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Record W2995686425 · doi:10.3390/app9245371

Finite Element Steady-State Vibration Analysis Considering Frequency-Dependent Soil-Pile Interaction

2019· article· en· W2995686425 on OpenAlexaboutno aff
Seung-Han Song, Sean Seungwon Lee

Bibliographic record

VenueApplied Sciences · 2019
Typearticle
Languageen
FieldEngineering
TopicHydraulic and Pneumatic Systems
Canadian institutionsnot available
FundersHanyang University
KeywordsPileStiffnessFoundation (evidence)VibrationStructural engineeringGas compressorFinite element methodEngineeringPile capGeotechnical engineeringMechanical engineeringAcousticsPhysics

Abstract

fetched live from OpenAlex

The vibration response of equipment foundation structures is not only affected by the structural stiffness and mass, but also greatly influenced by the degree of a soil-foundation structural interaction. Furthermore, the vibratory performance of equipment foundation structures supported by pile systems largely depends on the soil-pile dynamic stiffness and damping, which are variable in nature within the speed range that machines operate at. This paper reviews a method for evaluating effective soil-pile stiffness and damping that can be computed by Novak’s method or by commercial software (DYNA6, University of Western Ontario). A series of Finite Element (FE) time history and steady-state analyses using SAP2000 have been performed to examine the effects of dynamic soil-pile-foundation interaction on the vibration performance of equipment foundations, such as large compressor foundations and steam/gas turbine foundations. Frequency-dependent stiffness is estimated to be higher than frequency-independent stiffness, in general, and, thus, affects the vibration calculation. This paper provides a full-spectrum steady-state vibration solution, which increases the reliability of the foundation’s structural design.

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 score0.139
Threshold uncertainty score0.588

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.236
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

Citations6
Published2019
Admission routes1
Has abstractyes

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