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Record W2612205280 · doi:10.15273/ijge.2017.01.002

IMPLEMENTATION OF SEISMIC WAVE THEORY IN HOMOGENEOUS SLOPE FRACTURE ANALYSIS

2017· article· en· W2612205280 on OpenAlexvenueno aff
Zhenlin Chen, Nanqi Huang

Bibliographic record

VenueInternational Journal of Georesources and Environment · 2017
Typearticle
Languageen
FieldEngineering
TopicRock Mechanics and Modeling
Canadian institutionsnot available
FundersState Key Laboratory of Geohazard Prevention and Geoenvironment ProtectionNational Natural Science Foundation of China
KeywordsEarthquake shaking tableHomogeneousStability (learning theory)Fracture (geology)GeologySurface (topology)Function (biology)Geotechnical engineeringSeismic waveComputer scienceSeismologyMathematicsGeometry

Abstract

fetched live from OpenAlex

For slope stability analysis, it is important to efficiently and directly detect potential sliding surfaces and fracture areas. Since slope failure is not induced by one broken surface but a region where may contain a number of cracks. In order to extend the application of the wave theory analysis method in computing slope dynamic response and reduce the cost, the concept of Specific Effective Contributing Region (SECR) is proposed in this research to assess a homogeneous rock slope’s stability. By studying the dynamic response of all meshed surfaces based on seismic motion synthesis function in the SECR, the potential fracture region was detected efficiently and directly. Moreover, the relationship between the location of sliding surface and the frequency of excitation was investigated. All results obtained by this theoretical procedure are consistent with observations from a shaking table test. The proposed method can be further extended to study failure mechanisms of layered rock slopes induced by earthquakes.

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 score0.351
Threshold uncertainty score0.225

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 head0.007
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 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

Citations0
Published2017
Admission routes1
Has abstractyes

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