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Record W4385460077 · doi:10.1080/13632469.2023.2241914

Numerical Modeling and Parametric Assessment of Log Shear Walls with Bonded Corners

2023· article· en· W4385460077 on OpenAlex
Reza Kalantari, Ghazanfarah Hafeez

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Earthquake Engineering · 2023
Typearticle
Languageen
FieldEngineering
TopicWood Treatment and Properties
Canadian institutionsConcordia University
Fundersnot available
KeywordsShear wallParametric statisticsStiffnessStructural engineeringWork (physics)Scale (ratio)Finite element methodEngineeringShear (geology)Geotechnical engineeringMaterials scienceMathematicsMechanical engineeringComposite materialStatisticsPhysics

Abstract

fetched live from OpenAlex

The paper investigates the stiffness and strength of log walls with standard and dovetail bonded corners under lateral loads. The finite element model is developed and validated with experimental work at small-scale and wall-scale levels. The validated wall-scale model was modified by conducting a detailed investigation on assessing the wall behaviour with varying crucial parameters, including coefficient of friction, vertical load, geometric irregularities, and the wall’s aspect ratio. The study suggested various techniques for improving the wall’s lateral load resistance capacities and initial stiffness, employing steel and wood pins.

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.

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)0.000
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.435

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.017
GPT teacher head0.223
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