Formal reliability analysis of oil and gas pipelines
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Depending on the operational environment, installation location, and aging of oil and gas pipelines, they are subject to various degradation mechanisms, such as cracking, corrosion, leaking, and thinning of the pipeline walls. Failure of oil and gas pipelines due to these degradation mechanisms can lead to catastrophic events, which, in the worst case, may result in the loss of human lives and huge financial losses. Traditionally, paper-and-pencil proof methods and Monte Carlo based computer simulations are used in the reliability analysis of oil and gas pipelines to identify potential threats and thus avoid unwanted failures. However, paper-and-pencil proof methods are prone to human error, especially when dealing with large systems, while simulation techniques primarily involve sampling-based methods, i.e., not all possible scenarios of the given systems are tested, which compromises the accuracy of the results. As an accurate alternative, we propose to use a higher-order-logic theorem proving for the reliability analysis of oil and gas pipelines. In particular, this paper presents the higher-order-logic formalization of commonly used reliability block diagrams (RBDs), such as series, parallel, series–parallel, and k-out-of- n, and provides an approach to utilize these formalized RBDs to assess the reliability of oil and gas pipelines. For illustration, we present a formal reliability analysis of a pipeline transportation subsystem used between the oil terminals at the Port of Gdynia, Poland, and Dębogórze.
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.
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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 it