILI were pursued. One involves the use of reportable incident rates based on the PHMSA statistics in conjunction with the number of ILI dent indications per mile to get a probability of failure. The second consists of a decision-making process based on the ILI-reported dent depths and the dent fatigue life probability-of-exceedance function. The third relates to a decision-making process based on successive excavations of dents located by ILI, in which the Bayesian method is applied to compare predicted versus actual severity and thereby determine the probability of failure associated with stopping after a specific number of excavations.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".