MétaCan
Menu
Back to cohort
Record W2278195817 · doi:10.14288/1.0095534

Particle moisture content effects on the physical and mechanical properties of magnesite cement-bonded particleboard

2010· article· en· W2278195817 on OpenAlexaffabout
India E. O. Musokotwane

Bibliographic record

VenuecIRcle (University of British Columbia) · 2010
Typearticle
Languageen
FieldEngineering
TopicInnovations in Concrete and Construction Materials
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMagnesiteCementMaterials scienceWater contentComposite materialMoistureParticle sizeParticle (ecology)MetallurgyMagnesiumGeotechnical engineeringChemical engineeringGeologyEngineering

Abstract

fetched live from OpenAlex

The effects of initial particle moisture content, wood-cement ratio and density on physical (thickness swelling and water absorption) and mechanical properties (MOE, MOR, IB and edgewise compression) were investigated. Five initial particle moisture content levels - 0-6%, 8-15%, 25-30%, 40-50% and 60-80%; three wood-cement ratios - 1:1, 1:1.5 and 1:2; and three density levels at each wood-cement ratio - 1:1 -0.472 g/cm³, 0.528 g/cm³ and 0.622 g/cm³, 1:1.5 - 0.636 g/cm³, 0.707 g/cm³ and 0.809 g/cm³; and 1:2 - 0.763 g/cm³, 0.847 g/cm³ and 0.939 g/cm³ were used. Combinations of the above variables gave 45 treatments. Three replicate boards were made for each treatment thus giving a total of 135 panels for the study. A total of 135 test specimens were used for each property tested. Results from the tests were compared to the German and ISO Standards for similar boards and to the Canadian Waferboard Standard. Initial particle moisture content was highly significant in the development of physical and mechanical properties of magnesite cement-bonded particleboard. Increasing initial particle moisture content from 0-6% to 60-80% resulted in the reduction of the physical and mechanical properties of the boards. The highest initial particle moisture content of (60-80%) yielded the lowest physical and mechanical properties. For manufacture of boards of favourable mechanical properties, an initial particle moisture content of not more than 15% is recommended. On the other hand, a higher initial particle moisture content (>40%) is considered desirable if board thickness and water absorption are to be minimized. All the mechanical properties tested consistently increased by increasing wood-cement ratio and density and were highest at 1:2 wood-cement ratio and density level 3 of each wood-cement ratio. Thickness swelling and water absorption were consistently reduced by increasing wood-cement ratio and density. In both physical properties tests, the 1:2 wood-cement ratio and density level 3 yielded the lowest values. Thirty-two of the forty-five treatment combinations of initial particle moisture content, density and wood-cement ratio pass the MOE requirement of the German Standard DIN 52 362 for Portland cement-bonded particleboard; forty-one treatments met the minimum MOE Canadian Waferboard Standard requirements, while no treatment meet the MOR requirements for this Standard. Eleven of the forty-five treatments met the minimum IB Canadian Waferboard Standard requirements. All the 45 treatments pass the ISO building board requirements in thickness swelling, while 18 treatments pass the water absorption requirements for this Standard. Most of the treatment combinations compare favourably with results obtained in tests conducted in Europe for cement-bonded particleboard.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
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)0.0010.000

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 head0.158
Teacher spread0.146 · 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 designBench or experimental
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

Citations3
Published2010
Admission routes2
Has abstractyes

Explore more

Same venuecIRcle (University of British Columbia)Same topicInnovations in Concrete and Construction MaterialsFrench-language works237,207