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Record W2981262959 · doi:10.1139/cgj-2019-0147

An experimental investigation of the response of slender protective structures to rockfall impacts

2019· article· en· W2981262959 on OpenAlexvenueno aff
Stéphane Lambert, F. Bourrier, Philippe Gotteland, François Nicot

Bibliographic record

VenueCanadian Geotechnical Journal · 2019
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Stabilization
Canadian institutionsnot available
FundersAgence Nationale de la Recherche
KeywordsBallastRockfallStructural engineeringGeotechnical engineeringDisplacement (psychology)BreakageGeologyEngineeringMaterials scienceComposite material

Abstract

fetched live from OpenAlex

This article investigates the mechanical response of slender rockfall protection embankments subjected to impacts based on real-scale experiments. More specifically, it deals with rectangular (in cross-section) vertical-sided gabion structures, designed to meet footprint constraints. These three-layered structures, 3 m in width and 4 m in height, are made up of gabion cages filled with different materials, depending on their location in the structure. Real-scale experiments were conducted with impact energies up to about 2000 kJ on two structures differing by the fill material used for their middle layer: ballast or sand–tire mixture. The experiments demonstrate the capacity of these slender structures in resisting high-energy impacts. The response of the structures is also addressed considering data obtained using different measuring techniques and a large number of sensors within the structure. The results are presented and discussed with the aim of highlighting some issues associated with the structure impact response, such as the load lateral diffusion, stone breakage, the contribution of the wire mesh, and the fill material characteristics. In the end, a structure with a middle layer filled with ballast appears more efficient in reducing the structure back face displacement.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.577
Threshold uncertainty score0.375

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 head0.007
GPT teacher head0.210
Teacher spread0.203 · 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

Citations22
Published2019
Admission routes1
Has abstractyes

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