A Life-Cycle Approach to the Assessment of Pipeline Dents
Bibliographic record
Abstract
The application of in-line inspection (ILI) to assess pipelines for various anomalies is standard practice in the pipeline industry. When ILI data identifies the presence of anomalies such as denting or ovalization, current convention is to perform either a depth-based or strain-based assessment to assess the severity. Although a strain-based methodology is generally accepted in the pipeline industry, this approach does not address all of the primary damage mechanisms associated with pipeline dents. Assessment based upon either depth or strain alone may not only provide non-conservative results but also fail to properly rank dents in order of their true severity. A life-cycle assessment approach that considers the damage caused by the dent formation, the stress intensification effect of the dent profile, and the severity of future pressure cycling provides an improved understanding of the probability of failure, allowing for more informed integrity management decision making. Strain-based assessment of dents in pipelines is typically performed by calculating the local curvatures in the dent geometry as measured by ILI. Local strains are then calculated based on these local curvatures. However, this approach does not address that once a dent has been formed, continued pressure cycling at that location is what will ultimately cause a failure. The current strain-based methodology does not account for the severity of the pressure cycling at the dent. A new and innovative methodology has been developed which takes a life-cycle approach to the assessment of pipeline dents. This approach estimates the remaining life of a dent based on fatigue damage accumulation. Finite element analysis (FEA) is used to calculate various stress concentration factors (SCFs) based on the geometry of the dent. These SCFs are used to calculate an equivalent alternating stress for a unit pressure cycle event. Past representative pressure cycling data is gathered using a rainflow counting approach. The amount of damage accumulated during each pressure cycle is calculated using stress or strain based (S-N) fatigue curves; this allows for a damage rate to be calculated based on past operational history. A remaining life can be estimated based on this damage rate and an estimation of the initial fatigue damage accumulated during formation of the dent. This estimation is made based on previous elastic-plastic FEA of various scenarios which simulate the formation and shakedown of a pipeline dent. Case studies which explore the use of different assessment methods to analyze dents will be presented. A comparison of different assessment methodologies will illustrate the improved understanding of the probability of failure of dents based upon the life-cycle assessment.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".