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Record W1986999887 · doi:10.1139/t06-038

Settlement analysis of pile groups in layered soils

2006· article· en· W1986999887 on OpenAlexvenueno aff
Roberto Cairo, Enrico Conte

Bibliographic record

VenueCanadian Geotechnical Journal · 2006
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Mechanics
Canadian institutionsnot available
Fundersnot available
KeywordsPileNonlinear systemSettlement (finance)StiffnessGeotechnical engineeringStructural engineeringContext (archaeology)Soil waterGeologyEngineeringComputer sciencePhysics

Abstract

fetched live from OpenAlex

This paper presents a method to perform a nonlinear analysis of pile groups subject to vertical loading. The method makes use of the dynamic stiffness matrices to simulate the response of layered soils. These matrices are incorporated in a calculation procedure that is computationally very efficient because the response of a pile group can be achieved using essentially the solution for a single pile. The method is first used to perform a linear elastic analysis of pile groups and is then extended to include the nonlinearity effects. In this context, the widely accepted approach is adopted in which nonlinearity is considered to be confined in a narrow zone close to each pile, whereas outside this zone the soil is assumed to behave as a linear elastic medium. Moreover, a global interaction factor is introduced to account for the interaction among the piles in the group. The theoretical predictions from the proposed method are compared with experimental measurements from several published full-scale and model tests on pile groups loaded up to failure. The agreement between predicted and observed behaviour is found to be very satisfactory, even approaching the ultimate load, when the results of loading tests on single piles are available and the group efficiency with respect to the failure load is close to unity.Key words: pile groups, settlement analysis, nonlinear behaviour, layered soils.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.100
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.006
GPT teacher head0.184
Teacher spread0.178 · 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
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

Citations33
Published2006
Admission routes1
Has abstractyes

Explore more

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