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Record W2918380999 · doi:10.1139/cgj-2018-0503

In situ stress states and lateral deformations of soil–bentonite cutoff walls during consolidation process

2019· article· en· W2918380999 on OpenAlexvenueno aff
Xing Tong, Yuchao Li, Han Ke, Ying Li, Qian Pan

Bibliographic record

VenueCanadian Geotechnical Journal · 2019
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Stabilization
Canadian institutionsnot available
FundersNational Science Foundation
KeywordsConsolidation (business)Geotechnical engineeringCutoffGeologyPore water pressureBentoniteIsotropyLateral earth pressureStress (linguistics)Lateral strainEffective stress

Abstract

fetched live from OpenAlex

A long-term in situ test was performed on two soil–bentonite (SB) cutoff walls with different dimensions. The total stresses and pore pressures in the walls were monitored for 8 months, and the lateral wall deformations were monitored for 15 months. The monitoring results revealed that the primary consolidation of the presented SB cutoff walls took approximately 8 months. In the first 5 days, the stress states of the walls were nearly isotropic. The total stresses were less than the geostatic stress, and the lateral wall deformations were negligible. As the consolidation developed, the total horizontal stresses in the walls decreased and then remained unchanged at most depths after 1 month, while the stresses at the bottoms continued decreasing. The nonlinear profiles of the horizontal effective stresses in the cutoff walls were similar to the active earth pressure distributions behind the retaining walls moving in translation mode. The trench sidewall movements were found in translation mode within a specific depth range. Obvious dependencies of the horizontal effective stress and the horizontal strain on the wall height were observed. Comparison with the literature illustrates the influence of backfill properties on the consolidation behavior of the SB cutoff walls.

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 score0.059
Threshold uncertainty score0.478

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.003
GPT teacher head0.189
Teacher spread0.185 · 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

Citations23
Published2019
Admission routes1
Has abstractyes

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