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Record W7023782315

Parametric macro-modelling of old Canadian brick masonry walls under in-plane loading

2023· article· en· W7023782315 on OpenAlexaboutno aff

Bibliographic record

VenueeScholarship@McGill (McGill) · 2023
Typearticle
Languageen
FieldEngineering
TopicMasonry and Concrete Structural Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsParametric statisticsMasonryBrickMasonry veneer
DOInot available

Abstract

fetched live from OpenAlex

A large portion of Eastern Canada’s existing building stock constructed pre-1965 features unreinforced masonry (URM) loadbearing walls. Eastern Canada’s old URM buildings are highly vulnerable to even low levels of ground shaking, having been built prior to the release of a robust seismic design standard with traditional construction practices and materials. The in-plane (IP) response of URM walls is critical to understanding seismic response of Eastern Canada’s old URM structures, yet limited data on their material characteristics and behaviours are presently available in literature. In this paper, validated numerical macro-models based on the Distinct Element Method (DEM) are used to extend the limited previous experimental results on IP-loaded URM walls made of clay bricks, and contribute to uncover their response under various boundary conditions, geometrical configurations, vertical overburden. Preliminary results allow for a greater understanding of the seismic behaviour of Eastern Canada’s old URM, enabling us to observe how internal (geometry) and external (boundary and loading conditions) factors may effect their IP response.

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 score0.525
Threshold uncertainty score0.945

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.000
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.021
GPT teacher head0.205
Teacher spread0.184 · 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

Citations0
Published2023
Admission routes1
Has abstractno

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