MétaCan
Menu
Back to cohort

Seismic Retrofit of Steel Moment-Resisting Frames with High-Performance Fiber-Reinforced Concrete Infill Panels: Large-Scale Hybrid Simulation Experiments

2013· article· en· W2028003617 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Structural Engineering · 2013
Typearticle
Languageen
FieldEngineering
TopicSeismic Performance and Analysis
Canadian institutionsMcGill University
Fundersnot available
KeywordsInfillStructural engineeringMoment (physics)DissipationSeismic retrofitFrame (networking)RetrofittingSteel frameEngineeringReinforced concreteMechanical engineering

Abstract

fetched live from OpenAlex

Recent earthquakes around the world have demonstrated that steel moment-frame buildings designed based on older seismic provisions are seismically deficient. To enhance the seismic performance of these buildings, a new seismic retrofit system has been developed and evaluated experimentally as part of a two-story steel moment-resisting frame, designed in California in the 1980s. The proposed retrofit system consists of high-performance fiber-reinforced concrete (HPFRC) infill panels acting as energy dissipation elements that can be easily replaced after a major earthquake. Through two large-scale hybrid simulation tests of the retrofitted two-story steel moment-resisting frame, it is demonstrated that (1) the proposed retrofit system is effective in terms of reducing the maximum story drift ratios and residual deformations of the retrofitted steel moment resisting frame relative to the predicted bare frame performance, and (2) the structural damage of the retrofitted steel moment-resisting frame is kept minimal because energy dissipation is concentrated in the infill panel retrofit system.

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.

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 score0.085
Threshold uncertainty score0.826

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 communication0.0000.001
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 head0.006
GPT teacher head0.202
Teacher spread0.196 · 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