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Record W4317372034 · doi:10.1061/ppscfx.sceng-1218

Effects of Ground Motion Incident Angle on Inelastic Seismic Demands of Skewed Bridges Subjected to Mainshock–Aftershock Sequences

2023· article· en· W4317372034 on OpenAlexaff
Alireza Garakaninezhad, Saeed Amiri, Ehsan Noroozinejad Farsangi

Bibliographic record

VenuePractice Periodical on Structural Design and Construction · 2023
Typearticle
Languageen
FieldEngineering
TopicSeismic Performance and Analysis
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsAftershockSeismologyGeologyGround motionStrong ground motionBridge (graph theory)Geodesy

Abstract

fetched live from OpenAlex

Several aftershocks can occur after a strong mainshock and lead to additional structural damage. The investigation of the seismic response of bridges is more challenging when the effects of incident angles of mainshocks and subsequent aftershocks are considered. This study investigates the effects of the ground motion incident angle on the nonlinear structural responses of skewed bridge structures against mainshock–aftershock sequences. For this objective, the seismic performance of a 60° skewed bridge located in San Fernando, California, is examined under real bidirectional mainshock–aftershock sequences. In this study, ground motion directionality effects were investigated considering the relative difference between mainshock and corresponding aftershock incident angles. The results showed that taking into account the incident angles of both mainshock and the corresponding aftershock can significantly affect the seismic performance of skew bridges. In particular, when the difference between the angles of the mainshock and corresponding aftershock was considered, the nonlinear responses of the case study increased up to 65.56%.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

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)0.0010.000

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 head0.010
GPT teacher head0.239
Teacher spread0.229 · 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

Citations15
Published2023
Admission routes1
Has abstractyes

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