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Record W2896059470 · doi:10.1142/s179343111850015x

Seismic Performance of the Buckling-Restrained Brace Central Buckle for Long-Span Suspension Bridges

2018· article· en· W2896059470 on OpenAlexaff
Wei Guo, Jianzhong Li, Nailiang Xiang

Bibliographic record

VenueJournal of Earthquake and Tsunami · 2018
Typearticle
Languageen
FieldEngineering
TopicStructural Engineering and Vibration Analysis
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
FundersTongji UniversityNational Natural Science Foundation of China
KeywordsBuckleStructural engineeringBucklingSuspension (topology)BraceSpan (engineering)DamperGeologyEngineering

Abstract

fetched live from OpenAlex

In this paper, a novel central buckle composed of buckling-restrained braces (BRBs) is developed for long-span suspension bridges, and its preliminary design procedure is presented. Seismic performance of suspension bridges equipped with BRB central buckles is investigated and compared with those with conventional central buckles (e.g. rigid or flexible central buckles). Furthermore, the effect of BRB yield force, as well as the effectiveness of BRB central buckles combined with viscous dampers, is evaluated using parametric analyses. The results indicate that the BRB central buckle is more effective than other central buckles in reducing both the longitudinal girder displacements and force demands on towers during an earthquake. Furthermore, the combination of BRB central buckles and viscous dampers is a superior option for mitigating the seismic response of long-span suspension bridges.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

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 science0.0000.000
Research integrity0.0000.000
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 head0.007
GPT teacher head0.209
Teacher spread0.202 · 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 designBench or experimental
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

Citations14
Published2018
Admission routes1
Has abstractyes

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