MétaCan
Menu
Back to cohort
Record W6996144309

Proposal of simplified design methods to evaluate second-order effects in tall reinforced masonry walls

2019· article· en· W6996144309 on OpenAlexaboutno aff

Bibliographic record

VenueResearch Padua Archive (University of Padua) · 2019
Typearticle
Languageen
FieldEngineering
TopicMasonry and Concrete Structural Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsMasonryCurvatureParametric statisticsReliability (semiconductor)Column (typography)Moment (physics)
DOInot available

Abstract

fetched live from OpenAlex

A previous experimental campaign has demonstrated the good behavior of tall rein-forced masonry (RM) walls, with vertically perforated clay units, when subjected to lateral actions. The samples tested represent typical construction systems of commercial and industrial single-story RM buildings provided with deformable roofs.
\nHowever, to date, EN1996 does not have a consistent approach to check the second-order effects due to out-of-plane loads in such structures, providing requirements for RM walls which are too restrictive.
\nThis paper firstly discusses some of the major results of a recent numerical parametric study which implements fiber FE models calibrated from the above tests, as the actual slenderness limits for RM walls, also on the basis of experimental evidences. Then, more rational simplified approaches than that provided in EN1996 for evaluating second-order effects in tall RM walls are proposed, starting from the design methods of the Model Column (MC) and the Nominal Curvature (NC) generally used for RC structures; details on how these general methods were adapted to be used for RM tall walls are given in the paper. Finally, their reliability is assessed with respect to the numerical results of the previous parametric study, as well as that of the Moment Magnifier (MM) method proposed by the American (TMS 402) and Canadian (CSA S304) standards.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.847
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.029
GPT teacher head0.305
Teacher spread0.276 · 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.

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
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueResearch Padua Archive (University of Padua)Same topicMasonry and Concrete Structural AnalysisFrench-language works237,207