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Record W2805314208 · doi:10.1061/9780784481639.007

Modelling the Behaviour and Efficiency of Minipile Groups in Clay

2018· article· en· W2805314208 on OpenAlexaff
A. V. Rose, R.N. Taylor

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Stabilization
Canadian institutionsWSP (Canada)
FundersEngineering and Physical Sciences Research Council
KeywordsPileCentrifugeGeotechnical engineeringSettlement (finance)Block (permutation group theory)PerimeterStructural engineeringGroup (periodic table)Structural loadEngineeringGridGeologyMathematicsGeometryComputer science

Abstract

fetched live from OpenAlex

Minipile groups have been used to create high load capacity foundations especially where significant space restrictions exist during construction. The interest in such foundations, particularly when the piles are arranged solely around a perimeter, initiated a research project in which geotechnical centrifuge model tests were used to investigate the key geometric parameters affecting the load-settlement behaviour and efficiency of groups of bored, slender, high aspect ratio piles. Circular and square perimeter pile groups were tested along with conventional grid groups. Two modes of failure were observed: either the individual piles pushed into the ground with no obvious settlement of the surrounding soil or as a block with the soil contained within the outer ring of piles settling by the same or similar amount as the piles. The change from block failure to individual pile failure generally occurred at a pile spacing of approximately 2 pile diameters. A complementary numerical modelling study gave insight into the pile-soil interaction and in particular the patterns of displacement as failure changed from individual pile to block mode. Overall, a grid group arrangement was found to be less efficient in terms of load carrying capacity than a perimeter group and the inclusion of a single target pile within a perimeter group could further enhance the group efficiency.

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 score0.171
Threshold uncertainty score0.124

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 head0.009
GPT teacher head0.193
Teacher spread0.184 · 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

Citations0
Published2018
Admission routes1
Has abstractyes

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