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Record W2550800221

Dynamic Pile-Soil Interaction in Laterally Spreading Slopes

2009· dissertation· en· W2550800221 on OpenAlexfundaboutno aff
Raejee Kaewsong

Bibliographic record

VenueQSpace (Queen's University Library) · 2009
Typedissertation
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Mechanics
Canadian institutionsnot available
FundersQueen's University
KeywordsPileGeotechnical engineeringGeologyEnvironmental scienceSoil science
DOInot available

Abstract

fetched live from OpenAlex

The collapse of buildings and infrastructure is an unfortunate consequence of major earthquakes (e.g., the 1964 Alaskan earthquake, the 1995 Kobe earthquake in Japan and the 2007 Pisco earthquake in Peru).Liquefaction-induced lateral spreading is known to be one cause of severe damage to deep foundation systems.However, the dynamic soil-structure interaction between liquefied soil and piles is extremely complex and further work is required to define the appropriate design pressures and to understand the mechanisms at work.This thesis presents the findings of an experimental program carried out using the large geotechnical centrifuge at C-CORE in St John's Newfoundland, to investigate the mechanism of lateral spreading and its implications for dynamic soil-pile interaction.Soil and pile responses were measured using accelerometers, pore pressure transducers, and digital imaging using a high speed camera.Using these images, transient profiles of slope deformation were quantitatively measured using Particle Image Velocimetry (PIV).These tests illustrate the potential for earthquake shaking to excite the natural frequency of the liquefied soil column, which can lead to increased transient lateral pressures on piles in liquefiable ground.This study recommends that this potential for "auto tuning" should be anticipated in design and proposes a new limiting pseudo-static backbone p-y curve for use in the design of piles subjected to lateral spreading ground deformation.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.589
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.001
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 head0.003
GPT teacher head0.170
Teacher spread0.167 · 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.

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

Citations2
Published2009
Admission routes2
Has abstractyes

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