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Seismic Analysis of Long-Span Cable-Stayed Bridges by an Integrated Finite Strip Method

2015· article· en· W2198053671 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Bridge Engineering · 2015
Typearticle
Languageen
FieldEngineering
TopicVibration and Dynamic Analysis
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsStructural engineeringBridge (graph theory)Span (engineering)DeckFinite element methodFinite strip methodEngineeringSeismic analysis

Abstract

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This paper provides a very efficient, integrated framework for seismic analyses of long-span cable-stayed bridges. The efficiency comes from the dramatic reduction in formation time and the degrees of freedom (DOF) associated with the structure, using the integrated finite strip method (IFSM) along with the application of a very robust and efficient time history method (THM) using the Newmark scheme for dynamic analysis of the bridge structure. The previous versions of the finite strip method are limited to modeling the bridge deck only, whereas other structural components are replaced by assumed boundary conditions. Using the IFSM, all components of the long-span cable-stayed bridge can be modeled in a unified system, and consequently, the real dynamic behavior including the interactions between deck, piers, and cables can be perfectly considered. To verify the solution, the geometric and dynamic properties of the Kap Shui Mun (KSM) Bridge, as a real example of a long-span cable-stayed bridge, are derived by the proposed finite strip method. Then, the seismic response of KSM Bridge under uniform and nonuniform earthquake loadings is investigated by using the THM. The results show that the IFSM can be applied successfully for seismic analysis of long-span cable-stayed bridges, and the analysis can be performed in a minimal amount of time.

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.

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Simulation or modelinghigh
gptno category
Domain: not available · Genre: Methods
About the Canadian research system: no · About a Canadian topic: no
Simulation or modelingmedium
models agreeAgreement compares identical category sets and study designs across arms.

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)0.001
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: none
Teacher disagreement score0.946
Threshold uncertainty score0.895

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
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 head0.017
GPT teacher head0.253
Teacher spread0.236 · 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