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Record W2085506380 · doi:10.1061/41171(401)143

Assessment of Seismic Performance of Structures in 2010 Chile Earthquake through Field Investigation and Case Studies

2011· article· en· W2085506380 on OpenAlexaff
Oh‐Sung Kwon, Amr S. Elnashai, Bora Gencturk, S. Kim, Seong Hoon Jeong, Jazalyn Dukes

Bibliographic record

VenueStructures Congress 2011 · 2011
Typearticle
Languageen
FieldEngineering
TopicSeismic Performance and Analysis
Canadian institutionsUniversity of Toronto
FundersNational Research Foundation of KoreaUniversity of MissouriMissouri University of Science and TechnologyGeorgia Institute of TechnologyNational Science Foundation
KeywordsInfillBridge (graph theory)MasonryStructural engineeringSeismic analysisGeologyRetrofittingEngineeringEarthquake simulationGeotechnical engineering

Abstract

fetched live from OpenAlex

This paper presents an overview of the damage from the 2010 Chile Earthquake observed by a field reconnaissance team from the Mid-America Earthquake Center. Seismic performances of structures in Santiago, Biobío, and Maule regions in Chile are summarized based on field observations and discussions with researchers and city officials. In addition, post-earthquake analyses of a damaged bridge and a building structure are presented. The analyses are carried out with detailed documentation from the field and the acquired design drawings of the structures. For the building structure, the focus of the analysis is to understand the effect of masonry infill walls on the shear force demand of columns. The analysis results confirm that the infill walls of the building may have caused the damage to columns by increasing the shear force demand. For the bridge structure, the effect of seismic bars on the bridge response is investigated. The seismic bars in the bridge reduce the displacement demand of the superstructure by increasing the energy dissipation capacity of elastomeric bearings.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.026
GPT teacher head0.262
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 designObservational
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

Citations5
Published2011
Admission routes1
Has abstractyes

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