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Record W4409963027 · doi:10.1080/30656680.2025.2480868

Response and failure mode of buried pipeline crossing different fault types

2025· article· en· W4409963027 on OpenAlexaff
Junyan Han, Yansong Bi, Benwei Hou, M. Hesham El Naggar, Chengshun Xu, Xiuli Du

Bibliographic record

VenueUrban Resilience and Earthquake Engineering · 2025
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsWestern University
FundersNational Key Research and Development Program of ChinaNational Natural Science Foundation of China
KeywordsPipeline (software)Failure mode and effects analysisMode (computer interface)Fault (geology)GeologyComputer scienceReliability engineeringSeismologyEngineeringProgramming language

Abstract

fetched live from OpenAlex

Strong earthquakes' Permanent Ground Displacement (PGD) can make buried pipelines fail. Thus, analysing the seismic response and failure mode of fault-crossing pipelines, and evaluating their resistance to PGD from different fault types is crucial. This paper sets up 3D finite-element models of continuous steel and spigot ductile iron pipelines crossing reverse, normal, and strike faults. It assesses pipeline seismic resistance by critical fault displacement and explores failure mechanisms. The results show that for steel pipelines, local buckling is the main failure mode under the three fault types, yet buckling locations and peak compressions vary. Also, cross-sectional deformation lags. Ductile iron pipelines fail at joints for all fault types, with different specific failure modes. Their anti-fault capacity is lower than that of steel pipelines. Both types are more likely to fail under reverse fault movement at a 60° fault dip and 90° fault-pipeline angle, increasing the risk of pipeline functionality loss.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.003
GPT teacher head0.199
Teacher spread0.196 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations2
Published2025
Admission routes1
Has abstractyes

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