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Record W2136780757 · doi:10.1139/t08-015

Bearing capacity of strip footings on purely frictional soil under eccentric and inclined loads

2008· article· en· W2136780757 on OpenAlexvenueno aff
Dimitrios Loukidis, Tanusree Chakraborty, Rodrigo Salgado

Bibliographic record

VenueCanadian Geotechnical Journal · 2008
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsnot available
Fundersnot available
KeywordsGeotechnical engineeringBearing capacityDilatantFoundation (evidence)Flow (mathematics)Moment (physics)EmbedmentStructural engineeringEngineeringBearing (navigation)GeologyMathematicsGeometryPhysics

Abstract

fetched live from OpenAlex

The finite element method is used for the determination of the collapse load of a rigid strip footing placed on a uniform layer of purely frictional soil subjected to inclined and eccentric loading. The footing is set on the free surface of the soil mass with no surcharge applied. The soil is assumed to be elastic – perfectly plastic following the Mohr–Coulomb failure criterion. Two series of analyses were performed, one using an associated flow rule and one using a nonassociated flow rule. The first series is in accordance with bearing capacity solutions currently used in shallow foundation design practice, while the second one is consistent with the dilatancy exhibited by sands in reality. Both probe-type analyses and swipe-type analyses were undertaken. Analyses for associated and nonassociated flow rules yield essentially the same trends regarding the effective width, inclination factor, and normalized vertical force – horizontal force – moment (V–H–M) failure envelope. The results show that the inclination factor depends on the value of the friction angle, whereas the effective width does not.

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 score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.013
GPT teacher head0.184
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 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

Citations189
Published2008
Admission routes1
Has abstractyes

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