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Record W4394195183 · doi:10.6084/m9.figshare.20026798

Theoretical and experimental study of slender structural masonry walls

2022· dataset· en· W4394195183 on OpenAlexaboutno aff
Guilherme Aris Parsekian, Márcio Roberto Silva Corrêa, Guilherme Martins Lopes, Isabella Cavichiolli

Bibliographic record

VenueFigshare · 2022
Typedataset
Languageen
FieldEngineering
TopicMasonry and Concrete Structural Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsMasonryStructural engineeringGeologyGeotechnical engineeringEngineeringForensic engineering

Abstract

fetched live from OpenAlex

Abstract One of the factors to be taken into account in the design of compression loaded elements is their slenderness. Nowadays, in Brazil, the industry still uses the concept of simplified slenderness ratio - in which the buckling length is determined by an effective height (hef) and the radius of gyration is replaced by a parameter called effective thickness (tef) - when calculating the resistance reduction factor. Other masonry codes, such as the North American, European and Australian, also use the resistance reduction factor in their wall compression load capacity formulation. Yet, the Canadian code indicates the need for a more accurate and realistic analysis of slender walls, considering the balance in the deformed configuration of the wall (P-Delta analysis). This paper reports the findings of an experimental program that tested 18 ceramic and concrete block walls with high slenderness ratios, obtained with thin block dimensions. The predictions of load capacity for the case of hollow concrete blocks were close to the test results. In the case of the ceramic blocks, which had a complex, double-face shell geometry, only the Canadian code approach produced the test results with some degree of certainty and proximity. Slenderer walls and complex geometry blocks require more refined calculation procedures. P-Delta analysis and the verification of the section with brittle-tensile materials solutions may apply.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.931
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.9310.000

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 head0.242
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.

Study designNot applicable
Domainnot available
GenreDataset

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
Published2022
Admission routes1
Has abstractyes

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