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Record W4410437921 · doi:10.70803/001c.138258

Analysis of Shear-Dominated Reinforced Masonry Walls Using Truss Models

2015· article· en· W4410437921 on OpenAlexaff
Mohammadreza Moharrami, Ioannis Koutromanos, Scott A. Williams

Bibliographic record

VenueThe Masonry Society Journal · 2015
Typearticle
Languageen
FieldEngineering
TopicMasonry and Concrete Structural Analysis
Canadian institutionsBerger (Canada)
Fundersnot available
KeywordsMasonryTrussStructural engineeringShear wallShear (geology)Reinforced concreteGeologyUnreinforced masonry buildingGeotechnical engineeringEngineeringMaterials scienceComposite material

Abstract

fetched live from OpenAlex

This study uses nonlinear truss models for the analysis of fully grouted, shear-dominated, reinforced masonry (RM) walls subjected to cyclic lateral loads. A previously established method, developed for the analysis of reinforced concrete walls, is appropriately modified to allow the simulation of RM walls, including walls with openings. The modeling approach is validated using data from previously conducted quasi-static tests on singlestory walls and a dynamic test on a two-story wall system. The very good agreement of the analytical results with the experimental observations and the conceptual simplicity of the modeling approach indicate that it can be used for parametric analyses of RM wall systems.

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)0.001
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: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.028
GPT teacher head0.238
Teacher spread0.210 · 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

Citations4
Published2015
Admission routes1
Has abstractyes

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