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Record W4252785437 · doi:10.1504/ijmri.2021.112070

Testing and finite element modelling of concrete block masonry walls under axial and out-of-plane loading

2021· article· en· W4252785437 on OpenAlexaffabout
Andrea C. Isfeld, Anna L. Müller, Mark Hagel, Nigel G. Shrive

Bibliographic record

VenueInternational Journal of Masonry Research and Innovation · 2021
Typearticle
Languageen
FieldEngineering
TopicMasonry and Concrete Structural Analysis
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMasonryRebarStructural engineeringFinite element methodMaterials scienceGroutMortarDisplacement (psychology)Geotechnical engineeringBoundary value problemGeologyComposite materialEngineeringMathematics

Abstract

fetched live from OpenAlex

The current Canadian masonry design standard, CSA S304-14 (2014), appears to underestimate the capacity of loadbearing masonry walls for some loading combinations and wall sizes. This is compounded by the fact that most testing has been completed on walls with pinned base supports, a condition that is not representative of practice. To understand the structural response of concrete block walls under eccentric axial and combined axial and out-of-plane loads better, walls with height to thickness ratio of 12.6 were tested. The displacement profiles under pinned-fixed and pinned-pinned boundary conditions were obtained for both load combinations and were used with the constituent material properties to calibrate finite element models. Models were developed using micro-modelling approaches, modelling units, mortar and grout as merged parts, or individually with friction contact interfaces, having rebar embedded in the grouted cells. The contact modelling approach showed broad agreement with the test results. Both the test results and the modelling showed a clear reduction in out-of-plane displacements, and change in the displaced profile, when a pinned connection was not forced at the wall base.

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)0.001
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.783
Threshold uncertainty score0.344

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.083
GPT teacher head0.311
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.

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

Citations1
Published2021
Admission routes2
Has abstractyes

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Same venueInternational Journal of Masonry Research and InnovationSame topicMasonry and Concrete Structural AnalysisFrench-language works237,207