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Record W2887987505 · doi:10.1155/2018/5428670

An Experimental Study on Mechanical Behavior of Parallel Joint Specimens under Compression Shear

2018· article· en· W2887987505 on OpenAlexaff
Fei Wang, Ping Cao, Yu Chen, Qing-peng Gao, Zhu Wang

Bibliographic record

VenueAdvances in Civil Engineering · 2018
Typearticle
Languageen
FieldEngineering
TopicRock Mechanics and Modeling
Canadian institutionsGeomechanica (Canada)
FundersFundamental Research Funds for the Central UniversitiesState Key Laboratory for GeoMechanics and Deep Underground EngineeringChina Postdoctoral Science FoundationNational Natural Science Foundation of China
KeywordsMaterials scienceShear (geology)Failure mode and effects analysisJoint (building)Direct shear testComposite materialShear stressStructural engineeringAcoustic emissionEngineering

Abstract

fetched live from OpenAlex

In order to investigate the influence of the joint on the failure mode, peak shear strength, and shear stress‐strain curve of rock mass, the compression shear test loading on the parallel jointed specimens was carried out, and the acoustic emission system was used to monitor the loading process. The joint spacing and joint overlap were varied to alter the relative positions of parallel joints in geometry. Under compression‐shear loading, the failure mode of the joint specimen can be classified into four types: coplanar shear failure, shear failure along the joint plane, shear failure along the shear stress plane, and similar integrity shear failure. The joint dip angle has a decisive effect on the failure mode of the specimen. The joint overlap affects the crack development of the specimen but does not change the failure mode of the specimen. The joint spacing can change the failure mode of the specimen. The shear strength of the specimen firstly increases and then decreases with the increase of the dip angle and reaches the maximum at 45°. The shear strength decreases with the increase of the joint overlap and increases with the increase of the joint spacing. The shear stress‐displacement curves of different joint inclination samples have differences which mainly reflect in the postrupture stage. From monitoring results of the AE system, the variation regular of the AE count corresponds to the failure mode, and the peak value of the AE count decreases with the increase of joint overlap and increases with the increase of joint spacing.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.091
Threshold uncertainty score0.820

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 head0.026
GPT teacher head0.292
Teacher spread0.266 · 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
Published2018
Admission routes1
Has abstractyes

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