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Record W2151600140 · doi:10.1139/t02-073

Prediction and analysis of subsidence induced by shield tunnelling in the Madrid Metro extension

2002· article· en· W2151600140 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueCanadian Geotechnical Journal · 2002
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsExcavationSettlement (finance)ShieldSubsidenceGeotechnical engineeringGround movementEngineeringCivil engineeringTunnel constructionMining engineeringGeologyComputer science

Abstract

fetched live from OpenAlex

The development of tunnelling projects under heavily populated cities has been rapidly increasing around the world during the last decades. Since tunnel construction can have disastrous effects on buildings, structures, and utilities near the excavation, construction methods have necessarily to provide maximum safety inside and outside the tunnel. To predict and correct dangerous ground movements due to the tunnelling works, the authors developed a numerical model to simulate the earth pressure balance (EPB) excavation procedure and injection to complement some deficiencies found in previous analytical or empirical subsidence estimating procedures. This model takes into account the full excavation sequence and has been validated by a large amount of monitoring data from the previous Madrid Metro extension. In the present paper, several predictive methods are used to predict the ground movements generated during a new Madrid Metro extension project consisting of 48 km of tunnel (1999–2003). At the end of the works the results will be compared with data from monitored sections placed in all five cities linked by the extension. Conclusions about the applicability and accuracy of the methods will be established with the aim of helping researchers and engineers in their future projects.Key words: ground movements, monitoring, numerical modelling and analysis, settlement, tunnels.

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.

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: Empirical
Teacher disagreement score0.044
Threshold uncertainty score0.424

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
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 head0.018
GPT teacher head0.193
Teacher spread0.175 · 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