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Record W1146970197 · doi:10.3233/jgs-13007

A new framework for analysis of laterally loaded piles

2013· article· en· W1146970197 on OpenAlexaff
Dipanjan Basu, Rodrigo Salgado, Mônica Prezzi

Bibliographic record

VenueJournal of Geo-Engineering Sciences · 2013
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Mechanics
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsPileDeflection (physics)ComputationFinite element methodDisplacement fieldGeotechnical engineeringFinite differenceDisplacement (psychology)GeologyStructural engineeringMathematicsEngineeringMathematical analysisPhysicsClassical mechanicsAlgorithm

Abstract

fetched live from OpenAlex

A new analysis framework is presented for calculation of the response of laterally loaded piles in multi-layered, heterogeneous elastic soil. The governing differential equations for the pile deflections in different soil layers are obtained using the principle of minimum potential energy after assuming a rational soil displacement field. Solutions for the pile deflection are obtained analytically, while those for the soil displacements are obtained using the finite difference method. The input parameters needed for the analysis are the pile geometry, soil profile and the elastic constants of the soil and pile. The method produces results with accuracy comparable to that of a three-dimensional finite element analysis but requires much less computation time. The analysis can take into account the spatial variation of soil properties along vertical, radial and tangential directions.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.014
GPT teacher head0.234
Teacher spread0.220 · 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 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

Citations9
Published2013
Admission routes1
Has abstractyes

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