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Record W2170772066 · doi:10.5539/esr.v1n2p137

Effects of Geometrical Properties of Rectangular Trenches Intended for Passive Isolation in Sandy Soils

2012· article· en· W2170772066 on OpenAlexvenueno aff
Mehrab Jesmani, Arash Moghadam Fallahi, Hamed Faghihi Kashani

Bibliographic record

VenueEarth Science Research · 2012
Typearticle
Languageen
FieldEngineering
TopicRailway Engineering and Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsTrenchFinite element methodVibrationGeotechnical engineeringStructural engineeringParametric statisticsGeologyVibration isolationSoil waterRADIUSShallow foundationShallow trench isolationEngineeringAcousticsMaterials scienceComputer scienceBearing capacityPhysicsComposite material

Abstract

fetched live from OpenAlex

Wave barriers are placed actively and passively in the ground to reduce the transmission of vibration produced by industry, traffic, train, etc. They include open trenches, in-filled trenches and sheet piles, etc. In most recent studies, the researchers have worked on parameters such as depth, width, length of trenches and also, the distance between the source of vibration and trench in cohesive soils. Most researches are evaluation on screening induced by shallow foundation. In this study the passive isolation has been investigated with the help of open trenches screening against the vibration produced by deep foundations in sandy soils, and the effect of trench angle and radius have been studied. Moreover, the reasons for amplitude increasing before open trenches have been evaluated. Three dimensional finite element analyses (FEM) with ANSYS software are used to achieve an exact parametric study on passive screening. The assumed strains are less than 10-3so the bilinear Elasto-Plastic behavior has been utilized.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.037
GPT teacher head0.296
Teacher spread0.259 · 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 designObservational
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

Citations18
Published2012
Admission routes1
Has abstractyes

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