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Record W1746690401 · doi:10.1139/cjce-2012-0278

Seismic performance comparison between direct displacement-based and force-based design of a multi-span continuous reinforced concrete bridge with irregular column heights

2014· article· en· W1746690401 on OpenAlexaffvenueabout
Samy Muhammad Reza, M. Shahria Alam, Solomon Tesfamariam

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2014
Typearticle
Languageen
FieldEngineering
TopicSeismic Performance and Analysis
Canadian institutionsOkanagan University CollegeUniversity of British Columbia, Okanagan Campus
Fundersnot available
KeywordsStructural engineeringSeismic analysisBridge (graph theory)Displacement (psychology)DissipationSpan (engineering)ResidualEngineeringColumn (typography)Nonlinear systemEarthquake resistant structuresComputer science

Abstract

fetched live from OpenAlex

North American bridge design codes, e.g., Canadian Highway Bridge Design Code and AASHTO 2007, follow force-based design (FBD) method, which is focused at the target force resistance capacity of the structure. Displacement-based design (DBD) method, on the other hand, aims to ensure a target maximum displacement of the bridge during earthquakes in a specific zone. In this study, bridges with irregular column heights have been designed according to DBD and FBD (as per Canadian standards and AASHTO 2007) considering seismic loading. Subsequently, seismic performances of the bridges designed with the two different methods have been compared by conducting nonlinear dynamic analyses in the longitudinal direction. Maximum and residual displacements and energy dissipation capacity are used as performance indicators. This study outlines the necessity of possible modification in the current Canadian seismic design standards as well as DBD for bridges with irregular column heights.

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 score0.409
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.011
GPT teacher head0.186
Teacher spread0.175 · 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

Citations7
Published2014
Admission routes3
Has abstractyes

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