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Record W1949772009 · doi:10.1139/t11-103

Lower-bound calculations of the bearing capacity of eccentrically loaded footings in cohesionless soil

2012· article· en· W1949772009 on OpenAlexvenueno aff
Sven Krabbenhøft, Lars Damkilde, K. Krabbenhøft

Bibliographic record

VenueCanadian Geotechnical Journal · 2012
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsGeotechnical engineeringBearing capacityGeology

Abstract

fetched live from OpenAlex

Lower-bound calculations based on the finite element method are used to determine the bearing capacity of a strip foundation subjected to a vertical, eccentric load on cohesionless soil with varying surcharges. The soil is assumed perfectly plastic following the Mohr–Coulomb failure criterion. The results are reported as tables and graphs showing the bearing capacity as a function of the eccentricity and surcharge. Normalised interaction diagrams in the vertical force versus moment plane have been produced. The results from the analysis are in reasonable agreement with existing methods for smaller eccentricities, whereas for greater eccentricities (e > 0.25B–0.3B, where B is the width of the foundation), the lower-bound values in general — and especially for greater surcharges — are considerably smaller than the bearing capacities predicted by existing methods. For the special case of no surcharge, the results are in very good agreement with results obtained by the effective-width approach originally proposed by Meyerhof.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.012
GPT teacher head0.194
Teacher spread0.182 · 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 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

Citations47
Published2012
Admission routes1
Has abstractyes

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