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Record W3213318734 · doi:10.3390/geosciences11110472

Evaluation of the Static Design Procedure in the Canadian Foundation Engineering Manual for Piles in Cohesionless Soil

2021· article· en· W3213318734 on OpenAlexaffabout
Hany El Naggar, Islam Ezzeldin

Bibliographic record

VenueGeosciences · 2021
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Mechanics
Canadian institutionsDalhousie University
Fundersnot available
KeywordsPileFoundation (evidence)Geotechnical engineeringBearing capacitySettlement (finance)EngineeringShallow foundationCivil engineeringSoil waterEnvironmental scienceComputer scienceSoil science

Abstract

fetched live from OpenAlex

Piles provide a convenient solution for heavy structures, where the foundation soil bearing capacity, or the tolerable settlement may be exceeded due to the applied loads. In cohesionless soils, the two frequently used pile installation methods are driving and drilling (or boring). This paper reviews the results of a large database of pile load tests of driven and drilled piles in cohesionless soils at various locations worldwide. The load test results are compared with the static analysis design method for single piles recommended in the Canadian Foundation Engineering Manual (CFEM) and other codes and standards such as the American Association of State Highway and Transportation Officials, Federal Highway Administration, American Petroleum Institute, Eurocode, and the Naval Facilities Engineering Command. An improved pile design procedure is proposed linking the pile design coefficients (β) and (Nt) to the friction angle of the soil, rather than employing the generalized soil type grouping scheme previously used in the CFEM. This improvement included in the new version of the CFEM 2021 produces a more unified value of the pile capacity calculated by different designers, reducing the obtained design capacity discrepancies.

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)0.002
metaresearch head score (Gemma)0.001
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 score0.093
Threshold uncertainty score0.922

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.038
GPT teacher head0.257
Teacher spread0.219 · 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

Citations2
Published2021
Admission routes2
Has abstractyes

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