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Record W2904293564 · doi:10.1139/cgj-2018-0075

Ground response to tunnelling incorporating soil reinforcement system

2018· article· en· W2904293564 on OpenAlexvenueno aff
Binh Thanh Le, R.N. Taylor

Bibliographic record

VenueCanadian Geotechnical Journal · 2018
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsGeotechnical engineeringCentrifugeHeading (navigation)ExcavationStiffnessSettlement (finance)Quantum tunnellingGeologyWind tunnelStructural engineeringEngineeringMaterials scienceComputer scienceGeodesy

Abstract

fetched live from OpenAlex

The forepole umbrella system (FUS) uses steel pipes installed from within a tunnel to provide a canopy above the tunnel heading that both increases stability and reduces tunnelling-induced ground movements. Although the system is known to be beneficial and has been used in a number of projects, there is little information on how key parameters including length and forepole stiffness combine to produce effective support. To investigate this, centrifuge tests incorporating the three-dimensional (3D) geometry of a tunnel heading in clay and the model FUS have been undertaken. The tunnel heading was supported by a pressurized rubber bag lining with excavation being simulated by a reduction in air support pressure. Image analysis was used to obtain subsurface ground movements and a newly developed 3D imaging system was used to measure the soil surface deformations accurately. The performance of the FUS and the influences of key FUS parameters were quantified via the settlement reduction factor. The results showed that the FUS, arranged in various settings, reduced the maximum surface settlement by 35%–75%. The effects of the FUS parameters on the reinforcing effectiveness are dependent on the ratio of cover depth to tunnel diameter. An optimum design arrangement of the FUS is suggested.

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)0.000
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.871
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.008
GPT teacher head0.194
Teacher spread0.186 · 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

Citations4
Published2018
Admission routes1
Has abstractyes

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