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Record W2554195067 · doi:10.1115/ipc2016-64513

Integrity Management of Ground Movement Hazards

2016· article· en· W2554195067 on OpenAlex
Yong-Yi Wang, Don West, Douglas Dewar, Alex Mckenzie-Johnson, Millan Sen

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicStructural Integrity and Reliability Analysis
Canadian institutionsSpectra Energy (Canada)
Fundersnot available
KeywordsProcess (computing)Computer scienceRisk analysis (engineering)Event (particle physics)Pipeline transportLandslidePipeline (software)Forensic engineeringEngineeringBusiness

Abstract

fetched live from OpenAlex

Ground movements, such as landslides and subsidence/settlement, can pose serious threats to pipeline integrity. The consequence of these incidents can be severe. In the absence of systematic integrity management, preventing and predicting incidents related to ground movements can be difficult. A ground movement management program can reduce the potential of those incidents. Some basic concepts and terms relevant to the management of ground movement hazards are introduced first. A ground movement management program may involve a long segment of a pipeline that may have a threat of failure in unknown locations. Identifying such locations and understanding the potential magnitude of the ground movement is often the starting point of a management program. In other cases, management activities may start after an event is known to have occurred. A sample response process is shown to illustrate key considerations and decision points after the evidence of an event is discovered. Such a process can involve fitness-for-service (FFS) assessment when appropriate information is available. The framework and key elements of FFS assessment are explained, including safety factors on strain capacity. The use of FFS assessment is illustrated through the assessment of tensile failure mode. Assessment models are introduced, including key factors affecting the outcome of an assessment. The unique features of girth welds in vintage pipelines are highlighted because the management of such pipelines is a high priority in North America and perhaps in other parts of the worlds. Common practice and appropriate considerations in a pipeline replacement program in areas of potential ground movement are highlighted. It is advisable to replace pipes with pipes of similar strength and stiffness so the strains can be distributed as broadly as possible. The chemical composition of pipe steels and the mechanical properties of the pipes should be such that the possibility of HAZ softening and weld strength undermatching is minimized. In addition, the benefits and cost of using the workmanship flaw acceptance criteria of API 1104 or equivalent standards in making repair and cutout decisions of vintage pipelines should be evaluated against the possible use of FFS assessment procedures. FFS assessment provides a quantifiable performance target which is not available through the workmanship criteria. However, necessary inputs to perform FFS assessment may not be readily available. Ongoing work intended to address some of the gaps is briefly described.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.632
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.012
GPT teacher head0.230
Teacher spread0.218 · 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