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Record W2553010700 · doi:10.1115/ipc2016-64040

Risk-Based Mitigation of Mechanical Damage

2016· article· en· W2553010700 on OpenAlex

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 institutionsDesjardins
Fundersnot available
KeywordsPipeline transportHazardous wasteForensic engineeringLeakPipeline (software)CorrosionEngineeringMaterials science

Abstract

fetched live from OpenAlex

According to the PHMSA data on reportable incidents, for the 20 years ranging from 1995 to 2014, excavation damage accounted for 16.4% of the incidents on 301,732 miles of gas transmission pipelines and 15.6% of the incidents on 199,210 miles of hazardous liquid pipelines. On the whole, excavation damage is a major cause of incidents, ranking third following incidents caused by material/weld/equipment failure and corrosion. For the purposes of this study, mechanical damage is separated into two categories, i.e. immediate failures and delayed failures. An immediate failure is one which occurs at the instant the damage is done to the pipeline. A puncture, for example, is an immediate failure. Delayed failures involve damage that is not sufficient to cause a leak or a rupture at the time it is inflicted. On average, 14.6% of the mechanical damage incidents in gas transmission pipelines and 13.3% of the mechanical damage incidents in hazardous liquid pipelines can be classified as delayed failures. The immediate failures are generally minimized through the preventative measure and design efforts. For instance, it is shown herein that the puncture probability can be calculated through the comparison between the likelihood of any given external load being imposed and inherent pipe resistance. While preventative measures serve to reduce the occurrences of delayed failures as well as the occurrences of immediate failures, delayed failures are largely mitigated through in-line inspection and timely remediation actions. The fact that the assessment methods for mechanical damage are generally not as robust as those for cracks and corrosion tends to limit the reliability of deterministic calculations of response times. Therefore, in the study described herein, risk-based approaches to minimizing delayed failures were developed. Three different approaches to deciding which dents need to be excavated after an ILI were pursued. One involves the use of reportable incident rates based on the PHMSA statistics in conjunction with the number of ILI dent indications per mile to get a probability of failure. The second consists of a decision-making process based on the ILI-reported dent depths and the dent fatigue life probability-of-exceedance function. The third relates to a decision-making process based on successive excavations of dents located by ILI, in which the Bayesian method is applied to compare predicted versus actual severity and thereby determine the probability of failure associated with stopping after a specific number of excavations.

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 categoriesnone
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 score0.089
Threshold uncertainty score0.912

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 head0.007
GPT teacher head0.209
Teacher spread0.202 · 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