MétaCan
Menu
Back to cohort
Record W2326560211 · doi:10.1061/9780784413470.053

Self-Centering Energy-Dissipative (SCED) Brace: Overview of Recent Developments and Potential Applications for Tall Buildings

2014· article· en· W2326560211 on OpenAlexaff
Jeffrey Erochko, Constantin Christopoulos

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSeismic Performance and Analysis
Canadian institutionsCarleton UniversityUniversity of Toronto
Fundersnot available
KeywordsBraceStructural engineeringEconomic shortageEngineering

Abstract

fetched live from OpenAlex

The self-centering, energy-dissipative (SCED) brace is an innovative cross-bracing system that eliminates residual building deformations after an earthquake while simultaneously dissipating energy to reduce drifts. Several recent studies are summarized which have confirmed and extended the capabilities of SCED braces. These include a multi-storey SCED frame shake table test, a high axial-capacity SCED brace prototype, and a telescoping configuration called the T-SCED brace which greatly increases axial elongation capacity. These recent advancements have improved the desirability of SCED braces for use in high-rise structures because the braces can now better accommodate the larger forces and elongations prevalent in tall buildings. SCED braces may also provide an increased level of performance compared to other damping systems for tall buildings because they are designed to return a structure to its original, undeformed position after an earthquake. This paper reviews several potential configurations for the use of SCED braces in tall buildings, including the use of SCED braces in tall braced frames, coupled with shear walls and with vertical outriggers.

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 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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.927
Threshold uncertainty score0.352

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 head0.013
GPT teacher head0.243
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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
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

Citations9
Published2014
Admission routes1
Has abstractyes

Explore more

Same topicSeismic Performance and AnalysisFrench-language works237,207