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Record W4366976845 · doi:10.1139/cgj-2022-0384

Field study on behavior of load distributive compression anchor installed in weathered rock and soft rock

2023· article· en· W4366976845 on OpenAlexvenueno aff
Gyu-Beom Shin, Bum-Hee Jo, Sung‐Ryul Kim, Choong‐Ki Chung, Sung-Ha Baek

Bibliographic record

VenueCanadian Geotechnical Journal · 2023
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsnot available
FundersNational Research Foundation of KoreaSeoul National University
KeywordsGroutGeotechnical engineeringRock boltGeologyUltimate tensile strengthRock mass classificationCompression (physics)AnchoringStructural engineeringEngineeringMaterials scienceComposite material

Abstract

fetched live from OpenAlex

As a load distributive compression anchor (LDCA) consists of multiple anchor bodies and unbonded tendons, the load applied to the tendon is transmitted to grout and ground by the movement of each anchor body installed along the anchor length. Therefore, the load transfer behavior of LDCA is affected by the interference between adjacent anchor body, and the effect of multiple anchor bodies should be considered in the LDCA design. This study performed a series of pull-out field tests on LDCAs installed in soft rock. LDCAs were designed to have various number and spacing of anchor bodies to investigate the effect of multiple anchor bodies. Furthermore, the test results were compared with the results of pull-out field tests conducted on weathered rock to examine the effect of ground conditions. The load-displacement relationship showed that the grout failure occurred in the LDCA installed in soft rock, and thus the ultimate bearing capacity was smaller than that of the LDCA installed in weathered rock. Additionally, the grout axial load distribution indicated that the LDCA installed in soft rock expressed tensile stress in the grout due to the effect of multiple anchor bodies leading to the tensile failure on the grout.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.608
Threshold uncertainty score0.673

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 integrity0.0000.001
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 head0.011
GPT teacher head0.228
Teacher spread0.218 · 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

Citations7
Published2023
Admission routes1
Has abstractyes

Explore more

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