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Evaluation of Seismic Soil–Structure Interaction of Full-Scale Grouped Helical Piles in Dense Sand

2020· article· en· W3092427360 on OpenAlexaff
Maryam Shahbazi, Amy B. Cerato, M. Hesham El Naggar, Ahmed Elgamal

Bibliographic record

VenueInternational Journal of Geomechanics · 2020
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Mechanics
Canadian institutionsWestern University
Fundersnot available
KeywordsPileEarthquake shaking tableGeotechnical engineeringHead (geology)Natural frequencyStiffnessStructural engineeringSoil structure interactionGeologyParametric statisticsScale modelEngineeringVibrationFinite element methodPhysicsMathematicsAcoustics

Abstract

fetched live from OpenAlex

A full-scale shake table testing program was performed with two helical pile groups supporting model structures to better understand the dynamic properties of a soil–pile-group–structure system. Each group had four piles embedded in dry, dense sand confined in a 6.7 m L × 3.0 m W × 4.6 m H laminar box. One group was comprised of 8.8-cm diameter and 3.66-m long helical piles, while the second group had 14-cm diameter and 4.27-m long helical piles. To investigate the influence of the pile-head fixity condition on group behavior, both pinned head and fixed head connections were implemented and tested. White noise excitation and two replicated strong earthquake motions were used to determine the natural frequency and observe the seismic behavior of the soil–pile-group–structure system, respectively. The experimental observations were analyzed to evaluate structural natural frequency and pile-group stiffness and damping. The experimental results were used to calibrate a numerical model that was then used to conduct a parametric study to gain a broader understanding of the seismic behavior of helical pile groups under varying conditions. The experimental and numerical results were compared and the effects of varying properties in a soil–pile–structure system, including their system seismic response, are discussed.

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)0.001
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 score0.125
Threshold uncertainty score0.497

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.017
GPT teacher head0.247
Teacher spread0.231 · 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

Citations17
Published2020
Admission routes1
Has abstractyes

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