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Record W4388493951 · doi:10.1061/jpsea2.pseng-1509

Probabilistic Analysis of Pipelines in Geohazard Zones Using a Novel Approach for Strain Calculation

2023· article· en· W4388493951 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

VenueJournal of Pipeline Systems Engineering and Practice · 2023
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsAlberta Environment and Protected AreasUniversity of Alberta
Fundersnot available
KeywordsGeohazardPipeline transportConditional probabilityProbabilistic logicStructural engineeringLandslideGeotechnical engineeringPipeline (software)Monte Carlo methodEngineeringComputer scienceMathematicsStatisticsMechanical engineering

Abstract

fetched live from OpenAlex

This paper presents an approach for probabilistic analysis of pipelines buried through geohazard-prone areas that induce permanent ground movements potentially. In this approach, an easy-to-implement response prediction tool based on the finite-difference method is integrated with simple but robust Monte Carlo simulation methods. The probability of strain capacity exceedance is calculated when a pipeline is subjected to the ground movement of different magnitudes. In the strain-based limit state function, the strain capacity is determined using existing equations in the literature, and the strain demand is calculated using an accurate and efficient tool based on the finite-difference method. After obtaining the conditional probabilities of failure of pipes at given magnitudes of ground movement, the probability of failure of pipes as a function of time is also calculated considering the probability of ground movement initiation. The proposed approach is demonstrated through a case study of pipelines subjected to landslides.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.878
Threshold uncertainty score0.674

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.032
GPT teacher head0.280
Teacher spread0.248 · 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