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Record W2884730508 · doi:10.15273/ijge.2018.02.005

Analysis of Abnormal Use of Cement Paste in Perfusion during Tunnel Construction using TBM

2018· article· en· W2884730508 on OpenAlexvenueno aff
Meng Wei, Ningxin Zhang, Yuan Tong

Bibliographic record

VenueInternational Journal of Georesources and Environment · 2018
Typearticle
Languageen
FieldEngineering
TopicTunneling and Rock Mechanics
Canadian institutionsnot available
Fundersnot available
KeywordsSlurryCementGeotechnical engineeringExcavationQuantum tunnellingGeologyMining engineeringMaterials scienceComposite material

Abstract

fetched live from OpenAlex

During underground tunnel construction, rock burst causes substantial problems and impacts on the perfusion material use. This article explores the influences of rock burst on a construction project in a highly complex geological area in Tibet, including segment oscillation, injection port design, segments sealing, and slurry leakage, which contribute to the abnormal use of cement slurry during TBM tunneling. After comprehensively examined the reasons for the abnormal cement slurry filling during the TBM tunneling, we reach the conclusions that, in this tunnel, it is normal for the cement slurry volume to exceed its originally designed volume. The multiple factors affecting the cement slurry perfusion can be classified into three categories, including geological factors such as high ground stress, rock burst, and the complexity of the tectonic structure, human-made factors such as imperfect segment design and infusion hole design, and mechanical factors such as the reserved deformation and excavation adjustment due to rock burst. Abnormal perfusion does not result in any decreased quality in the backfill perfusion. Based on these conclusions, this paper also proposes recommendations to solve the abnormal use of the cement filling during the tunneling.

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 score0.157
Threshold uncertainty score0.250

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 head0.011
GPT teacher head0.198
Teacher spread0.187 · 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

Citations0
Published2018
Admission routes1
Has abstractyes

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