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Record W4400287321 · doi:10.1121/10.0027714

Tri-axial ground-borne vibration measurements during rail pass-bys

2024· article· en· W4400287321 on OpenAlexaffabout
Harry Ao Cai, Nathan Gara, Brian Howe

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2024
Typearticle
Languageen
FieldEngineering
TopicRailway Engineering and Dynamics
Canadian institutionsBGC Engineering (Canada)
Fundersnot available
KeywordsVibrationAcousticsStructural engineeringPhysicsGeologyEngineering

Abstract

fetched live from OpenAlex

Ground-borne vibrations from rail pass-bys, transmitted from train wheels rolling on the rails, have the potential to cause various adverse effects at nearby receptors, such as annoyance and re-radiated noise. To assess the impact of rail pass-bys, surface-level vibration can be characterized in three components: one vertical and two horizontal directions. Prior experience, best-practice guidelines from the Federal Transit Administration Transit Noise and Vibration Impact Assessment Manual, and theory of propagation of Rayleigh surface waves indicate that the vertical component dominates the horizontal components, such that vibrations from rail pass-bys can be adequately characterized by the vertical component only. This purpose of this study is to assess the sufficiency of characterizing the impact of ground-borne vibrations caused by rail pass-bys based solely in the vertical direction. Tri-axial vibration measurements were conducted for freight and passenger trains in Ontario, Canada, using a multi-channel signal analyzer for simultaneous measurement of multiple-axis vibration levels. The measured levels were then examined across the three components.

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: none
Teacher disagreement score0.887
Threshold uncertainty score0.341

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 head0.011
GPT teacher head0.219
Teacher spread0.208 · 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

Citations0
Published2024
Admission routes2
Has abstractyes

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