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Record W4405733905 · doi:10.1002/eer2.99

Seismic fragility and life‐cycle loss analyses of high‐speed railway bridges supported by segmentally assembled round‐end hollow piers

2024· article· en· W4405733905 on OpenAlexaff
Zhiyuan Hu, Tieyi Zhong, Hongyu Qin, Xin-Lin Ji, Lianxu Zhou

Bibliographic record

VenueEarthquake Engineering and Resilience · 2024
Typearticle
Languageen
FieldEngineering
TopicStructural Behavior of Reinforced Concrete
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
FundersChina Railway
KeywordsFragilityEngineeringForensic engineeringStructural engineeringMaterials sciencePhysics

Abstract

fetched live from OpenAlex

Abstract The shortcomings of segmentally assembled round‐end hollow‐section piers (SRHPs), such as weak segment joints and poor energy‐dissipation capacity, have limited their application in high‐intensity earthquake regions. Therefore, this article particularly focuses on the seismic performance of SRHPs. Three high‐speed railway bridges are designed, which are equipped with three different types of round‐end hollow‐section piers (RHPs): cast‐in situ RHP (CRHP), segmentally assembled RHP with energy‐dissipation bar (E‐SRHP), and segmentally assembled RHP with low‐yield point steel connection buckles (L‐SRHP). Subsequently, three nonlinear finite element models of the corresponding bridges were established and validated by quasi‐static test results. Furthermore, compared to CRHP, the seismic performance of E‐SRHP and L‐SRHP was evaluated from the perspectives of seismic fragility and life‐cycle seismic loss. Research results revealed that the seismic fragility performance of the bridge with L‐SRHP is the best among all three bridges, followed by the bridge with E‐SRHP. Notably, the life‐cycle cost considering seismic loss for E‐SRHP is 83% of that for CRHP, whereas L‐SRHP is only 65% of that for CRHP. In general, the high‐speed railway bridge supported by L‐SRHP possesses the best seismic performance and economic benefits among the three bridges, which shows promising application prospects in high‐intensity earthquake‐prone regions.

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 categoriesMeta-epidemiology (narrow)
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 score0.238
Threshold uncertainty score1.000

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 head0.008
GPT teacher head0.234
Teacher spread0.227 · 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.

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

Citations2
Published2024
Admission routes1
Has abstractyes

Explore more

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