MétaCan
Menu
Back to cohort
Record W4399918610 · doi:10.1177/87552930241257250

Damped rigid substructure system for seismic protection of structures

2024· article· en· W4399918610 on OpenAlexaff
Saeed Towfighi

Bibliographic record

VenueEarthquake Spectra · 2024
Typearticle
Languageen
FieldEngineering
TopicSeismic Performance and Analysis
Canadian institutionsStantec (Canada)
Fundersnot available
KeywordsSubstructureStructural engineeringGeologySeismologyEngineeringGeotechnical engineering

Abstract

fetched live from OpenAlex

Building codes commonly accept inelastic deformations that inevitably occur due to seismic excursions in structures. To control the damage, limits on the inelastic deformations have been established. Standard passive energy dissipation systems have been used to reduce the damage, generally with some effectiveness. The damped rigid substructure (DRS) passive damping system, proposed in this article, geometrically amplifies the damper displacements and exerts a re‐centering force, leading to effective discharge of the seismic energy to the extent that structural damage can be prevented. This is achieved using common damped and undamped diagonals arranged per implementation and design principles introduced in this article. The DRS system can considerably reduce material consumption and construction costs, leading to more sustainable structures when seismic forces govern the design. It can also benefit from the usage of high‐strength materials to enhance its re‐centering mechanism. The system is adaptable to the architectural needs and can be used for all categories of importance and height variation, made of steel or concrete.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0080.002

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 head0.008
GPT teacher head0.206
Teacher spread0.198 · 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 designSimulation or modeling
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

Citations0
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueEarthquake SpectraSame topicSeismic Performance and AnalysisFrench-language works237,207