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Record W3164147935 · doi:10.18280/ijsse.110203

Seismic Design Assessment of Bridge Piers Location Effect on the Structural Capacity of Supports under Earthquake Action

2021· article· en· W3164147935 on OpenAlexvenueno aff
Ali Fadhil Naser, Hussam Ali Mohammed, Ayad Ali Mohammed

Bibliographic record

VenueInternational Journal of Safety and Security Engineering · 2021
Typearticle
Languageen
FieldEngineering
TopicSeismic Performance and Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsStructural engineeringBridge (graph theory)PierGirderDisplacement (psychology)Seismic analysisEngineeringBox girderSeismic loadingGeotechnical engineering

Abstract

fetched live from OpenAlex

The objective of this study was to assess the seismic performance of two types of bridges structures under effect of earthquake by using different locations and numbers of piers. The results of D/C ratio showed that simply supported I girder bridge appeared higher structural capacity than continuous box girder bridge which was resisted the seismic demand. Continuous box girder bridge had higher seismic demand and lower structural capacity comparing with simply supported I girder bridge. Commonly, the seismic design for two types of bridges models with increasing of piers numbers was suitable to resist the earthquake action for region type B. The results of non-linear static analysis (pushover method) showed that the increasing of piers numbers had significant effects on the seismic design of bridges structures to increase the displacement capacity, force capacity, and decreasing of seismic demand to reduce the effects of earthquake action on the bridges structural members. The bridge type simply supported I girder had higher capacity in longitudinal direction than continuous box girder bridge. Whereas, for continuous box girder bridge appeared higher capacity in transverse direction than simply supported I girder. The performance points which were based on displacement were decreased with increasing the piers numbers for bridges structures supports.

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 score0.129
Threshold uncertainty score0.287

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 head0.014
GPT teacher head0.249
Teacher spread0.235 · 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
Published2021
Admission routes1
Has abstractyes

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