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Record W4402651056 · doi:10.1051/e3sconf/202456928002

Bearing capacity of strip footings seated on unreinforced and reinforced granular layers over random homogeneous and isotropic spatially variable undrained soft clay

2024· article· en· W4402651056 on OpenAlexaff
Richard J. Bathurst, Reza Jamshidi Chenari

Bibliographic record

VenueE3S Web of Conferences · 2024
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Stabilization
Canadian institutionsRoyal Military College of Canada
Fundersnot available
KeywordsIsotropyGeotechnical engineeringHomogeneousBearing capacityGeologyMaterials sciencePhysicsStatistical physics

Abstract

fetched live from OpenAlex

The bearing capacity of a footing seated directly on a soft clay foundation with constant undrained shear strength is the classical Prandtl geotechnical problem. However, a footing seated directly on an undrained clay soil is an unlikely arrangement in practice. Rather, a granular layer is used as a working platform for the construction of the footing and to dissipate the footing loads over a wider area of the clay foundation surface. This paper revisits the footing problem for the case of a footing seated directly on the clay foundation, and seated on an unreinforced and geosynthetic reinforced granular layer overlying the clay foundation. Both deterministic and stochastic numerical modelling of the bearing capacity problem using the program FLAC 2D were carried out. The relationship between the design factor of safety and probabilistic margins of safety is explored for clay foundations with random homogenous and isotropic spatially variable undrained shear strength with mean strengths from 5 to 25 kPa (very soft to soft clay). For practical target probabilistic margins of safety against exceeding a design bearing capacity, the random homogenous soil condition is shown to be the most critical case for design.

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 score0.077
Threshold uncertainty score0.613

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 head0.194
Teacher spread0.185 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

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