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Record W3141295382

Challenges in Construction of Underwater Tunnels and Countermeasures

2014· article· en· W3141295382 on OpenAlexaff
Qian Qi-h

Bibliographic record

VenueTunnel Construction · 2014
Typearticle
Languageen
FieldEngineering
TopicCivil and Geotechnical Engineering Research
Canadian institutionsImpact
Fundersnot available
KeywordsUnderwaterShieldEngineeringDrillImmersed tubeMining engineeringGeotechnical engineeringCivil engineeringMarine engineeringGeologyMechanical engineering
DOInot available

Abstract

fetched live from OpenAlex

The advantages and disadvantages of three major underwater tunnel construction methods,including drill and blast method,TBM /shield method and immersed tube method,are presented. Comparison and contrast is made among the three mentioned construction methods in terms of geological conditions, water flow velocity, water surface transportation,cover depth,number of traffic lanes,water leakage,work quantity,construction period,construction safety and construction cost. Finally,major challenges in the construction of underwater tunnels and their countermea sures are analyzed. In the construction of immersed tunnels,the erosion of the riverbed,the phenomenon of the local parts of tube elements being higher than the riverbed and the increasing cover depth are big challenges; in the TBM /shield method,boring under shallow cover,wearing of cutter heads and cutting tools,increasing cover depth,damage of main bearing and boring in fault and fracture zones are big challenges.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.003
Open science0.0020.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.026
GPT teacher head0.223
Teacher spread0.197 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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
Published2014
Admission routes1
Has abstractyes

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