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Revisiting the Fundamental Concepts of Soil Mechanics Using the General Effective Stress Concept for Saturated Geomaterials

2022· article· en· W4307891221 on OpenAlexaff
Zhouxiang Ding, Wenjun Zhang, Zhe Wang, Zhenhao Zhang, Zhaohui Yang, Shanyong Wang

Bibliographic record

VenueInternational Journal of Geomechanics · 2022
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsTerzaghi's principleGeotechnical engineeringEffective stressGeomechanicsCauchy stress tensorSoil mechanicsPore water pressureContext (archaeology)Permeability (electromagnetism)MathematicsMechanicsSoil waterGeologyMathematical analysisPhysicsSoil scienceChemistry

Abstract

fetched live from OpenAlex

This paper presents a work to define four fundamental concepts, namely, buoyant force, submerged unit weight, seepage force, and critical hydraulic gradients, for saturated geomaterials including soils, rocks, and concrete under normal and high pressures using the general effective stress (GES) concept along with Terzaghi’s effective stress. In particular, four typical GES expressions are used for this purpose, and their impacts on the definition of the four concepts are compared based on available experimental evidence in the literature. The results suggest that (1) Terzaghi’s effective stress can be physically validated in the context of Archimedes’ principle for soils under normal conditions; (2) the generalized buoyant force on the unit volume of saturated geomaterials is the product of the unit weight of pore fluid and the GES coefficient tensor; and (3) the generalized seepage force theoretically acts in the direction of pore fluid flow only when the GES coefficient tensor is proportional to the permeability coefficient tensor. These four fundamental concepts have a profound significance for geotechnical applications with GES and thus merit further validation with adequate laboratory and in situ observations.

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 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 score0.332
Threshold uncertainty score0.401

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.010
GPT teacher head0.266
Teacher spread0.256 · 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

Citations3
Published2022
Admission routes1
Has abstractyes

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