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Record W2040081002 · doi:10.1139/t01-043

Failure in MohrCoulomb soil cavities

2001· article· en· W2040081002 on OpenAlexvenueno aff
Antonio Gesualdo, Vincenzo Minutolo, L. Nunziante

Bibliographic record

VenueCanadian Geotechnical Journal · 2001
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsLimit analysisParaboloidHoek–Brown failure criterionGeotechnical engineeringPlasticityYield surfaceMohr–Coulomb theoryStructural engineeringRoofMathematicsEngineeringGeometrySurface (topology)Finite element methodMaterials scienceConstitutive equation

Abstract

fetched live from OpenAlex

In many cavities, resulting from both natural excavation and anthropic action, the phenomenon of the collapse of blocks from the cavity roof presents a serious safety hazard. In a previous publication the authors proposed a method to calculate the shape and dimensions of the collapsing block by means of the upper bound theorem of the plasticity theory. The soft rock material was modelled by means of the Mohr–Coulomb yield criterion, and the associated flow rule was considered for strain plastic velocity. The linear yield criterion was suitably regularized by means of a circle in the tensile zone. The boundary of the collapsing block is described by a paraboloid surface. An optimization procedure formulated in standard Kuhn–Tucker form and an analytical solution were obtained. The above-mentioned algorithm has been successfully applied to common soils of southern Italy. To validate the theoretical formulation, several numerical tests are performed. These tests show an optimal agreement with the closed-form solution. Therefore the proposed modelling may be used as an efficient guideline for the cavity-strengthening design.Key words: roof stability, regularized Mohr–Coulomb material, limit analysis, failure mechanics.

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 score0.285
Threshold uncertainty score0.987

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.005
GPT teacher head0.176
Teacher spread0.171 · 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
Published2001
Admission routes1
Has abstractyes

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