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Record W4386316508 · doi:10.1080/15583058.2023.2253434

In-Plane Investigation of Log Walls with Various Orthogonal Joineries Employing Finite Element Modeling

2023· article· en· W4386316508 on OpenAlexaff
Reza Kalantari, Ghazanfarah Hafeez

Bibliographic record

VenueInternational Journal of Architectural Heritage · 2023
Typearticle
Languageen
FieldEngineering
TopicWood Treatment and Properties
Canadian institutionsConcordia University
Fundersnot available
KeywordsFinite element methodParametric statisticsStructural engineeringStiffnessEngineeringMathematicsStatistics

Abstract

fetched live from OpenAlex

Log house construction is a well established style of home building across many centuries. Orthogonal joineries classically guarantee structural strength in log house construction. The paper investigates the structural response of log walls with orthogonal, standard, and Butt-and-Pass joineries under lateral loads. Finite element models are developed and validated through comparisons with available experimental results. Various parametric studies are presented assessing the effect of the penetration length, openings, reinforcement methods, log profiles, and log wall orthogonal joineries on the lateral strength and stiffness of the wall systems. The study indicates a significant impact of these parameters on the log wall lateral load-resisting behaviour. Finite element models show that the log walls with the TR system (three bottom and three top logs were tied with the rods) demonstrated the greatest maximum lateral resistance, followed by the Tie-down system. Generally, a greater initial stiffness was indicated in the log walls with the Tie-down system, followed by the TR system.

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 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 score0.180
Threshold uncertainty score0.367

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 head0.021
GPT teacher head0.230
Teacher spread0.209 · 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 teacher head, 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 abstractyes

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