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Analytical Seismic Fragility Curves for Reinforced Concrete Wall pier using Shape Memory Alloys considering maximum drift

2019· article· en· W2914962809 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

VenueMATEC Web of Conferences · 2019
Typearticle
Languageen
FieldEngineering
TopicSeismic Performance and Analysis
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
Fundersnot available
KeywordsPierFragilityStructural engineeringIncremental Dynamic AnalysisRebarSMA*Reinforced concreteSeismic analysisPlastic hingeGeologyGeotechnical engineeringEngineeringComputer sciencePhysics

Abstract

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Fragility curves express the seismic vulnerability of bridge piers for different damage states at various earthquake intensities. A fragility curve typically gives a physical understanding of repair costs and functionally levels of a bridge pier. Shape memory alloys (SMAs) provide a promising alternative material in reducing the failure probability of a bridge pier. This study develops a family of seismic fragility curves for reinforced concrete (RC) wall piers reinforced with three different types of SMA rebar in plastic hinge regions. An incremental dynamic analysis (IDA) using a total of 20 earthquake ground motions was performed on a SMA-RC wall pier to evaluate its seismic performance. The maximum drift recorded for each ground motion was taken as the seismic performance demand parameter of interest in this study. The probabilistic seismic demand model (PSDM) was used to generate fragility curves for each RC-SMA wall pier. The results show that the different mechanical properties and type of SMAs affect the performance of RC-SMA wall pier.

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 categoriesInsufficient payload (model declined to judge)
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 score0.355
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.023
GPT teacher head0.244
Teacher spread0.221 · 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