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Record W3048587360 · doi:10.1201/9781003077480-36

A statistically-based approach for the design of HDD crossings

2020· book-chapter· en· W3048587360 on OpenAlexaboutno aff
M.E. Baumert, E. N. Allouche

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldComputer Science
TopicVLSI and Analog Circuit Testing
Canadian institutionsnot available
Fundersnot available
KeywordsComputer science

Abstract

fetched live from OpenAlex

A review of current design practices for calculating tensile loading during horizontal directional drilling (HDD) installations is provided. These design procedures vary in the ways they treat the basic installation loads such as borehole profile, pipe’s stiffness, frictional drag and fluidic drag. Summarized field data from a recent HDD installation, a 400m double crossing of the Grand River conducted near Brantford, Ontario, is presented. Using the actual profile and parameters of this installation, three representative design approaches are applied and are shown to yield different estimations of the tensile load distribution along the length of the pipe product. A statistical design approach using Monte Carlo simulation is presented where discrete input design parameters are replaced with distributed variables. The potential of the statistical design approach as a tool to aid the HDD designer is demonstrated by applying the simulation technique to produce a CDF plot of the maximum tensile load for a 508mm diameter siphon installed across the Grand River.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.950
Threshold uncertainty score0.554

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 science0.0010.000
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 head0.088
GPT teacher head0.254
Teacher spread0.166 · 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 designTheoretical or conceptual
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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