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Record W2060540627 · doi:10.1061/9780784479117.193

Seismic Performance of Reinforced Concrete Wall with Superelastic Shape Memory Alloy Rebar

2015· article· en· W2060540627 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

VenueStructures Congress 2015 · 2015
Typearticle
Languageen
FieldEngineering
TopicSeismic Performance and Analysis
Canadian institutionsUniversity of British Columbia, Okanagan CampusOkanagan University CollegeUniversity of British Columbia
Fundersnot available
KeywordsRebarShear wallStructural engineeringShape-memory alloySMA*Materials scienceShear (geology)Reinforced concreteSeismic analysisGeologyEngineeringComposite materialComputer science

Abstract

fetched live from OpenAlex

The superelastic shape memory alloy (SMA) rebar can significantly improve the seismic performance of reinforced concrete (RC) structures. This study was motivated from a blind prediction contest of a full-scale 7-storey RC shear wall building at the University of California, San Diego. A finite element (FE) analysis was conducted to investigate the seismic performance of RC shear wall with the application of SMA rebar. The SMA rebar was applied at the plastic hinge location of the shear wall only and the steel rebar was used for all other sections. The seismic performance of shear wall was evaluated by performing incremental dynamic analysis (IDA) using 13 ground motions. The seismic demand was recorded in terms of inter-storey drift ratio until the full collapse of the building. The safety of the wall was measured in terms of collapse margin ratio (CMR) explicitly considering the uncertainty. The results show that the SMA rebar significantly improved the performance of the shear wall building and yielded higher CMR as compared to the conventional RC building.

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 score0.006
Threshold uncertainty score0.915

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 head0.010
GPT teacher head0.210
Teacher spread0.200 · 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