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Record W2141524294 · doi:10.1139/cgj-2012-0298

Investigation of the long-term tunnel settlement mechanisms of the first metro line in Shanghai

2013· article· en· W2141524294 on OpenAlexvenueno aff
C.W.W. Ng, G.B. Liu, Qing Li

Bibliographic record

VenueCanadian Geotechnical Journal · 2013
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsGeotechnical engineeringSettlement (finance)AquiferGroundwaterServiceability (structure)Consolidation (business)Compression (physics)GeologyEnvironmental scienceEngineeringCivil engineeringMaterials science

Abstract

fetched live from OpenAlex

The long-term performance of a metro tunnel is obviously a major concern for the authority and designers responsible for its construction, as excessive tunnel settlement would affect the serviceability and safety of the entire metro system. In this study, measured long-term settlement of the 16.4 km long tunnel of Shanghai Metro Line 1 from 1994 to 2007 (a period of 12.5 years) is reported and discussed. The tunnel settlement was continuous with time, and reached a maximum of 288 mm. To assist in the investigation of the mechanisms of long-term tunnel settlement, records of groundwater pumping and subsurface soil compression between 1985 and 2007 are analyzed. Four possible causes — namely, effects of tunnel construction, cyclic loading due to running trains, secondary compression of soft clay, and groundwater pumping in sandy aquifers — are investigated. From the measurements of subsurface soil compression, it is revealed that the observed large tunnel settlement was mainly caused by the compression of sandy Aquifer IV due to groundwater pumping. The measured compression of sandy Aquifer IV accounted for about 65% of the maximum tunnel settlement of Line 1. Soil yielding due to an increase in effective stress was induced by a significant decline in groundwater level. The observed continuing settlement of the tunnel over the 12.5 years was the result of secondary compression (creep), which is a common feature of Aquifer IV at various locations in Shanghai.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.054
Threshold uncertainty score0.107

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.009
GPT teacher head0.180
Teacher spread0.170 · 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 designObservational
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

Citations136
Published2013
Admission routes1
Has abstractyes

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