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Record W2904060069 · doi:10.1680/jgeot.18.p.001

Seismic performance of helical piles in dry sand from large-scale shaking table tests

2018· article· en· W2904060069 on OpenAlexaff
Moustafa Khaled Elsawy, M. Hesham El Naggar, Amy B. Cerato, Ahmed Elgamal

Bibliographic record

VenueGéotechnique · 2018
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Mechanics
Canadian institutionsWestern University
FundersNational Science Foundation
KeywordsEarthquake shaking tablePileGeotechnical engineeringFoundation (evidence)EngineeringStructural engineeringGeology

Abstract

fetched live from OpenAlex

Recent earthquakes have highlighted the need for safe and efficient construction of earthquake-resilient structures. Meanwhile, helical piles are gaining popularity as a foundation not only for new construction, but also retrofitted structures, owing to their advantages over conventional driven pile alternatives. In addition, several structures supported by helical piles in New Zealand sustained minimal damage during the Christchurch earthquake, proving that helical piles can be a suitable foundation option in highly seismic areas. However, there is a knowledge gap concerning their seismic performance. Therefore, the current study investigates the performance of helical piles through a full-scale shaking table test programme – the inaugural large-scale seismic testing programme performed on helical piles. The experimental set-up involved installing ten steel piles with different configurations and pile head masses in dry sand enclosed in a laminar shear box mounted on the Network for Earthquake Engineering Simulation at University of California, San Diego (NEES/UCSD) large high-performance outdoor shaking table (LHPOST). The loading scheme consisted of white noise and two earthquake time histories with varying intensity and frequency content. The experimental results are presented in terms of the natural frequency and response of test piles (peak deflections). The effects of loading frequency and intensity and the pile's geometrical configuration and installation method were evaluated.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.390
Threshold uncertainty score0.709

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 head0.201
Teacher spread0.195 · 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

Citations47
Published2018
Admission routes1
Has abstractyes

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