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Record W4412130908 · doi:10.55274/r0000142

PR676-233801-R09 Pipe Vulnerability Analysis Methodology

2025· report· en· W4412130908 on OpenAlexaff
Kachi Ndubuaka

Bibliographic record

Venuenot available
Typereport
Languageen
FieldEngineering
TopicMechanical Failure Analysis and Simulation
Canadian institutionsStantec (Canada)
Fundersnot available
KeywordsVulnerability (computing)Vulnerability assessmentComputer scienceEnvironmental scienceComputer securityPsychology

Abstract

fetched live from OpenAlex

Continuous buried pipelines are generally required to traverse long distances and as such, inevitably pass through unstable landforms which may impart critical levels of stress beyond the intrinsic capacity of the pipelines. The deformational response of buried pipelines subject to relative ground movement is strongly influenced by the geotechnical properties of the immediate backfill and surrounding soil, the size and direction of the soil movement in which the pipeline is embedded, as well as mechanical soil-pipe interaction behavior. With respect to the mode of deformation, relative ground movement may be classified as transient ground deformation (TGD) or permanent ground deformation (PGD). Studies show that pipelines are more susceptible to brittle fracture due to cyclic loading when subjected to TGD whereas, under PGD, they tend to exhibit a more ductile response. The orientation of the moving soil mass relative to the pipe axis is a major factor that determines the deformational response of a buried pipeline under PGD: transverse and oblique PGD tend to induce predominantly bending strains while longitudinal PGD is more likely to induce axial tension and compression close to the margins of the PGD zone. Ductile behavior in metallic pipelines is characterized by a definitive yield point in the stress-strain curve beyond which the pipe material experiences a significant loss of stiffness (i.e., significantly less loading capacity relative to material deformation); hence, strain-based design and assessment (SBDA) methodology is well suited to account for geohazard-related failure mechanisms in pipelines subjected to landslides. Pipeline vulnerability evaluation relies on the SBDA methodology as it represents the limit state function which describes the relationship between the strain demand on the pipeline and the strain capacity of the pipeline. The applicability of the vulnerability analysis method presented in this report is limited to strain demand in the tensile region of the pipe; where non-bending strains are more likely to initiate cracking. Finite element models of seven real-world cases were simulated using a commercial finite element analysis (FEA) software package, ABAQUS CAE (version 2019), and the resultant axial strains in the tensile zones of the models were compared to the strain demand predicted using the analytical methods outlined in this report. The results showed reasonable correlation between the FEA results and the analytically predicted strain demand. Appropriate implementation of the vulnerability analysis methodology presented herein requires subject matter and state of the art familiarity to determine the appropriate margin of safety to account for differences between real word soil-pipe interaction and the simplified assumptions inherent in the strain demand models.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.774
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.097
GPT teacher head0.365
Teacher spread0.268 · 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 designSimulation or modeling
Domainnot available
GenreMethods

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
Published2025
Admission routes1
Has abstractyes

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