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Application of a bonded critical state model to design tunnel support for rockmass bulking

2021· article· en· W3198285613 on OpenAlexaff
Matteo Oryem Ciantia, Marcos Arroyo, Peter Kaiser

Bibliographic record

VenueIOP Conference Series Earth and Environmental Science · 2021
Typearticle
Languageen
FieldEngineering
TopicRock Mechanics and Modeling
Canadian institutionsLaurentian University
FundersMinisterio de Ciencia e Innovación
KeywordsShotcreteGeotechnical engineeringRebarStructural engineeringParametric statisticsEngineeringFinite element methodOverburden pressureGeology

Abstract

fetched live from OpenAlex

Abstract Gabion-type support is a favoured option to restrain bulking in pillar walls of mine footprint tunnels. It uses closely spaced short reinforcements in tunnel walls (typically fully grouted rebar) in combination with surface support (rock fragment retention systems such as shotcrete, weld wire mesh, straps, etc.). The system is installed while the rock is still mostly intact and is conceived to maintain support capacity even when, the rock attains a fully fragmented state, acting then like a gabion or earth-reinforced type retaining wall. In this paper the interaction between the support system and the highly stressed pillar walls is investigated numerically by means of finite element analyses within the framework of displacement-based design. Because the material response should capture the passage from intact rock to fully fragmented state, an advanced elasto-plastic bonded constitutive model was adopted as a simulation framework. The model is calibrated to replicate the mechanical behaviour of Bursnip Sandstone and Amarelo Pais Granite. These two rocks were selected because of high quality triaxial tests results from the literature. After showing the good performance of the model to reproduce both low and high pressure triaxial compression behaviour an extensive parametric study investigating the effects of bolt types on gabion response is presented.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.637
Threshold uncertainty score0.395

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 head0.024
GPT teacher head0.227
Teacher spread0.204 · 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 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

Citations1
Published2021
Admission routes1
Has abstractyes

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