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Record W3164570601 · doi:10.1061/9780784483411.032

Cone Penetration and Dilatometer Tests to Evaluate Stresses in a Soil-Bentonite Slurry Trench Cutoff Wall

2021· article· en· W3164570601 on OpenAlexaff
Jeffrey Evans, Daniel Ruffing, Timothy Guider, Benjamin Summers

Bibliographic record

VenueIFCEE 2021 · 2021
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsKensington Health
Fundersnot available
KeywordsDilatometerSlurryGeotechnical engineeringTrenchBentoniteLeveeHydraulic conductivityStress (linguistics)Materials scienceCutoffPenetration testGeologyComposite materialSoil waterSoil science

Abstract

fetched live from OpenAlex

Soil bentonite (SB) slurry trench cutoff walls have been widely used for over 40 years to control groundwater flow, seepage through dams and levees, and contaminant transport. The hydraulic conductivity of SB backfill is stress dependent, and the concept that the state-of-stress in SB slurry trench cutoff walls is less than geostatic was first published over 30 years ago. A full-scale, instrumented SB slurry trench cutoff wall was constructed with embedded instrumentation that directly measures the stress and pore pressure. In addition, cone penetration (CPT) and Marchetti dilatometer (DMT) tests were performed in the wall after the wall was allowed to sit for one year. The combination of direct stress measurements and in situ tests presents a unique opportunity to compare results from CPT and DMT tests to the direct measurements of stress in the wall and to predictions from available models. Finally, the authors provide summary opinions regarding cone and dilatometer testing for predicting stress in low strength materials along with practical recommendations for the design and construction of SB 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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.009
GPT teacher head0.236
Teacher spread0.227 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations3
Published2021
Admission routes1
Has abstractyes

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Same venueIFCEE 2021Same topicGeotechnical Engineering and Underground StructuresFrench-language works237,207