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Record W3139526224 · doi:10.1155/2021/6699252

Investigation of Water Leakage of Metro Segments Caused by Metro Underpass Structures

2021· article· en· W3139526224 on OpenAlexaff
Xiao Liang, Taiyue Qi, Zhiyi Jin, Pengtao Chen, Bo Lei, Wangping Qian, Wang Xian-yu

Bibliographic record

VenueAdvances in Civil Engineering · 2021
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsMinistry of Transportation of Ontario
FundersNational Natural Science Foundation of China
KeywordsLeakage (economics)Water leakageStructural engineeringForensic engineeringMetro stationMaterials scienceCivil engineeringEngineeringEnvironmental scienceComposite materialTransport engineering

Abstract

fetched live from OpenAlex

Water leaking into metro segments is related directly to the presence of underground water. However, to date, the underground confined water channels formed by metro underpass reinforcement structures have not been considered as causes of the problem. In the present study, based on field investigations and geological data, confined water is identified as the source of segment leakage on Line 5 of Hangzhou Metro in China. It is then hypothesized that the problem stems from an underground confined water channel formed under the reinforced structure of the subway. Numerical simulations predict a settlement difference of 8–10 mm between the reinforcement area and the weak strata, and field monitoring data show this difference to be 8.62 mm. This consistency between the numerical simulations and the field measurements verifies the hypothesis. Finally, given the causes of underground confined water channels, corresponding technical measures such as piecewise secondary grouting are proposed to cut them off and avoid metro‐segment leakage. The present research results contribute to the causes of and the laws governing the metro‐segment leakage that occurs at subway stations and other reinforced structures.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.664
Threshold uncertainty score0.910

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 head0.006
GPT teacher head0.202
Teacher spread0.196 · 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 designBench or experimental
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
Published2021
Admission routes1
Has abstractyes

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