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Record W2096379908 · doi:10.1139/t06-105

Analysis of piles in a residual soil—The ISC'2 prediction

2007· article· en· W2096379908 on OpenAlexvenueno aff
Bengt H. Fellenius, Jaime Santos, António Viana da Fonseca

Bibliographic record

VenueCanadian Geotechnical Journal · 2007
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Mechanics
Canadian institutionsnot available
FundersFundação para a Ciência e a Tecnologia
KeywordsPileGeotechnical engineeringEmbedmentStrain gaugeStructural engineeringLoad testingDynamic load testingBearing capacityEngineering

Abstract

fetched live from OpenAlex

The 2nd International Conference on Site Characterization (ISC'2), held in 2004, included a seminar for prediction of pile capacity involving three 6 m embedment length test piles, one 350 mm square driven concrete pile, and two 600 mm diameter, strain-gage instrumented, bored piles. Invited predictors were provided with results of in situ, laboratory tests and dynamic tests. Test layout, soil information, and pile data are presented with calculations of pile capacity and load distribution, submitted predictions, and results of the static loading tests. The CPT-calculated capacities show considerable scatter—total values ranged from 500 to 1400 kN for the driven pile and from 1000 to 1900 kN for the bored piles. The static loading test on the driven pile showed an offset limit load of 1200 kN and a plunging capacity of 1500 kN. Despite pile movements of 100 mm for 1200 kN of applied load, neither of the bored piles showed signs of having reached an ultimate resistance value. Effective stress analysis of strain measurements for the bored piles showed the data to correlate to a β coefficient of 1.0 and a toe coefficient of 16. Most submitted predictions underestimated the capacity of the driven pile and overestimated the capacities of the bored piles.Key words: pile capacity, effective stress analysis, shaft and toe resistances, β coefficient, CPTU, dynamic testing.

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)0.001
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 score0.109
Threshold uncertainty score0.981

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.007
GPT teacher head0.191
Teacher spread0.184 · 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

Citations29
Published2007
Admission routes1
Has abstractyes

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