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Record W2115986331 · doi:10.1139/t07-107

Compression testing and analysis of drilled concrete tapered piles in cohesive-frictional soil

2008· article· en· W2115986331 on OpenAlexaffvenue
Muhammad Kamran Khan, M. Hesham El Naggar, Mohamed Elkasabgy

Bibliographic record

VenueCanadian Geotechnical Journal · 2008
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Mechanics
Canadian institutionsWestern University
Fundersnot available
KeywordsPileGeotechnical engineeringStructural engineeringBearing capacityLoad testingCompressive strengthFinite element methodCompression (physics)EngineeringMaterials scienceComposite material

Abstract

fetched live from OpenAlex

When pile driving is difficult and (or) economically not viable, cast-in-place piles are indispensable. Tapered piles, which have top cross-sections larger than the bottom cross-sections, have the potential for substantial advantages over conventional straight-sided piles. This paper investigates the construction and performance of innovative drilled concrete tapered piles. A full-scale pile load-testing program was conducted to evaluate the axial compressive capacity of drilled concrete tapered piles in frictional soil. One straight and three tapered augers were designed and manufactured to produce six piles. The piles that were constructed and tested included four tapered and two straight piles. The testing results showed that tapered piles with a taper angle varying between 0.95° and 1.91° had a load carrying capacity up to 50% higher than the straight-sided piles with equal volume. It should be noted, however, that the experimental results are site specific. Moreover, an analytical expression was developed to evaluate the pile taper effect on its shaft capacity in terms of a taper coefficient, K t . The value of K t evaluated from the analytical expression compared well with experimentally measured values. Finally, a two dimensional nonlinear finite element analysis was conducted to simulate the load test conditions, and it proved to be successful.

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 score0.040
Threshold uncertainty score0.661

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.015
GPT teacher head0.188
Teacher spread0.173 · 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

Citations64
Published2008
Admission routes2
Has abstractyes

Explore more

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