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Passive Earth Pressure of Overconsolidated Cohesionless Backfill

2005· article· en· W2145608175 on OpenAlexafffund
Adel Hanna, Imad Al Khoury

Bibliographic record

VenueJournal of Geotechnical and Geoenvironmental Engineering · 2005
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Stabilization
Canadian institutionsConcordia University
FundersNatural Sciences and Engineering Research Council of CanadaConcordia University
KeywordsLateral earth pressureGeotechnical engineeringGeologyRetaining wallHomogeneousPressure coefficientEngineering

Abstract

fetched live from OpenAlex

An experimental investigation on the passive earth pressure of overconsolidated cohesionless soil on retaining walls was conducted. A prototype model of a vertical rough wall, retaining horizontal backfill, was developed in the laboratory. The model was instrumented to measure the total passive earth pressure acting on the wall, the passive earth pressure acting on selected locations on the wall, and the overconsolidation ratio (OCR) of the sand in the testing tank. In order to develop the state of passive pressure, the wall was pushed horizontally toward the backfill without any rotation. Overconsolidated sand was produced in the testing tank by placing the sand in thin layers; each was compacted mechanically for a period of time. Tests were performed on walls retaining homogeneous overconsolidated sand, and overconsolidated sand backfill overlying the deep sand layer. The method of slices developed for predicting the coefficient of passive earth pressure for normally consolidated soil was adopted for the conditions stated above. The theoretical values compared well with the experimental results of the present investigation. It is of interest to note that the OCR and the soil condition below the founding level significantly affect the value of the coefficient of passive earth pressure on these walls. Design charts and formulae are presented for practical use.

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 score0.035
Threshold uncertainty score0.679

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 head0.002
GPT teacher head0.156
Teacher spread0.153 · 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

Citations25
Published2005
Admission routes2
Has abstractyes

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