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Record W2326755792 · doi:10.1061/41138(386)77

When to Intervene? Using Rates of Failure to Determine the Time to Shut Down Your PCCP Line

2010· article· en· W2326755792 on OpenAlexaff
Mike Wrigglesworth, Michael S. Higgins

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicConcrete Corrosion and Durability
Canadian institutionsBP (Canada)
Fundersnot available
KeywordsPrestressed concretePipeline (software)Mains electricityPipeline transportCorrosionStructural engineeringCrackingWeldingForensic engineeringEngineeringGeotechnical engineeringMaterials scienceMechanical engineeringComposite material

Abstract

fetched live from OpenAlex

Performing structural evaluations of aged civil engineering structures has traditionally been performed on a static basis. The condition of the structure is tested either destructively or non-destructively to determine its present day condition and the strength is evaluated to determine if the structure can safely withstand anticipated loads. One of the problems with managing structures in this fashion is that structures do not behave in a static fashion. This is especially true for large diameter PCCP (Prestressed concrete cylinder pipeline) mains that have wire break damage resulting from corrosion or cracking from hydrogen embrittlement. Given the variability in rate of deterioration in a PCCP main the risk associated with a pipe section changes with time. Pipe sections where risk is deemed to be acceptable may change to a point where risk is not acceptable in a relatively short duration of time. This phenomenon has been observed on multiple pipe sections. This paper examines the rate of deterioration of PCCP mains to evaluate how risk changes or does not change with time and how this information can be used to determine when a pipeline should be repaired.

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 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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.218
Threshold uncertainty score0.997

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)0.0040.000

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 head0.027
GPT teacher head0.267
Teacher spread0.240 · 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 teacher head, not a consensus.

Study designBench or experimental
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

Citations7
Published2010
Admission routes1
Has abstractyes

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