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Record W1986162831 · doi:10.1139/t10-031

Impact compression behaviors of high-capacity long piles

2010· article· en· W1986162831 on OpenAlexvenueno aff
Arthur K.O. So, Charles W. W. Ng

Bibliographic record

VenueCanadian Geotechnical Journal · 2010
Typearticle
Languageen
FieldEngineering
TopicGranular flow and fluidized beds
Canadian institutionsnot available
Fundersnot available
KeywordsPileHammerGeotechnical engineeringCompression (physics)Drop (telecommunication)Impact energyStructural engineeringMechanicsEngineeringMaterials sciencePhysicsMechanical engineeringComposite material

Abstract

fetched live from OpenAlex

The Hiley formula underestimates driving resistance of long piles. Methods using affected pile length have been suggested, but have been found to be inapplicable for high-capacity piles. The impact compression behaviors of about 4700 high-capacity H-piles that were 14–80 m long at final set were studied. Measured data revealed that maximum impact forces are very scattered, but their means are independent of the hammer type, ram weight, ram drop, impact velocity, and pile length. Maximum impact compression of pile and affected pile length exist in both long and short piles. The affected pile length in turn is significant to the blow efficiency, hammer constant, and energy transfer ratio. This length is governed by the impact momentum and impact energy, and can be estimated by an energy-based equation. If the affected pile length determined by this equation is substituted into the Hiley formula to back-analyze the driving resistance, predictability of the driving formula can be improved by about 8%. This improvement is significant enough to reduce the number of hammer blows required at very hard driving conditions and reduce pile damage. Furthermore, this proposed equation is simple to use in the field and is more economical compared with stress-wave monitoring techniques.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.833
Threshold uncertainty score0.998

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.219
Teacher spread0.210 · 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 designBench or experimental
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

Citations4
Published2010
Admission routes1
Has abstractyes

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