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Record W3047995685 · doi:10.1061/9780784483206.063

Simplified Application of the Delft Method to Estimate Maximum Allowable Annular Pressure in HDD

2020· article· en· W3047995685 on OpenAlexaff
In-Shik Park, Alireza Bayat

Bibliographic record

VenuePipelines 2020 · 2020
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsGeotechnical engineeringMargin (machine learning)EngineeringStandard penetration testStructural engineeringMathematicsGeologyComputer scienceMachine learning

Abstract

fetched live from OpenAlex

The Delft method has been broadly used for inadvertent return (IR) assessment in the horizontal directional drilling (HDD) industry. Prediction of the maximum allowable mud pressure, Pmax, using the Delft equation requires multiple geotechnical parameters as inputs; however, this often poses challenges for practitioners. Since there is no apparent guideline established for the extraction of geotechnical parameters from site investigation data, the determination of Delft equation inputs becomes subjective, which ultimately results in the Pmax prediction being unsystematic. Instead of converting site investigation data—such as the N-value from the standard penetration test (SPT)—into laboratory-based geotechnical parameters, a direct method of determination of Pmax from the N-value is considered to be preferable. In this paper, a guideline for predicting Pmax using the N-value is presented. Multiple correlations between N-values and geotechnical parameters have been evaluated, and these are introduced into Delft equation using a conservative margin of error.

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: Methods · Consensus signal: none
Teacher disagreement score0.967
Threshold uncertainty score0.435

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 head0.008
GPT teacher head0.251
Teacher spread0.244 · 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
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
Published2020
Admission routes1
Has abstractyes

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