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Record W7001482779

Isolation and Damping Mitigation Strategies for Bridges in Western and Eastern North America

2012· article· en· W7001482779 on OpenAlexaboutno aff

Bibliographic record

VenuePolyPublie (École Polytechnique de Montréal) · 2012
Typearticle
Languageen
FieldEngineering
TopicSeismic Performance and Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsBridge (graph theory)Seismic analysisLinearizationNonlinear systemRange (aeronautics)Seismic isolationEarthquake engineeringGround motion
DOInot available

Abstract

fetched live from OpenAlex

The seismic design provisions of the CSA-S6 Canadian Highway Bridge Design Code and the AASHTO LRFD Seismic Bridge Design Specifications have been developed primarily based on historical earthquake events that have occurred along the North American west coast. For the design of seismic isolation systems, the simplified equivalent analysis and design method included in these codes relies on an equivalent effective linearization of the nonlinear isolated structures and a response reduction factor, or damping coefficient, Β which takes into account the effect of the equivalent added damping. The appropriateness of the damping coefficient B for western and eastern North American ground motions is investigated in this paper. Results from a series of time-history analyses performed for a set of linear equivalent and nonlinear structural systems covering a wide range of isolated and damped bridge configurations are presented. It is found that there is a need to adjust existing design guidelines to better capture the expected nonlinear response of isolated bridges, especially for earthquakes expected in eastern North America. © 2012, International Association for Earthquake Engineering. All rights reserved.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.025

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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.009
GPT teacher head0.218
Teacher spread0.209 · 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

Citations0
Published2012
Admission routes1
Has abstractyes

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