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Properties of Mortar Containing High Volume Palm Oil Biomass Waste

2015· article· en· W2242727313 on OpenAlexaff
Nor Hasanah Abdul Shukor Lim, Mohd Warid Hussin, Abdul Rahman Mohd Sam, Muhammad Aamer Rafique Bhutta, Nur Farhayu Ariffin, Nur Hafizah A. Khalid, Mostafa Samadi

Bibliographic record

VenueAdvanced materials research · 2015
Typearticle
Languageen
FieldEngineering
TopicConcrete and Cement Materials Research
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMortarCompressive strengthPalm kernelWaste managementBiomass (ecology)Palm oilVolume (thermodynamics)CementEnvironmental sciencePulp and paper industryMaterials scienceComposite materialEngineeringGeology

Abstract

fetched live from OpenAlex

The utilization of waste materials which are abundant and cheap, especially from clean resources, has become more pressing than ever. This paper, discusses the utilization of the wastes in the form of palm oil fuel ash and oil palm kernel shell in the production of mortar mixes as a part of new and innovative materials in construction industry. The studies include the basic properties including the morphology of the composite with regards to variations in the mix design process. In order to get a better performance in terms of strength development, the ash used has gone through heat treatment and ground up to the size less than 2µm. High volume of 60%, 80% and 100% palm oil fuel ash was used as cement replacement. The incorporation of more than 80% of palm oil biomass waste as cement and sand replacement has produced mortar having an improved compressive strength than normal mortar. In addition, the density of the mortar with biomass waste was less than normal mortar. Overall results have revealed that the inclusion of high volume palm oil biomass waste can produce mortar mix with high strength, good performance and most importantly more sustainable.

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
Bibliometrics0.0010.000
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 head0.081
GPT teacher head0.316
Teacher spread0.235 · 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

Citations9
Published2015
Admission routes1
Has abstractyes

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