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Record W4405360488 · doi:10.1115/ipc2024-133083

Application of a Newly Standardized Risk-Based Pressure Design Approach – Implications From Pilot Studies

2024· article· en· W4405360488 on OpenAlexaffabout
Lowell McAllister, Aiden Svitich, Riski Adianto, Cory Wiechnik, Emeka Ezeiruaku, Dongliang Lu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldHealth Professions
TopicWorkplace Health and Well-being
Canadian institutionsPetroleum Technology Alliance Canada
Fundersnot available
KeywordsComputer scienceRisk analysis (engineering)Reliability engineeringEngineeringBusiness

Abstract

fetched live from OpenAlex

Abstract A risk-based pressure design approach has been developed as an alternative to the class location approach currently used in the Canadian Standards Association’s (CSA’s) Standard Z662. This new approach was adopted in the non-mandatory Annex C of the 2023 edition of CSA Z662. The risk-based design approach uses a set of hoop stress factors to calculate the minimum wall thickness from the pipe’s pressure, diameter, and specified minimum yield strength. The hoop stress factors are calibrated to meet reliability targets based on undamaged pipe burst and third-party mechanical damage failure limit states. These reliability targets are defined for a set of consequence-based safety classes to achieve a consistent, broadly acceptable, level of risk for all pipelines. Since the approach was adopted in CSA Z662 in 2023, several pilot studies, as described in this paper, have applied it to segments of high-pressure transmission pipelines in Canada. While this approach can also apply to new designs, the pilot studies described in this paper consider scenarios in which existing designs must be re-evaluated, including for class location changes due to population encroachment and the construction of road crossings. This paper describes the results of these pilot studies and demonstrates the safety and financial implications of adopting the new risk-based pressure design approach at such sites that need re-evaluating. This is done by comparing the design requirements under the new risk-based approach, such as pressure and wall thickness, with the requirements of the existing class location approach. In some cases, the pilot studies demonstrate that the new risk-based approach can reduce over-conservatism with less-restrictive design requirements, resulting in a positive financial benefit while maintaining broadly acceptable levels of safety. The benefit of using alternative mitigation options, such as mechanical damage prevention measures, is also explored in some of the pilot studies.

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)0.022
metaresearch head score (Gemma)0.026
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.114

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0220.026
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0030.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.086
GPT teacher head0.426
Teacher spread0.340 · 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 designObservational
Domainnot available
GenreEmpirical

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 routes2
Has abstractyes

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