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Record W2768589921 · doi:10.5539/jsd.v10n6p186

The Development of a Concrete Block Containing PET Plastic Bottle Flakes

2017· article· en· W2768589921 on OpenAlexvenueno aff
Tanut Waroonkun, Tanapong Puangpinyo, Yuttana Tongtuam

Bibliographic record

VenueJournal of Sustainable Development · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicMicroplastics and Plastic Pollution
Canadian institutionsnot available
Fundersnot available
KeywordsAggregate (composite)CementCompressive strengthMaterials scienceBottlePlastic wastePlastic bottleComposite materialBlock (permutation group theory)FlakeWater–cement ratioWaste managementMathematicsEngineering

Abstract

fetched live from OpenAlex

Plastic waste is increasing continuously, especially in the form of throw away packaging such as drinking water bottles, designed to be convenient, inexpensive, and accessible. Plastic disposal, however, is difficult and has a lower recyclability rate than other types of materials such as glass and paper. This study presents a method of reducing the amount of plastic waste by recycling plastic containers in architectural work. Non-load-bearing concrete blocks for safe and efficient use can be manufactured using plastic flakes as an alternative material aggregate. This study developed such block sand tested them for compressive strength integrating four major factors:(1) the cement to aggregate ratio, (2) the water to cement ratio, (3) the size of plastic flakes used and (4) the proportion of plastic flake that replaced sand. The findings revealed that using a ratio of 1:3 cement to aggregate, where the aggregate mix comprised of 20% small and medium sized (combined at 1:1) plastic flakes plus 80% sand and a water to cement ratio of 0.5, provided the optimal compressive strength to form a concrete block that can be used to construct a non-load bearing wall.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.450
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.010
GPT teacher head0.218
Teacher spread0.208 · 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 designObservational
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

Citations17
Published2017
Admission routes1
Has abstractyes

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