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Record W4285195550 · doi:10.2749/prague.2022.1590

Dynamic Analysis and Innovative Design of a Kilometer-Long Highway Bridge under Extreme Landslide Generated Wave Loading

2022· article· en· W4285195550 on OpenAlexaff
Jianping Jiang, Sean O’Hagan, Rashedul Kabir, Jacek Doniec, Qi Zhang

Bibliographic record

VenueReport · 2022
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsWSP (Canada)
Fundersnot available
KeywordsPierBridge (graph theory)PileStructural engineeringDisplacement (psychology)Geotechnical engineeringLandslideEngineeringWave loadingBeam bridgeGeologyMarine engineeringCivil engineeringSpan (engineering)

Abstract

fetched live from OpenAlex

This paper focuses on the dynamic analysis and innovative design of a 1042 m long highway bridge under extreme Landslide Generated Wave (LGW) impact loading. The proposed bridge crosses a deep and wide river valley which will become a part of a large reservoir after a hydroelectric dam is constructed. A rare extreme landslide event could occur and generate fast-moving waves in the reservoir impinging large forces on the bridge. To investigate the wave impact loadings on the bridge, three-dimensional Computational Fluid Dynamics (CFD) modeling of wave propagations were completed by hydrotechnical specialists, which were then used to analyze the structural dynamic response and to obtain force and displacement demands for structural design via a time- stepping analysis of the bridge structure. The innovative design included the use of non-uniform single circular-shaped pier columns supported on a hexagon-shaped pile cap with axisymmetrically configured rock-socketed steel pipe piles to address directional uncertainty associated with LGW. The bridge is currently under construction.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

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 integrity0.0010.000
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 head0.026
GPT teacher head0.226
Teacher spread0.200 · 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 source (direct Gemma or distilled Codex), 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
Published2022
Admission routes1
Has abstractyes

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