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Record W4285082281 · doi:10.1016/j.istruc.2022.06.072

A solution for the frictional resistance in macro-block limit analysis of non-periodic masonry

2022· article· en· W4285082281 on OpenAlexaff
‪Marco Francesco Funari, Bora Pulatsu, Simon Szabò, Paulo B. Lourénço

Bibliographic record

VenueStructures · 2022
Typearticle
Languageen
FieldEngineering
TopicMasonry and Concrete Structural Analysis
Canadian institutionsCarleton University
FundersFundação para a Ciência e a TecnologiaMinistério da Ciência, Tecnologia e Ensino SuperiorEuropean CommissionUniversità degli Studi Roma Tre
KeywordsMasonryLimit analysisSolverBlock (permutation group theory)MacroLimit (mathematics)ComputationHeuristicStructural engineeringMathematicsUpper and lower boundsComputer scienceGeometryEngineeringMathematical analysisMathematical optimizationAlgorithm

Abstract

fetched live from OpenAlex

This paper proposes a general equation to assess the crack inclination upper threshold when non-periodic textures characterise masonry walls. The proposed formulation introduces the computation of the frictional resistance at the macro-block interface by evaluating two masonry quality indexes, i.e., vertical and horizontal lines of trace. The proposed equation is adopted in conjunction with a macro-block limit analysis formulation in which the failure mechanism is parametrised and formulated according to the upper bound limit analysis theorem, coupled with a heuristic solver that is able to minimise the load multiplier and identify the geometry of the associated macro-block. The proposed analytical model is verified in a number of case studies by comparing advanced DEM simulations and numerical results arising from the literature.

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)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0030.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.007
GPT teacher head0.213
Teacher spread0.206 · 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 designTheoretical or conceptual
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

Citations45
Published2022
Admission routes1
Has abstractno

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