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Record W2605314958 · doi:10.1139/cgj-2016-0019

Seismic assessment of sheet pile reinforcement effect on river embankments constructed on a soft foundation ground including soft estuarine clay

2017· article· en· W2605314958 on OpenAlexvenueno aff
Kentaro Nakai, Toshihiro Noda, Kenta Kato

Bibliographic record

VenueCanadian Geotechnical Journal · 2017
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Stabilization
Canadian institutionsnot available
FundersJapan Society for the Promotion of Science
KeywordsGeotechnical engineeringPileGeologyPenetration testFoundation (evidence)LiquefactionLeveeSheet pileSubgrade

Abstract

fetched live from OpenAlex

Conventional seismic assessments of river embankments have focused on liquefaction damage of sandy ground. However, following the 2011 earthquake centered off the Pacific Coast of Tohoku, extensive damage of river embankments built on a clayey foundation has garnered greater attention. This paper presents seismic response analyses of river embankments constructed on soft and sensitive estuarine clay, as well as analyses of countermeasures implemented with sheet piles with a succession of penetration depth and placement. River embankments have kept stable if the clayey ground was assumed to be a nonsensitive condition. However, if a strong shake impacts actual sensitive clay, there is a risk of slippage generated from the clayey layer. If the penetration depth of piles is shallow in the clayey layer, the clay is strongly disturbed by the tip of the pile, generating extensive damage; if the penetration depth is sufficiently deep in the supporting layer, significant deformation control may be obtained at the reinforced side, although there is a risk of promoting deformation at the opposite side. These results indicate the importance of the specific inspection of pile penetration depth; otherwise, not only is it not possible to obtain a sufficient countermeasure effect, but also an adverse effect may be generated.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.141
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.014
GPT teacher head0.257
Teacher spread0.243 · 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.

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

Citations14
Published2017
Admission routes1
Has abstractyes

Explore more

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