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Record W4362608291 · doi:10.1002/suco.202200238

Inversion model for mechanical status of longitudinal joints in shield tunnel

2023· article· en· W4362608291 on OpenAlexaff
Xiaohui Zhang, Lizhi Qi, Shunhua Zhou, Zhiyao Tian

Bibliographic record

VenueStructural Concrete · 2023
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsHudbay Minerals (Canada)University of Toronto
FundersNational Natural Science Foundation of ChinaNatural Science Foundation of Shanghai
KeywordsShieldStructural engineeringJoint (building)Deformation (meteorology)Inversion (geology)Rotation (mathematics)Internal forcesEngineeringGeologyComputer science

Abstract

fetched live from OpenAlex

Abstract The mechanical status of longitudinal joints in shield tunnel during operation is an important index for structural evaluation. For most of the tunnels that are not pre‐embedded with monitoring elements, only a few indicators can be measured directly during daily inspections. To comprehensively evaluate the deformation and mechanical status of the tunnel, an inversion model of the longitudinal joints, which is based on the measured convergence deformation of the shield tunnel, is proposed. First, the joint rotation deformation is determined using the successive rotation method. Then, the internal force of the joint is obtained based on the joint deformation. The two steps of the model are coupled with the iterative process for obtaining the accurate position of the joint neutral axis. The proposed model is verified by full‐scale ring test. It provides a new approach for obtaining the tunnel mechanical status and thus lays a theoretical foundation for decision‐making with regard to tunnel maintenance.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.016
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.024
GPT teacher head0.247
Teacher spread0.223 · 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 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

Citations7
Published2023
Admission routes1
Has abstractyes

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