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Record W2511462219 · doi:10.1139/cgj-2016-0056

Model uncertainty in uplift resistance calculations for sandy backfills

2016· article· en· W2511462219 on OpenAlexvenueno aff
Bruno Stuyts, David Cathie, T. Powell

Bibliographic record

VenueCanadian Geotechnical Journal · 2016
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsnot available
Fundersnot available
KeywordsGeotechnical engineeringSubmarine pipelineGeologyProbabilistic logicPipeline transportResistance (ecology)Pipeline (software)Engineering

Abstract

fetched live from OpenAlex

Trenching and backfilling is one of the most practical and cost-effective methods for protection and stabilization of offshore pipelines. Defining the geotechnical properties of backfill material resulting from mechanical backfilling or jet trenching is an area of substantial uncertainty and the resistance against pipeline uplift provided by these backfills needs to be characterized accounting for these uncertainties. This paper compares the properties of sandy backfill material and the available calculation models for uplift resistance against a database of more than 300 controlled pipeline uplift tests. The model uncertainty for uplift resistance calculations is derived from a back-analysis of the uplift tests. The uncertainties on uplift resistance and mobilization distance are correlated and characterized using a joint probability distribution. The selected distributions are applied to an example uplift resistance problem. When compared against this probabilistic formulation, the factors applied to uplift resistance in pipeline analysis can be refined to lead to a more cost-effective solution.

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)0.000
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.969
Threshold uncertainty score0.997

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)0.0000.000

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 head0.012
GPT teacher head0.215
Teacher spread0.203 · 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

Citations8
Published2016
Admission routes1
Has abstractyes

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