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Record W2576769344 · doi:10.1139/cgj-2016-0162

Lateral spreading near deep foundations and influence of soil permeability

2017· article· en· W2576769344 on OpenAlexvenueno aff
Liangcai He, J. M. Ramírez, Jinchi Lu, Liang Tang, Ahmed Elgamal, Kohji Tokimatsu

Bibliographic record

VenueCanadian Geotechnical Journal · 2017
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsnot available
Fundersnot available
KeywordsGeotechnical engineeringGeologyLiquefactionPermeability (electromagnetism)Pore water pressurePileSiltEarthquake shaking tableGeomorphology

Abstract

fetched live from OpenAlex

A three-dimensional finite element (FE) model is calibrated based on a large-scale (1g) shake-table experiment. In this experiment, single piles were subjected to liquefaction-induced lateral spreading. The testing configuration, experimental results, and FE framework are presented and discussed. The presence of piles in this fully saturated ground model caused a significant reduction in the extent of accumulated lateral soil deformation. In this regard, high shear strains, additional to those in the free field, occur as the soil moves around the piles in the downslope direction. The associated shear-induced tendency for dilation increases the effective confinement, and reduces the resulting downslope deformations. As such, an FE parametric study is undertaken to investigate the effect of soil permeability on this observed liquefaction-induced lateral response. As the prescribed soil permeability increased (in the silt–sand range), higher levels of ground lateral deformation occured, albeit with a lower pile head displacement and lateral load. Eventually, high permeability (in the gravels range) precluded the accumulation of significant excess pore pressure, with low levels of both soil and pile lateral displacement. On this basis, permeability is highlighted as a critical potentially primary parameter in dictating the effects of liquefaction-induced lateral load on embedded foundation systems.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.510
Threshold uncertainty score0.952

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 studies0.0010.000
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 head0.008
GPT teacher head0.216
Teacher spread0.208 · 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

Citations32
Published2017
Admission routes1
Has abstractyes

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