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Seismic Design of Bridges in Moderate Seismic Region and Response Modification Factors

2009· article· en· W24577968 on OpenAlexfundno aff
Seung-Kyu Kook, Dong Uk Lee

Bibliographic record

VenueJournal of the Computational Structural Engineering Institute of Korea · 2009
Typearticle
Languageen
FieldEngineering
TopicStructural Health Monitoring Techniques
Canadian institutionsnot available
FundersCanadian Institutes of Health Research
KeywordsSeismic analysisResponse spectrumBridge (graph theory)Incremental Dynamic AnalysisStructural engineeringEngineeringCode (set theory)Set (abstract data type)Civil engineeringComputer science

Abstract

fetched live from OpenAlex

‘Korean Highway Bridge Design Code’ provides the spectrum analysis method with response modification factors for the seismic design of typical bridges. However, considering that korean peninsula is classified as moderate seismic regions and domestic circumstances for bridge design and construction are different from other countries, the applicability of this code is not yet proved. Therefore it is required to verify that applying the spectrum analysis method fulfills the no collapse requirement which is set forth as the basic seismic design concept. In this study two typical bridges with T and type piers are selected as analysis bridges and seismic designs are carried out by applying the spectrum analysis method with design conditions given for moderate seismic regions. Based on the results obtained through deign procedures, the role of the response modification factors and fulfillment of the no collapse requirement are discussed, from which supplementary provisions for the design code are identified.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.011

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)0.0030.001

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 head0.030
GPT teacher head0.266
Teacher spread0.236 · 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

Citations2
Published2009
Admission routes1
Has abstractyes

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