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Record W4362554324 · doi:10.24425/ace.2023.144188

An early warning method for a slope based on the increment ratio of anchor cable internal force

2023· article· en· W4362554324 on OpenAlexaff
Jincai Feng, Jiaxin Chen, Jian Li, Jianhua Guo, Hongyong Qiu

Bibliographic record

VenueArchives of Civil Engineering · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicLandslides and related hazards
Canadian institutionsGeomechanica (Canada)
Fundersnot available
KeywordsWarning systemStructural engineeringInternal forcesEngineeringGeotechnical engineeringTelecommunications

Abstract

fetched live from OpenAlex

Prestressed anchor cables are active reinforcement to improve slope stability. However, the anchoring is not a permanent guarantee of stability, and the slope retains a potential risk of instability. From the perspective of the internal force of anchor cables, a new early warning method for the safety of the slope is provided, and a slope analysis model is established. With the increase in the strength reduction factor, the internal force increment curves of anchor cables under different prestresses are obtained. The point corresponding to strength reduction factors λ1 and λ2 represents a warning point. Key conclusions are drawn as follows: (1) The internal force of an anchor cable can be used to judge the stability of the slope strengthened by a prestressed anchor cable. (2) A warning index based on the internal force increment ratio of anchor cables is established. (3) The internal force increment ratio of anchor cables eliminates the influence of the initial prestress and is convenient for engineering applications.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.008
GPT teacher head0.226
Teacher spread0.219 · 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 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

Citations2
Published2023
Admission routes1
Has abstractyes

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