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Record W2039276042 · doi:10.1680/geot.2000.50.6.667

Laboratory investigation of efficiency of conical-based pounders for dynamic compaction

2000· article· en· W2039276042 on OpenAlexaboutno aff
Tao Feng, K.-H. Chen, Yang Su, Yunfang Shi

Bibliographic record

VenueGéotechnique · 2000
Typearticle
Languageen
FieldEngineering
TopicDrilling and Well Engineering
Canadian institutionsnot available
FundersNational Science Council
KeywordsDynamic compactionCompactionImpact craterConical surfaceGeotechnical engineeringRelative densityPenetration (warfare)Penetration testMaterials scienceVolume (thermodynamics)GeologyComposite materialEngineeringSubgrade

Abstract

fetched live from OpenAlex

The pounder for dynamic compaction is conventionally built to have a flat bottom. An innovative idea of using a conical-based pounder to improve the efficiency of dynamic compaction has been investigated, and the results are presented in this paper. Both a conical-based pounder and a flat-based pounder were used in an extensive programme of laboratory dynamic compaction tests. An air pluviation technique was used to prepare dry sand specimens, 60 cm in diameter and 50 cm high, to have initial relative densities of 30% and 40%. Wet sand specimens with an initial relative density of 60% were prepared from the dry sand specimens. A total of 14 dynamic compaction tests were performed. The results of each test include dimensions of the crater, heave around the crater, and cone penetration resistance at 13 penetration locations. It is found from the test results that the efficiency of dynamic compaction with the conical-based pounder depends on both the grain size characteristics and the volume change behaviour of the sand. The conical-based pounder is more effective in the Mai-Liao fine sand with 8% fines than in the clean Ottawa medium sand.

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: none
Teacher disagreement score0.589
Threshold uncertainty score0.336

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 integrity0.0000.000
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 head0.006
GPT teacher head0.205
Teacher spread0.200 · 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

Citations33
Published2000
Admission routes1
Has abstractyes

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