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Record W4389764431 · doi:10.5267/j.esm.2023.10.005

Measurement and analysis mechanical properties of cross-laminated timber (CLT) product: Case study on typical lampung lamina arrangement

2023· article· en· W4389764431 on OpenAlexvenueno aff
Sugito Sugito, Ronny Purba, Sella Gita Rizkia, Ridho Ardian

Bibliographic record

VenueEngineering Solid Mechanics · 2023
Typearticle
Languageen
FieldEngineering
TopicWood Treatment and Properties
Canadian institutionsnot available
Fundersnot available
KeywordsCross laminated timberStiffnessStructural engineeringBeam (structure)BendingPolyvinyl acetateBending momentMechanical engineeringEngineeringMaterials scienceComposite material

Abstract

fetched live from OpenAlex

Recently, the development of cross-laminated timber (CLT) products that can substitute large-dimensional wood for structures and reduce waste of wood products with small dimensions and pieces continues to be developed, and demand from industries continues to increase. Therefore, this study aims to measure and analyze the mechanical properties of cross-laminated timber (CLT) products designed with a typical arrangement of the Lampung region, Indonesia, called a screen arrangement. Measurement and analysis of mechanical properties include measuring load tests and analyzing moments and stiffness of CLT products intended for floor and beam structures. The screen-type wood arrangement used in the product consists of an inner screen arrangement, an outer screen arrangement, and a standing screen arrangement with polyvinyl acetate adhesive. Load test measurements were carried out using a universal testing machine (UTM) and moment and bending stress analysis using engineering mechanics. Generally, the results show that CLT products with standing screens have smaller mechanical properties than those intended for floors and beams. This research shows that the typical CLT arrangement of the Lampung region (Indonesia), in the form of a screen, has strong mechanical properties and characteristics that can be compared with other arrangements.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.052
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.041
GPT teacher head0.253
Teacher spread0.212 · 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.

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
Published2023
Admission routes1
Has abstractyes

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