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Record W4377965250 · doi:10.1061/ijgnai.gmeng-8275

Dynamic Soil Reactions around a Beam-on-Multiple-Piles Structure and their Application in the Parallel Seismic Integrity Test

2023· article· en· W4377965250 on OpenAlexaff
Juntao Wu, M. Hesham El Naggar, Kuihua Wang, Syed Muhammad Faheem Rizvi

Bibliographic record

VenueInternational Journal of Geomechanics · 2023
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsWestern University
Fundersnot available
KeywordsPileBeam (structure)Structural engineeringVibrationFinite element methodSoil structure interactionGeotechnical engineeringDynamic testingEngineeringPhysicsAcoustics

Abstract

fetched live from OpenAlex

In this paper, an analytical solution to the dynamic reaction of half-space soil around a beam-on-multiple-piles structure is proposed. This can be used to evaluate the effect of an adjacent vibrating pile of unequal length or pile defects on parallel seismic (PS) integrity test results. The structure–soil interaction (SSI) problem was decomposed into a beam–multiple-pile coupled vibrating model and an extended half-space soil model. The surrounding soil reactions were calculated employing the half-space soil excited by known vibration (HEKV) solution scheme. The HEKV solution scheme and the accuracy of the analytical results were validated through comparisons with finite-element analysis (FEA). The validated model was then used to study the influence of an adjacent vibrating pile on the pile response, and to optimize the selection of technical parameters for the PS test. The obtained results were used to discuss the uncertainties of the PS test in terms of multiple-pile foundations and adjacent vibrating piles, and to develop the theoretical basis for testing multiple-pile foundations. Finally, practical guidance is provided for the application of the PS test to multiple-pile foundations in practice.

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 score0.145
Threshold uncertainty score0.391

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 integrity0.0000.001
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 head0.008
GPT teacher head0.226
Teacher spread0.218 · 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

Citations3
Published2023
Admission routes1
Has abstractyes

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