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Record W2952249676 · doi:10.1002/nag.2974

Discrete element analysis of the influence of bolt pattern and spacing on the force‐displacement response of bolted steel mesh

2019· article· en· W2952249676 on OpenAlexaff
Cong Xu, Dwayne D. Tannant, Wenbo Zheng

Bibliographic record

VenueInternational Journal for Numerical and Analytical Methods in Geomechanics · 2019
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Analysis
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
FundersChina Scholarship CouncilChina Postdoctoral Science Foundation
KeywordsStructural engineeringDeformation (meteorology)Finite element methodDisplacement (psychology)Wire meshBolted jointEngineeringUltimate tensile strengthFastenerMaterials scienceComposite material

Abstract

fetched live from OpenAlex

Summary Steel wire mesh held by a pattern of bolts can be used to stabilize a rock slope. Knowledge of the force‐displacement response of steel mesh is essential in the design of this support system. Laboratory tests have been used to test mesh that is held to rigid steel frames. These testing conditions differ from how steel mesh is held in the field by bolts. The existing laboratory test data may underestimate the deformation and overestimate the load‐bearing capacity of the steel mesh. This paper focuses on the response of the high tensile strength steel mesh held by commonly used bolt patterns. Discrete element models were used to study the force‐displacement response of the steel mesh pinned by bolts. The influences of different bolt patterns and varying ratios of bolt spacing on the effectiveness of steel wire mesh were analyzed. Relationships between the resistance force of steel mesh and bolt density at various mesh deformations were developed. These relationships can help engineers choose the bolt spacing and provide an estimate of the resistance force that a steel mesh can mobilize at a desired deformation limit for a given bolt pattern.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.438
Threshold uncertainty score0.297

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.012
GPT teacher head0.316
Teacher spread0.304 · 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

Citations11
Published2019
Admission routes1
Has abstractyes

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