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Record W1965495367 · doi:10.1193/1.4000089

Seismic Vulnerability Assessment of a Multi‐Span Continuous Highway Bridge Fitted with Shape Memory Alloy Bars and Laminated Rubber Bearings

2012· article· en· W1965495367 on OpenAlexaff
M. A. Rahman Bhuiyan, M. Shahria Alam

Bibliographic record

VenueEarthquake Spectra · 2012
Typearticle
Languageen
FieldEngineering
TopicConcrete Corrosion and Durability
Canadian institutionsOkanagan University CollegeUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
Fundersnot available
KeywordsPierStructural engineeringBridge (graph theory)SMA*EngineeringSpan (engineering)Bearing (navigation)FragilityVulnerability assessmentDisplacement (psychology)Shape-memory alloyVulnerability (computing)Geotechnical engineeringComputer science

Abstract

fetched live from OpenAlex

This study performs seismic vulnerability assessment in the longitudinal direction of a three‐span continuous highway bridge, restrained by shape memory alloy (SMA) bars and isolated with laminated rubber bearings. The analytical simulation method based on incremental dynamic analyses is used in evaluating the seismic fragility functions of the bridge components (pier and isolation bearing) and the system. A two‐dimensional finite element model scheme with nonlinear force‐displacement relationships is used for the bridge piers and bearings. This study shows that the bridge piers with SMA bars have led to relatively higher seismic vulnerability over the bridge piers without SMA bars, which is also reflected in the bridge system. The isolation bearings with SMA bars have revealed comparatively less seismic vulnerability than those without SMA bars. From the numerical results, it is recognized that the failure probability of the bridge system is dictated by the bridge pier over the isolation bearing.

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 score0.004

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)0.0000.000

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 head0.014
GPT teacher head0.239
Teacher spread0.225 · 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

Citations71
Published2012
Admission routes1
Has abstractyes

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