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Record W4405361163 · doi:10.1115/ipc2024-133623

Advanced Assessment of Pipeline Stress-Relief

2024· article· en· W4405361163 on OpenAlexaff
Ali Fathi, Onyekachi Ndubuaku, Matthew Fowler, Nader Yoosef‐Ghodsi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMaterials Science
TopicMaterial Properties and Failure Mechanisms
Canadian institutionsStantec (Canada)
Fundersnot available
KeywordsPipeline (software)Stress (linguistics)Stress reliefComputer scienceMaterials scienceOperating systemComposite material

Abstract

fetched live from OpenAlex

Abstract Pipelines traversing landslides can be subjected to forces due to ground displacement. Stress-relief projects are usually planned before strain in the pipe, or a girth weld reaches one of the strain capacities associated with a limit state event using a certain safety margin. Accurate estimation of the pipe strain growth due to the slide movement is a key input for planning stress-relief projects. Through presenting a case study, this paper introduces the concept of an advanced stress-relief assessment approach. This method uses finite element simulation of pipe-soil interaction, to estimate the evolution of strain before stress-relief, pipe rebound as a result of stress-relief, and extension of the asset life after the backfill is completed. Using this modeling, the effects of additional improvements that can be added to a stress-relief project can also be estimated properly. These measures can enhance the performance of the pipe by increasing its strain capacity or reducing the impact of the slide movement. The primary benefit of completing such assessment is to be able to optimize the timing of stress-relief projects. Given that the strain growth rate usually accelerates as more strain accumulates in the pipe, timely stress-relief can prevent excessive plastic deformations and consequently increase the benefit of stress-relief projects. This is especially important for lines, for which pipe replacement is much more expensive than a simple stress-relief (without cutouts). The next step in this assessment is to use the output of this model for different scenarios of stress-relief in a risk assessment. This risk assessment provided an opportunity to compare the stress-relief option with horizontal directional drilling with a completely different cost/performance combination. The result of this assessment showed that if planned and executed as recommended, the stress-relief option can result in risk reduction below the risk target with significantly lower cost and complexity.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.010
GPT teacher head0.271
Teacher spread0.261 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreMethods

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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