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Behavior of Axially and Eccentrically Loaded Trapezoidal Shell Footings Resting on a Granular Assembly

2022· article· en· W4281692575 on OpenAlexaff
Sohrab Sharifi, Saeed Abrishami, Daniel Dias, Pooya Dastpak

Bibliographic record

VenueInternational Journal of Geomechanics · 2022
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsEccentricity (behavior)Geotechnical engineeringAxial symmetryStructural engineeringSettlement (finance)Shell (structure)Bearing capacityRotation (mathematics)Displacement (psychology)Particle image velocimetryMechanicsGeologyMaterials scienceEngineeringMathematicsGeometryPhysicsComposite materialComputer scienceLaw

Abstract

fetched live from OpenAlex

The objective of this paper is to investigate the behavior of strip shell footings subjected to axial and eccentric loads resting on a cylindrical steel rods assembly that simulates granular soil. An image processing analysis called particle image velocimetry (PIV) is used to obtain the displacement field around the footings. The bearing capacity and ultimate settlements of the footings are presented that show a linear variation with respect to the footing peak angle. The efficiency of eccentrically loaded shell footings is also discussed using settlement and rotation factors. Shell footings are proven to demonstrate better performance under eccentric loading in terms of settlement compared with axial loading cases, while a better performance is found at low eccentricities in terms of rotation. The PIV analysis provided the opportunity for a deeper comprehension of ground wedges beneath the footing and a comparison of results with analytical methods that validated the experimental results. Eventually, an eccentricity factor is proposed for the upper-bound solution to make it applicable for nonaxial loading cases.

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 score0.079
Threshold uncertainty score0.491

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 head0.008
GPT teacher head0.214
Teacher spread0.206 · 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
Published2022
Admission routes1
Has abstractyes

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