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Record W2805676410 · doi:10.11159/iccste18.145

Evaluating the Response of Cable-Stayed Bridges Subjected toDelayed Seismic Time-Histories Using Multi-Support Excitation

2018· article· en· W2805676410 on OpenAlexaffvenueabout
Bashar Hariri, Lan Lin

Bibliographic record

VenueProceedings of the International Conference on Civil, Structural and Transportation Engineering · 2018
Typearticle
Languageen
FieldEngineering
TopicStructural Engineering and Vibration Analysis
Canadian institutionsConcordia University
Fundersnot available
KeywordsStructural engineeringGeologySeismologyExcitationEngineeringComputer scienceElectrical engineering

Abstract

fetched live from OpenAlex

Seismic Spatially varying loads for cable-stayed bridges are either neglected or poorly addressed in the most of the current bridge design codes around the world.According to Canadian Highway Bridge Design Code (CHBDC) it is the responsibility of the designer to check the effect of the spatially varying loads while no details are provided.Given this, the objective of this study is to evaluate the effects of multi-support excitation on the response of a cable-stayed bridge.For the purpose of the study, a well-known Quincy Bayview bridge located in Illinois, USA is under examination.The results from the study show that the seismic excitation in the longitudinal direction has caused resonance in the bridge vertical direction, which is due to the delay of the finite shear-wave velocity of the propagation soil at the pier supports.Furthermore, it is observed that the resonance takes place not only in soft soil but also in stiff soil depending on the frequency content of the ground motion, and the modal properties of the bridge.A formula for dominant shearwave velocity for resonance is proposed along with a method to develop the velocity vs response curve.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.036
GPT teacher head0.281
Teacher spread0.244 · 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

Citations1
Published2018
Admission routes3
Has abstractyes

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