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Record W3181395974 · doi:10.1139/cgj-2021-0072

Field study of group effects on the pullout capacity of “deep” helical piles in sand

2021· article· en· W3181395974 on OpenAlexvenueno aff
Aaron S. Bradshaw, Lindsay Cullen, Zachary K Miller

Bibliographic record

VenueCanadian Geotechnical Journal · 2021
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Mechanics
Canadian institutionsnot available
Fundersnot available
KeywordsPileGeotechnical engineeringSubmarine pipelineUltimate tensile strengthStructural engineeringEngineeringHelix (gastropod)Penetration testGroup effectGeologyMaterials scienceComposite material

Abstract

fetched live from OpenAlex

This paper presents the results of a field load test program used to investigate group effects on the pullout capacity of single-helix “deep” helical piles/anchors in sand. The high tensile capacity and silent installation of helical piles has given them serious consideration as an alternative to conventional deep foundations and anchors for offshore renewable energy structures. New offshore applications may consider the use of groups of helical piles to resist structural loads. Group interaction effects are known to occur in helical piles, but there is a scarcity of field data on groups in sands under tensile loading. This study involved the installation and load testing of single-helix 152 mm diameter round shaft piles and pile groups embedded in sand to depths of 12 and 18 helix diameters below the ground surface. The study was designed to explore the effects of close pile spacing, group configuration (i.e., number of piles), and soil strength (i.e., friction angle) as interpreted from cone penetration test (CPT) resistance. The results showed group efficiencies ranging from about 0.6 to 1.0 at a horizontal spacing of 2 to 3 times the helix diameter in sands with friction angles of about 39° to 44°. The data from this study may also be useful for validation of numerical models for analysis of helical pile group interactions.

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)0.001
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 score0.133
Threshold uncertainty score0.519

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 integrity0.0000.001
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 head0.009
GPT teacher head0.188
Teacher spread0.179 · 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

Citations24
Published2021
Admission routes1
Has abstractyes

Explore more

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