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Record W4396841327 · doi:10.1016/j.istruc.2024.106490

An analytical matrix approach for the prediction of the elastic lateral displacements of CLT platform-type lateral load resisting systems

2024· article· en· W4396841327 on OpenAlexafffund
Daniele Casagrande, Giuseppe D’Arenzo, Mohammad Masroor, Igor Gavrić, Ghasan Doudak

Bibliographic record

VenueStructures · 2024
Typearticle
Languageen
FieldEngineering
TopicWood Treatment and Properties
Canadian institutionsUniversity of Ottawa
FundersFPInnovationsEuropean Cooperation in Science and Technology
KeywordsStructural engineeringEurocodeStructural loadShear wallDeflection (physics)Cross laminated timberShear (geology)Structural systemMatrix (chemical analysis)EngineeringGeologyMaterials sciencePhysicsClassical mechanics

Abstract

fetched live from OpenAlex

This study presents an analytical method for the elastic lateral displacement calculation of platform-type cross laminated timber (CLT) lateral load resisting systems using a matrix approach. The current state-of-the-art models for lateral deflection calculation of single- and multi-panel CLT shear-walls systems were extended from single-storey cases to generalized multi-storey multi-panel CLT shear-wall systems. The proposed calculation method was validated against tests on twelve single-storey shear-walls and one two-storey shear-wall from four previously conducted experimental campaigns. The 2D lateral displacement analytical calculation model was then further expanded to the 3D application for CLT lateral load resisting systems through a matrix approach. The matrix method was verified against an analytical-numerical comparison on two case studies. The proposed analytical model forms a solid foundation for a potential implementation of this lateral deflection model for CLT shear-wall systems in the next generation of standards such as Eurocode 5 and CSA O86.

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: none
Teacher disagreement score0.005
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.022
GPT teacher head0.255
Teacher spread0.233 · 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

Citations6
Published2024
Admission routes2
Has abstractyes

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