MétaCan
Menu
Back to cohort
Record W2520576534 · doi:10.1139/cgj-2016-0128

Impact mechanisms of granular and viscous flows on rigid and flexible barriers

2016· article· en· W2520576534 on OpenAlexvenueno aff
Charles Wang Wai Ng, Dan Song, Clarence Edward Choi, L.H.D. Liu, J.S.H. Kwan, R.C.H. Koo, W. K. Pun

Bibliographic record

VenueCanadian Geotechnical Journal · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicLandslides and related hazards
Canadian institutionsnot available
FundersHong Kong GovernmentHong Kong University of Science and Technology
KeywordsGeotechnical engineeringShearing (physics)CentrifugeMechanicsGranular materialFlow (mathematics)GeologyMaterials sciencePhysics

Abstract

fetched live from OpenAlex

Structural countermeasures such as rigid and flexible barriers are commonly installed in mountainous regions to intercept mass-wasting processes. Without sufficient and reliable comparable physical data, the study of impact mechanisms remains difficult and not well understood. In this study, a newly developed flexible model barrier together with a rigid barrier are used to simulate either dry granular or viscous liquid impacts on these model barriers in a geotechnical centrifuge. The novel flexible barrier is made of four instrumented cables controlled by spring mechanisms to replicate a bilinear prototype loading response. Tests revealed that regardless of barrier type, both dry granular and viscous flows could have similar frontal dynamic impact coefficients around unity. Compared with the kinetic energy of flow mass (∼10 MJ), only 249 kJ of flexible barrier energy capacity was mobilized. This implies that debris-resisting barriers may only be required to intercept the dynamic flow front as the subsequent flow energy may mainly be dissipated through internal shearing. Attributing to the large deformation of the flexible barrier, the granular static load acting on the flexible barrier could be 39% lower than that on the rigid barrier, resulting in an active failure mode and a lower earth pressure.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.537
Threshold uncertainty score0.999

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)0.0020.000

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 head0.005
GPT teacher head0.210
Teacher spread0.205 · 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.

Study designOther design
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

Citations137
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Geotechnical JournalSame topicLandslides and related hazardsFrench-language works237,207