MétaCan
Menu
Back to cohort
Record W4389272955 · doi:10.3397/in_2023_0645

Study of Simple Prediction Method for Road Traffic Vibration from viaducts

2023· article· en· W4389272955 on OpenAlexaff
Noboru Kamiakito, Masayuki Shimura, Yasuyuki Sano, Tatsuaki Mori

Bibliographic record

VenueNOISE-CON proceedings · 2023
Typearticle
Languageen
FieldEngineering
TopicRailway Engineering and Dynamics
Canadian institutionsNexen (Canada)
Fundersnot available
KeywordsVibrationPierStructural engineeringSlabEngineeringPoint (geometry)Traffic flow (computer networking)Computer scienceAcousticsMathematicsGeometryPhysics

Abstract

fetched live from OpenAlex

On Prediction of Road Traffic Vibration of INCE/J Technical Subcommittees, we are studying the creation of prediction formulas for vibration from embankments and cuts and vibration from bridges. This paper presents the concept of a simplified method for predicting road traffic vibration from viaducts. When large vehicle runs on viaduct, vibration occurs in the floor slab and propagates from the piers and foundation to the ground. Here, the reference waveform at the bottom of the pier is used as the reference point unit pattern, and the vibration propagates from the bottom of each pier on the plane to the ground surface, and a method of calculating the combined value at the prediction point is considered, and the establishment of a prediction method is examined. bottom. In addition, we will report on the results of an attempt to predict the vibration propagation situation to the surroundings for a large vehicle test run and compare it with the actual measurement value. In addition, we examined the calculation procedure assuming traffic flow conditions in this model.

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 score0.146
Threshold uncertainty score0.725

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 head0.015
GPT teacher head0.248
Teacher spread0.232 · 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
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueNOISE-CON proceedingsSame topicRailway Engineering and DynamicsFrench-language works237,207