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Record W4318814290 · doi:10.3390/geosciences13020047

Assessment of the Response of Trenched–Backfilled Pipelines to Strike-Slip Faults: An Analytical Approach

2023· article· en· W4318814290 on OpenAlexafffund
Mozhgan Asgarihajifirouz, Xiaoyu Dong, Hodjat Shiri

Bibliographic record

VenueGeosciences · 2023
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsMemorial University of Newfoundland
FundersNatural Sciences and Engineering Research Council of CanadaMemorial University of Newfoundland
KeywordsTrenchPipeline transportGeotechnical engineeringDeflection (physics)GeologySlip (aerodynamics)EngineeringMining engineeringMaterials science

Abstract

fetched live from OpenAlex

Trenched pipelines may experience significant lateral displacement due to natural geohazards such as strike slip-fault movements, landslides, etc. Using pre-excavated soil to backfill trenches is a cost-effective option to protect pipelines against large deformations. These backfilling materials are heavily remolded and therefore softer than the native ground. Therefore, the shear strength difference between the backfill and native ground may affect the pipeline–backfill–trench interaction and the failure mechanism of the surrounding soil. By assuming a simplified uniform soil domain, the influence of softer pre-excavated backfilling material on the pipeline–backfill–trench interaction is neglected in the analytical methods that are usually used in the structural health monitoring of buried pipelines. In this study, the effects of trenching and backfilling were incorporated into an analytical solution for a fast assessment of the pipeline response at the early stages of engineering design projects and structural health monitoring. In comparison with other methods, this methodology provides a convenient and efficient method for computing pipeline strain and deflection curves in geohazardous regions.

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)0.001
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.312
Threshold uncertainty score0.289

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.021
GPT teacher head0.284
Teacher spread0.263 · 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.

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
Published2023
Admission routes2
Has abstractyes

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