MétaCan
Menu
Back to cohort

Modeling the Impact of a Falling Rock Cluster on Rigid Structures

2017· article· en· W2768530041 on OpenAlexafffund
Ge Gao, Mohamed A. Meguid

Bibliographic record

VenueInternational Journal of Geomechanics · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicLandslides and related hazards
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaFaculty of Engineering, McGill University
KeywordsRockfallDiscrete element methodGeologyCluster (spacecraft)Geotechnical engineeringFalling (accident)Surface finishMechanicsGeometryMaterials sciencePhysicsComputer scienceLandslideMathematics

Abstract

fetched live from OpenAlex

Rockfall is a common geological hazard in mountainous areas and can pose great danger to people and properties. Understanding the impact forces induced by a single rock or a rock cluster on retaining structures is considered key in the analysis and design of protection barriers. This study presents the results of small-scale laboratory experiments conducted to measure the impact forces induced by a group of rocks moving down a rough slope on a barrier wall. The effect of slope inclination angle and wall location on the impact pressure acting on the wall was examined. A three-dimensional discrete element model was then proposed and used to study the behavior of the rock cluster under different geometric conditions. Rocks were modeled using polydisperse clumps in which each clump consisted of several overlapping spherical particles to account for the shape effect of the falling rocks. First, the model was validated by comparing the measured and calculated forces, and then, it was used to investigate the role of different material and geometric parameters on the impact behavior. Conclusions were made regarding the role of modeling the irregular rock shapes and the roughness of the slope surface on the behavior impacted by the travel mode for different slope angles.

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 score0.027
Threshold uncertainty score0.394

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 science0.0010.000
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 head0.015
GPT teacher head0.288
Teacher spread0.274 · 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

Citations46
Published2017
Admission routes2
Has abstractyes

Explore more

Same venueInternational Journal of GeomechanicsSame topicLandslides and related hazardsFrench-language works237,207