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Record W4387745009 · doi:10.1680/jphmg.22.00062

Centrifuge modelling of axially loaded steel piles in cold and thawing frozen sand

2023· article· en· W4387745009 on OpenAlexaff
Chris Clarkson, Geoff Eichhorn, Greg Siemens

Bibliographic record

VenueInternational Journal of Physical Modelling in Geotechnics · 2023
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsRoyal Military College of Canada
Fundersnot available
KeywordsPileCentrifugeGeotechnical engineeringPermafrostGround freezingFoundation (evidence)StiffnessGeologyEngineeringEnvironmental scienceStructural engineering

Abstract

fetched live from OpenAlex

Foundations in northern climates are founded under ground conditions that are certain to change due to climate warming. Piled foundations situated in permafrost are designed to resist loads by mobilising the shaft friction from adfreeze strength that is attributed to the ice–soil bonds in contact with the pile. Design considers ground warming causing thawing over time and normally specifies a thermal condition whereby mitigation measures, such as thermosyphons, need to be implemented. While pile design and analysis for completely frozen and thawed profiles are defined in terms of pile capacity, the intermediate condition, during transition from frozen to thawed, is not well examined. In this study, centrifuge modelling is utilised to quantify the reduction in pile capacity and foundation stiffness under an axial monotonic loading as initially frozen sand profiles warm and thaw depth increases. The results show agreement between the physical models and analytical methods for piles in fully frozen and thawed ground. A marked decrease in pile capacity occurs as ground temperatures approach freezing and thaw depth increases. The results are the first comprehensive physical model testing programme aimed at quantifying pile performance in frozen and warming ground under field-realistic stress conditions.

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 score0.008
Threshold uncertainty score0.387

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 head0.057
GPT teacher head0.264
Teacher spread0.207 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Physical Modelling in GeotechnicsSame topicClimate change and permafrostFrench-language works237,207