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Record W4392520842 · doi:10.1061/9780784485330.062

Long-Term Stress and Consolidation Behavior of a Soil-Bentonite Slurry Trench Cutoff Wall

2024· article· en· W4392520842 on OpenAlexaff
Jeffrey Evans, Daniel Ruffing, Landon C. Barlow, Nathan Coughenour

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Stabilization
Canadian institutionsKensington Health
Fundersnot available
KeywordsSlurryConsolidation (business)BentoniteCutoffGeotechnical engineeringTrenchMaterials scienceTerm (time)GeologyComposite materialPhysics

Abstract

fetched live from OpenAlex

Soil-bentonite slurry trench cutoff walls have been used for over 50 years to control groundwater flow and contaminant transport. During the summer of 2016, a 200-m-long and 0.9-m-wide full-scale, instrumented soil-bentonite slurry trench cutoff wall was constructed to a depth of 6.2 to 7.1 m. Primary research goals for the wall included evaluation of (1) the anisotropic stresses in the backfill, (2) stress-dependency of the backfill hydraulic conductivity, and (3) the rate of consolidation. These phenomena are time dependent and affected by the construction and post construction movement, including excavation, backfilling, backfill consolidation, and long-term backfill secondary compression (creep). This paper presents the results of six years of monitoring of earth pressure cells and pore pressure sensors. After six years, effective stresses within the wall remain far below geostatic. The maximum stresses observed are transverse to the axis of the trench and attributed to lateral deformations (i.e., inward movement of the sidewalls). The lowest stresses observed are the vertical stresses, reflecting the impact of arching or side wall friction as the soft backfill moves downward within the trench. Longitudinal horizontal stresses are intermediate between the transverse horizontal stresses and vertical stresses. For the last five years, the principal phenomenon controlling changes in stress has been secondary compression (creep).

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 score0.138
Threshold uncertainty score0.368

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 head0.008
GPT teacher head0.226
Teacher spread0.218 · 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

Citations7
Published2024
Admission routes1
Has abstractyes

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