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Record W2969517029 · doi:10.1139/cgj-2020-0663

Meaning, measurement, and field application of fully softened shear strength of stiff clays and clay shales

2021· article· en· W2969517029 on OpenAlexvenueno aff
G. Mesri, Cai Wang, Thierno Kane

Bibliographic record

VenueCanadian Geotechnical Journal · 2021
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsGeotechnical engineeringGeologySlip (aerodynamics)Shear (geology)Shear strength (soil)Soil waterEngineering

Abstract

fetched live from OpenAlex

Fully softened shear strength mobilized in first-time slope failures, introduced by Skempton in 1970, corresponds to a random edge–face arrangement and interaction of clay particles in an entirely destructured fabric of stiff clays and clay shales. A series of triaxial compression tests was conducted on reconstituted normally consolidated specimens of 15 stiff clay and clay shale compositions. Based on the laboratory results an empirical correlation for secant fully softened friction angle, [Formula: see text], was developed for clay compositions with plasticity index in the range of 10%–250%, in effective normal stress range of 10–700 kPa. The laboratory measurements confirm an empirical equation for fully softened shear strength in terms of parameters [Formula: see text] and m fs . The field application of secant fully softened friction angle was examined by stability analyses of 63 first-time slope failures in 38 geologic materials. These include 45 slope failures with a segment of observed slip surface at residual condition and the back-scarp mobilizing fully softened shear strength, and 18 slope failures with entire observed slip surface at fully softened condition. The back-calculated fully softened secant friction angles for first-time slope failures are in good agreement with [Formula: see text] correlation based on laboratory tests.

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: none
Teacher disagreement score0.748
Threshold uncertainty score0.497

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 head0.009
GPT teacher head0.190
Teacher spread0.181 · 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

Citations4
Published2021
Admission routes1
Has abstractyes

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