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Record W2342613692 · doi:10.15273/ijge.2016.02.008

Impact Force of Boulders Conveyed in Debris Flows on Bridge Piers and Collision Protection Measures

2016· article· en· W2342613692 on OpenAlexvenueno aff
Quancai Wang, Jian Chen, Hao Wang, Qunli Zhan

Bibliographic record

VenueInternational journal of geohazards and environment · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicLandslides and related hazards
Canadian institutionsnot available
FundersChinese Academy of Sciences
KeywordsDebrisDebris flowPierImpactBridge (graph theory)CollisionFlow (mathematics)CantileverGeotechnical engineeringInertiaMagnitude (astronomy)MechanicsStructural engineeringGeologyComputer scienceEngineeringPhysicsClassical mechanics

Abstract

fetched live from OpenAlex

It is quite common in highway engineering that building a bridge across a debris flow gully to prevent roadbed from damage by strike of debris flows. As bridges are designed with the purpose to protect their piers against debris flows, it is crucially important for engineers to determine the magnitude of the Impact Force Exerted by Boulders Transported (IFEBT) in rush torrents. In view of the theory of energy conservation, a formula is introduced in this paper to calculate the IFEBT with appreciable improvement compared to the commonly used equations, in which only the two types of structures (cantilever and simply supported) are taken into account in modelling. The Thornton elastoplastic contact criterion is included in the formula in consideration of buffer effect of two-phase debris flow on bridge piers and dynamic responses of bridge upper-structure. Comparisons on calculation accuracy are elaborately made between our improved formula and previous methods in a case study of Den Jigou Bridge. It is found that according to our proposed method the values of IFEBT obtained in circumstances of varied velocity and boulders sizes are lower than the ones calculated by previous methods. Providing the depth of debris flow body in the two-phase condition is up to 2.4 cm, there is a considerable decrease of 21% in the value of IFEBT. In the meantime, a decrease of 1.4% in the IFEBT value is attained in consideration of the inertia force of the bridge’s upper-structure. In addition, it is feasible to dissipate impact energy of IFEBT when low elasticity modulus and high decrement material are used in practical engineering.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.562
Threshold uncertainty score0.329

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 head0.010
GPT teacher head0.240
Teacher spread0.230 · 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 designObservational
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
Published2016
Admission routes1
Has abstractyes

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