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Record W2002783512 · doi:10.1139/t02-009

Time-based two dimensional modelling of NATM tunnelling

2002· article· en· W2002783512 on OpenAlexvenueno aff
Jong-Ho Shin, David M. Potts

Bibliographic record

VenueCanadian Geotechnical Journal · 2002
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsnot available
Fundersnot available
KeywordsNew Austrian Tunnelling methodQuantum tunnellingGeotechnical engineeringField (mathematics)EngineeringGeologyMathematicsExcavationPhysics

Abstract

fetched live from OpenAlex

A two dimensional model is commonly employed in practice for the analysis of tunnelling. Such analyses are computationally cheap and are useful for assessing the sensitivity of the problem to the construction method, studying the influence of varying soil conditions, and (or) finding appropriate locations for placing measuring instruments. However, simulating the three dimensional nature of tunnelling in two dimensions requires certain simplifications, including the use of empirical parameters to represent the construction sequence. In many cases the choice of parameter values are arbitrary and often not fully explained. In addition, the modelling methods are often only applicable for undrained or fully drained soil conditions where no time-dependent behaviour is involved during tunnel construction. In this paper an alternative two dimensional approach termed the "time-based modelling method" is proposed that can simulate both the three dimensional effects at the tunnel heading and the time-dependent behaviour during construction. It is proposed that the new approach is appropriate for the analysis of tunnelling in a relatively permeable soil and, as an example, the method is applied to the analysis of a new Austrian tunnelling method (NATM) tunnelling problem in decomposed granite soil. The results are compared with field data and excellent agreement is obtained.Key words: numerical modelling, time-dependent behaviour, NATM tunnelling, decomposed granite soil.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.489
Threshold uncertainty score0.848

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.014
GPT teacher head0.182
Teacher spread0.168 · 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

Citations10
Published2002
Admission routes1
Has abstractyes

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