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Attenuator systems — an old method to deviate rocks but a new testing method for developing a design concept

2016· article· en· W2611152713 on OpenAlexaboutno aff
James Glover, Duncan C. Wyllie, Roland Bucher

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicLandslides and related hazards
Canadian institutionsnot available
Fundersnot available
KeywordsRockfallAttenuator (electronics)Deflection (physics)GeologyGeotechnical engineeringStructural engineeringComputer scienceEngineeringAttenuationLandslideOptics

Abstract

fetched live from OpenAlex

Rockfall attenuator systems, also known as hybrid rockfall barriers or hanging nets, are a widely applied rockfall mitigation method. Although they are conceptually a straight forward design, to–date, a definitive design guide remains elusive. Among some of the first testing of these systems e.g. Glover et al. 2012, a boarder understanding of the key mechanics, with which they function, has been attained. For example, the process of deflecting rocks to increased ground contact and catching and tangling of the rocks in the attenuator systems has been observed. Nonetheless, these observations remain to be quantified in detail, so they could be developed into a design guide. Addressing this need, a joint testing program is being carried out by Wyllie & Norrish Rock Engineers Ltd. and Geobrugg North America, to measure and validate the performance of rockfall attenuator systems. The preliminary stages of full-scale testing have been conducted at the Nicolum Quarry in Hope, British Columbia, Canada. Tests were conducted using natural rocks of different sizes, <0.5 m³, and steel reinforced concrete cubes, with dimensions of 0.42 and 1.0 m³, dropped into a slope, with a height potential of 60 m, leading to impact with the attenuator netting. The preliminary tests were documented with high speed cameras and load cells measuring the forces in the support ropes. The videos were analysed to determine changes in the rock’s kinetic energy, and the deflection of the attenuator net. The objectives of the tests were to find how to optimise system designs to minimise the impact energy absorbed by the netting and support structure. The maximum impact energy achieved during the preliminary test series was in the order of 600 kJ. Following these initial tests, a second test series was performed in January 2016. Additional to the high speed video and load cell measurements, some experiments involved a novel rock motion logger integrated into the blocks centre of mass. The rock motion logger permitted the rocks accelerations and rotations to be captured during contact with the netting. For the first time, measurements of this kind have been made for attenuators and offer detailed insights into the mechanics of the attenuation process. Further work, in the development of rockfall attenuator systems, is to analyse existing systems, which have been impacted by natural rocks, gaining an estimate of their performance. Alongside these observations, the experimental data is being analysed in detail to refine a new design concept of attenuator barriers. Further results will be given in this paper.

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)0.004
metaresearch head score (Gemma)0.004
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.003
Scholarly communication0.0020.003
Open science0.0020.002
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0100.003

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 head0.060
GPT teacher head0.316
Teacher spread0.255 · 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

Citations2
Published2016
Admission routes1
Has abstractyes

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