MétaCan
Menu
Back to cohort
Record W2915631467 · doi:10.1139/cgj-2018-0495

Investigation of behavior of footings over rigid inclusion-reinforced soft soil: experimental and numerical approaches

2019· article· en· W2915631467 on OpenAlexvenueno aff
Hung Van Pham, Laurent Briançon, Daniel Dias, J. Racinais

Bibliographic record

VenueCanadian Geotechnical Journal · 2019
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Stabilization
Canadian institutionsnot available
Fundersnot available
KeywordsGeotechnical engineeringInclusion (mineral)Overburden pressureGeologyStructural engineeringEngineering

Abstract

fetched live from OpenAlex

The aim of this study is to investigate the behavior of a footing lying directly upon a rigid inclusion-reinforced soft soil. Both experimental and numerical approaches were conducted. The studied cases include single rigid inclusion tests, a footing over nonrigid inclusion-reinforced soil, and a footing over rigid inclusion-reinforced soil. The vertical loading tests on single rigid inclusions and the footing over unreinforced soil showed the behavior of the multi-layered soil, thus allowing for the determination of soil parameters for the numerical analyses. The tests on the footing over reinforced soil were, furthermore, carried out with different loading cases (centered and eccentric vertical loads and horizontal loads). Special attention was paid to the influence of the complex loading cases on the footing over a reinforced soil system by the measurement of the inclusion head pressure, the vertical and lateral footing settlements, and the lateral inclusion displacements. The measured pressure on the inclusion seemed to increase linearly with the vertical loading on the footing. A good agreement between the numerical analysis results and measurement data has been found for the loading phases while underprediction appears for a few loading cycles, probably due to the simplified soil constitutive model adopted.

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 score0.086
Threshold uncertainty score0.589

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 head0.013
GPT teacher head0.194
Teacher spread0.181 · 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

Citations11
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Geotechnical JournalSame topicGeotechnical Engineering and Soil StabilizationFrench-language works237,207