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Record W2900667315 · doi:10.1139/cgj-2018-0470

Parametric study of fully grouted cable bolts subjected to axial loading

2018· article· en· W2900667315 on OpenAlexvenueno aff
Danqi Li, Hossein Masoumi, Serkan Saydam, Paul Hagan, Mostafa Asadizadeh

Bibliographic record

VenueCanadian Geotechnical Journal · 2018
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsnot available
FundersAustralian Coal Industry’s Research Program
KeywordsGroutBoreholeEmbedmentStiffnessStructural engineeringGeotechnical engineeringParametric statisticsRock boltEngineeringMaterials science

Abstract

fetched live from OpenAlex

The laboratory short encapsulation pull-out test (LSEPT) has been widely accepted as the most efficient method to characterize the mechanical behaviour of cable bolts under axial loading. In this study, a number of LSEPTs was performed on conventional (Plain SuperStrand) and modified (MW9S) cable bolts using the improved pull-out test design. The effects of several parameters including the uniaxial compressive strength (UCS) of the confining medium and grout and the borehole diameter on the mechanical behaviour of MW9S and Plain SuperStrand cable bolts were investigated. Response surface methodology (RSM) was employed to quantify the contribution of these parameters on the responses including peak and residual loads and initial stiffness. RSM revealed that the UCS of the confining medium is a key contributing factor to the mechanical behaviour of both cable bolts. Also, it was demonstrated that the borehole diameter had a negligible impact on the overall behaviour of the MW9S cable bolt while the peak load of the SuperStrand cable bolt was increased due to an increase in the diameter of the borehole. Finally, from a comparative analysis, it was confirmed that the improved design can better represent the field mechanical behaviour of cable bolts through maintenance of the embedment length during large deformation.

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 categoriesMeta-epidemiology (narrow)
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 score0.378
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.009
GPT teacher head0.210
Teacher spread0.201 · 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.

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

Citations39
Published2018
Admission routes1
Has abstractyes

Explore more

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