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Disturbed Stress Field Model for Unreinforced Masonry

2013· article· en· W1979769862 on OpenAlexaff
Luca Facconi, Giovanni Plizzari, Frank J. Vecchio

Bibliographic record

VenueJournal of Structural Engineering · 2013
Typearticle
Languageen
FieldEngineering
TopicMasonry and Concrete Structural Analysis
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsUnreinforced masonry buildingField (mathematics)Stress (linguistics)Geotechnical engineeringMasonryStructural engineeringEngineeringMathematics

Abstract

fetched live from OpenAlex

The traditional smeared crack macromodels for the analysis of masonry structures consider masonry as a homogeneous material with the effects of mortar joints included in an average sense. This approach, suitable for the analysis of large structures, implicitly excludes the possibility of representing local elastic and inelastic mechanisms involving mortar joints. In this study, an innovative formulation based on the disturbed stress field model (DSFM) is proposed for the analysis of unreinforced masonry structures. The advancement introduced by the model lies in the possibility of simulating the global average behavior of the composite material in combination with the local nonlinear shear slip response of both bed and head joints. This paper describes the formulation of the model; as well, it presents results obtained from the simulation of tests performed on shear walls demonstrating the ability of the DSFM to reproduce the structural response of masonry structures.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.000
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 head0.005
GPT teacher head0.192
Teacher spread0.186 · 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 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

Citations28
Published2013
Admission routes1
Has abstractyes

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