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Record W2904324806 · doi:10.11113/jt.v81.12774

MECHANICAL, THERMAL AND DURABLE PERFORMANCE OF WASTES SAWDUST AS COARSE AGGREGATE REPLACEMENT IN CONVENTIONAL CONCRETE

2018· article· en· W2904324806 on OpenAlexaff
Ghasan Fahim Huseien, Ruhal Pervez Memon, Ziyad Kubba, Abdul Rahaman Mohd Sam, Mohammad Ali Asaad, Jahangir Mirza, Uroosa Memon

Bibliographic record

VenueJurnal Teknologi · 2018
Typearticle
Languageen
FieldEngineering
TopicWood Treatment and Properties
Canadian institutionsHydro-Québec
FundersUniversiti Teknologi Malaysia
KeywordsSawdustMaterials scienceCompressive strengthComposite materialAggregate (composite)Volume (thermodynamics)CementWaste managementPulp and paper industryEngineering

Abstract

fetched live from OpenAlex

Wood yields a number of by-products and Sawdust is as useful as others. Sawdust is regarded as a waste material and is effectively utilised as sawdust concrete in the construction of buildings. It is capable to be utilised as light-weight concrete and holds the quality of long duration heat transfer. In this study, three different ratios (1:1, 1:2 and 1:3) volume mix proportions of cement to sawdust were adopted to make sawdust concrete. At varied intervals of 7, 28 and 56 days of air curing, thermal and mechanical properties like workability, density, elastic modulus, strength and heat transfer were probed of mentioned sawdust concrete proportions. The resistance to elevated temperatures was also evaluated after 28 days of age; weight loss, residual compressive strength, surface texture and ultrasonic pulse velocity were considered in evaluation process. The findings showed that increase in sawdust volume affected to decrease the workability, strength and elevated temperatures resistance. However, the concrete having higher proportion of sawdust performed competently and well in terms of thermal conductivity. Moreover, a decrease in the heat transfer of sawdust was also observed. Examining the all-embracing mechanical and physical properties, sawdust can be effectively utilised in the construction of 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 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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.232
Threshold uncertainty score0.321

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 head0.016
GPT teacher head0.217
Teacher spread0.202 · 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 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

Citations15
Published2018
Admission routes1
Has abstractyes

Explore more

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