MétaCan
Menu
Back to cohort
Record W3198063458 · doi:10.1002/tal.1891

A discontinuous cantilever beam analogy for quantifying higher mode demands in stacked rocking cores

2021· article· en· W3198063458 on OpenAlexaff
Navid Rahgozar

Bibliographic record

VenueThe Structural Design of Tall and Special Buildings · 2021
Typearticle
Languageen
FieldEngineering
TopicSeismic Performance and Analysis
Canadian institutionsAlpha Technologies (Canada)
Fundersnot available
KeywordsCantileverStructural engineeringParametric statisticsHingeBeam (structure)ModalAnalogyModal analysisSeismic analysisMode (computer interface)Core (optical fiber)Euler's formulaComputer scienceEngineeringMathematicsMathematical analysisMaterials scienceFinite element method

Abstract

fetched live from OpenAlex

Summary Stacked rocking core (SRC) combinations are innovative sustainable structural assemblies that mitigate seismic demands through uplift at the hinge joints and direct damage to replaceable fuses. The stacked segments curtail substantially higher mode effects by the relative motions between multilevel cores. Currently, few research studies have quantified the behavior of SRCs under earthquake shaking. This paper presents a novel closed‐form solution for quantifying the higher mode demands of stacked braced frames subjected to seismic excitation. The mathematical formulae for modal analysis are derived using the analogy of discontinuous cantilever beam and Euler–Bernoulli equation. The proposed equations provide a method for rapid analysis and preliminary design of low‐ to mid‐rise SRCs. A parametric study is carried out to investigate the influences of rotational restraint of segments and the location of the rocking joints. Results verified by time response analysis indicate the validity and accuracy of the suggested formulae.

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)0.001
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.025
GPT teacher head0.258
Teacher spread0.233 · 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

Citations21
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueThe Structural Design of Tall and Special BuildingsSame topicSeismic Performance and AnalysisFrench-language works237,207