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Record W2206203161 · doi:10.1139/cgj-2014-0437

Effect of support characteristics on the earth pressure in a jointed rock mass

2015· article· en· W2206203161 on OpenAlexvenueno aff
Moorak Son, Solomon Adedokun

Bibliographic record

VenueCanadian Geotechnical Journal · 2015
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Stabilization
Canadian institutionsnot available
FundersDaegu University
KeywordsGeotechnical engineeringRock mass classificationJoint (building)GeologyLateral earth pressureShear strength (soil)StratumGeological Strength IndexStiffnessDirect shear testShear (geology)Parametric statisticsFinite element methodDiscrete element methodMagnitude (astronomy)Structural engineeringEngineeringSoil waterMechanicsMathematicsPetrologySoil science

Abstract

fetched live from OpenAlex

This study examines the magnitude and distribution of earth pressures against a support system in a jointed rock mass according to the support characteristics (strut stiffness and spacing), different rock types, and joint conditions (joint shear strength and joint inclination angle). A series of numerical parametric analyses were performed after verifying the numerical approach through a physical model test. These analyses were based on the discrete element method, which can take into account the joint characteristics of the rock strata and the interactions between the ground and the retaining structure. The results were compared with Peck’s earth pressure for soil ground, which showed that the magnitude and distribution of earth pressure are strongly affected by the support characteristics, rock types, and joint conditions, and that the earth pressure in the rock stratum can be significantly different from that in the soil ground. The results suggest that the support characteristics, including the rock types and joint conditions, are important factors affecting the earth pressure, and should be considered for the safe and economic design and construction of retaining structures in a jointed rock mass.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.053
Threshold uncertainty score0.431

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.008
GPT teacher head0.194
Teacher spread0.186 · 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

Citations15
Published2015
Admission routes1
Has abstractyes

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