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Record W1883447107 · doi:10.1139/cgj-2014-0462

Experimental study on dynamic interference effect of two closely spaced machine foundations

2015· article· en· W1883447107 on OpenAlexvenueno aff
Abhijeet Swain, Priyanka Ghosh

Bibliographic record

VenueCanadian Geotechnical Journal · 2015
Typearticle
Languageen
FieldEngineering
TopicRailway Engineering and Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsStructural engineeringDamping ratioDisplacement (psychology)VibrationHarmonicEngineeringAmplitudeRange (aeronautics)Dynamic loadingForce dynamicsExcitationGeotechnical engineeringAcousticsPhysicsOpticsElectrical engineering

Abstract

fetched live from OpenAlex

This paper presents an experimental study on the dynamic interaction effect of closely spaced square foundations under machine vibration. Under a dynamic condition, a number of large-scale model tests were conducted in the field, which includes a wide range of study on the isolated as well as interacting footing response resting on the local soil available at Kanpur, India. The dynamic interaction of different combinations (size) of two-footing assemblies was investigated by inducing vertical harmonic load on one of the footings (active footing), while the other footing (passive footing) was loaded with static weight only. The active footing was excited with different magnitudes of dynamic loading and the response was recorded for both footings, placed at a different clear spacing (S). The results are compiled and shown as the variation of displacement amplitude with frequency. The transmission ratio that predicts the effect of dynamic excitation of the active footing on the passive one is determined for the interacting footings and plotted with respect to frequency ratio.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.013
GPT teacher head0.268
Teacher spread0.254 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations33
Published2015
Admission routes1
Has abstractyes

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