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Record W4396601751 · doi:10.1139/cgj-2024-0010

Geostress-associated settlements of a raft-foundation building due to shield tunnelling in soft ground

2024· article· en· W4396601751 on OpenAlexvenueno aff
Tianqi Zhang, Zhenkun Li, Ruikun Wang, Gang Zheng, Huayang Lei, Qi Fan

Bibliographic record

VenueCanadian Geotechnical Journal · 2024
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsGeotechnical engineeringFoundation (evidence)ShieldHuman settlementGeologyQuantum tunnellingForensic engineeringEngineeringMaterials scienceGeography

Abstract

fetched live from OpenAlex

The stress state of soil may affect the building settlements induced by tunnelling, which, however, has not been well understood. In this study, three dimensional numerical analyses combined with in situ measurements were performed to investigate the geostress-associated settlements of a raft-foundation building due to tunnelling in soft ground. Basically, two types of geostress fields were investigated: the first type considered the effect of additional stress generated in the foundation soil (FAS) due to building weight, while in the second type, a sequential twin tunnelling was presumed, and the effect of additional soil stress induced by the first tunnel (TAS) on the building response to the second tunnel was considered. The results indicated that FAS may aggravate the stress release of the foundation soil, and thus gave rise to a larger building settlement or inclination. In the sequential tunnelling process, the effect of TAS can be more complex: when the first tunnel lowered the stress of foundation soil, TAS effect of the first tunnel may help reduce the building settlements induced by the second tunnel; otherwise, it may aggravate building settlements. In addition to TAS effect, the sheltering effect was also found to play an important part in twin tunnelling.

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)0.001
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: none
Teacher disagreement score0.556
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.009
GPT teacher head0.222
Teacher spread0.213 · 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

Citations3
Published2024
Admission routes1
Has abstractyes

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