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Record W2805589492 · doi:10.7939/r3f18sv2z

Structural Reliability of Non-Slender Loadbearing Concrete Masonry Members under Concentric and Eccentric Loads

2017· article· en· W2805589492 on OpenAlexaboutno aff
Moosavi Nanehkaran

Bibliographic record

VenueUniversity of Alberta Library · 2017
Typearticle
Languageen
FieldEngineering
TopicStructural Load-Bearing Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsConcentricMasonryStructural engineeringEccentricReliability (semiconductor)EngineeringGeotechnical engineeringGeologyMathematicsGeometryPhysics

Abstract

fetched live from OpenAlex

Although the reliability levels of structural steel and reinforced concrete structures designed to the Canadian codes and standards have been investigated substantially in the past four decades, studies on the reliability of masonry structures are limited. Based on some preliminary studies in 1980s, the design of masonry structures using the limit states method was introduced in the 1994 edition of the Canadian standard S304 in order to provide more uniform and economical design guidelines. However, the limit states design criteria were not supported by a rigorous reliability-based analysis. The investigation reported herein was carried out to contribute to filling the gap in our knowledge on the reliability of structural masonry members designed using the limit states method, to establish reliability levels for masonry comparable to other structural materials and to help remove any unnecessary conservatism in the masonry design process. The first order reliability method was used to assess the reliability of unreinforced and reinforced non-slender concrete masonry walls under combined axial load and out-of-plane bending. In this research, only non-slender walls having a slender ratio (kh/t) not requiring consideration of second order effects are considered. Nevertheless, the procedure for performing the reliability analysis for walls with larger kh/t is proposed and explained.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.081
Threshold uncertainty score0.831

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 communication0.0000.001
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 head0.006
GPT teacher head0.180
Teacher spread0.173 · 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 designObservational
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

Citations1
Published2017
Admission routes1
Has abstractyes

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