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Record W4256147293 · doi:10.32920/ryerson.14657853.v1

Axial and shear behavior of profiled steel sheet dry board (PSSDB) composite walling system

2021· preprint· en· W4256147293 on OpenAlexaff
Asmah Jahan

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicStructural Analysis of Composite Materials
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsStructural engineeringInfillComposite numberShear (geology)Materials scienceShear wallStructural systemFinite element methodComposite materialEngineering

Abstract

fetched live from OpenAlex

This research describes the structural behavior of profiled steel sheet dry board (PSSDB) composite wall panels under shear and axial loading based on experimental, theoretical and finite element (FE) analyses. The proposed PSSDB walling system consists of an individual profiled steel sheet (PSS) assembled with a single or double plywood dry board (DB) with or without concrete in-fill. The influences of various parameters such as presence or absence of concrete-infill/opening and boundary frame as well as DB-PSS/DB-PSS-DB/PSSDB-frame connections/fasteners and panel geometric/material properties on load-deformation response, ultimate load capacity and failure modes are investigated. Experimental results of PSSDB wall panels were used to validate the performance of FE and theoretical models for predicting the shear and axial strength capacity. The FE analysis coupled with experimental and theoretical analyses provides a better understanding of the structural performance of PSSDB walls and hence, helps to develop design guidelines for their use as structural units in buildings.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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)0.0020.000

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 head0.011
GPT teacher head0.220
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 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

Citations1
Published2021
Admission routes1
Has abstractyes

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