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Record W4315786255 · doi:10.3390/jmse11010192

Effect of Relative Stiffness of Pile and Soil on Pile Group Effect

2023· article· en· W4315786255 on OpenAlexaff
Bo Liu, Xiaomin Wang, Chunhui Liu, Jingchang Kong

Bibliographic record

VenueJournal of Marine Science and Engineering · 2023
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Mechanics
Canadian institutionsUniversity of Alberta
FundersInstitute of Engineering Mechanics, China Earthquake AdministrationChina Earthquake AdministrationNatural Science Foundation of Heilongjiang Province
KeywordsPileStiffnessBending momentGroup effectGroup (periodic table)Geotechnical engineeringStructural engineeringBending stiffnessGeologyEngineeringPhysics

Abstract

fetched live from OpenAlex

Pile groups are designed to sustain complex loads in various engineering. During the design of a pile group, the obvious pile group effect should be considered for closely spaced pile groups. However, the group effect considered by different scholars varies, which makes it hard for engineers to consider the pile group effect for the design of a pile group. In this study, the finite element (FE) method is proposed to advance our understanding of the variations of pile group effects developed by different researchers, based on the concept of soil–pile relative stiffness. The relationship between soil–pile relative stiffness and normalized lateral load–displacement curves and bending moment profile response of the pile group is investigated. The results show that the pile group effect increases with the increase in soil–pile relative stiffness; the pile group effect increases with the decrease in pile spacing, increases with the increase in of number of piles in the group, and is significantly affected by pile group arrangement as well.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
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
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.004
GPT teacher head0.200
Teacher spread0.196 · 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

Citations10
Published2023
Admission routes1
Has abstractyes

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