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Record W2137219283 · doi:10.1139/l11-031

Combination rule for the prediction of the seismic demand on columns of regular bridges under bidirectional earthquake components

2011· article· en· W2137219283 on OpenAlexaffvenueabout
Amar Khaled, Robert Tremblay, Bruno Massicotte

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2011
Typearticle
Languageen
FieldEngineering
TopicSeismic Performance and Analysis
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsBridge (graph theory)Structural engineeringSkewSpan (engineering)Seismic analysisNonlinear systemGround motionResponse spectrumGeologyEngineeringMathematics

Abstract

fetched live from OpenAlex

A percentage rule for the prediction of interacting seismic responses of bridge columns under multi-directional earthquake components is derived for eastern and western Canada seismic regions. A total of 14 regular bridges with different geometrical properties were examined for the two sites. The new rule was developed by comparing the column longitudinal steel ratio required from response spectrum analysis with combination rules to the ratio determined from the results of linear dynamic time history analysis of the bridge structures under bidirectional ground motions. The required steel ratios were found to vary with the weighted percentages used in the combination rules, the ground motion characteristics, and bridge properties. It was found that a 100%–20% combination rule is applicable for most bridges studied in eastern Canada. A 100%–40% rule is more appropriate for bridges located in western Canada. The results show that these rules need not be applied for regular straight bridges without skew but further investigation is needed before such a relaxation can be applied. Nonlinear dynamic time histories analysis of a two-span skewed bridge designed with the proposed percentage rule for eastern and western Canada showed that the columns would not experience excessive damage.

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 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 score0.334
Threshold uncertainty score0.233

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 head0.015
GPT teacher head0.171
Teacher spread0.156 · 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.

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

Citations9
Published2011
Admission routes3
Has abstractyes

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