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Record W2800271434 · doi:10.1139/cgj-2017-0644

Evaluation of vertical effective stress and pile lateral capacities considering scour-hole dimensions

2018· article· en· W2800271434 on OpenAlexafffundvenue
Cheng Lin, Randall Wu

Bibliographic record

VenueCanadian Geotechnical Journal · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Sediment Transport Processes
Canadian institutionsUniversity of Victoria
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPileGeotechnical engineeringStress (linguistics)Parametric statisticsEffective stressGeologyRange (aeronautics)Overburden pressureStructural engineeringEngineeringMathematics

Abstract

fetched live from OpenAlex

Determination of vertical effective stress along piles is an essential part of calculation of both pile axial and lateral capacities under scour conditions. However, the current design manuals including those from the US Federal Highway Administration (FHWA) and American Petroleum Institute (API) recommend different methods for calculating vertical effective stress. Moreover, they are effective only for restricted scour-hole dimensions. This study presents an improved closed-form solution that allows estimation of the vertical effective stress for a wide range of scour-hole dimensions including scour depth, width, and slope angle. Using the improved analytical solution for stress, API p–y curves for sand were modified to compute pile lateral capacity at different scour-hole conditions. Based on a series of parametric analyses for laterally loaded piles in sand, errors of calculation using the existing methods were quantified and a simplified method was proposed for practical applications. Effects of different scour-hole dimensions on both vertical effective stress and pile lateral capacity were also discussed.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
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 head0.016
GPT teacher head0.243
Teacher spread0.227 · 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 designBench or experimental
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

Citations44
Published2018
Admission routes3
Has abstractyes

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Same venueCanadian Geotechnical JournalSame topicHydrology and Sediment Transport ProcessesFrench-language works237,207