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Record W2948525696 · doi:10.11159/iccste19.144

Performance of ecological cement-based composites containing sugarcane bagasse ash – A review

2019· review· en· W2948525696 on OpenAlexvenueno aff
Marco Antonio Maldonado-García, Pedro Montes-García, Pedro Leobardo Valdez-Tamez

Bibliographic record

VenueProceedings of the International Conference on Civil, Structural and Transportation Engineering · 2019
Typereview
Languageen
FieldEngineering
TopicConcrete and Cement Materials Research
Canadian institutionsnot available
FundersConsejo Nacional de Ciencia y TecnologíaInstituto Politécnico Nacional
KeywordsBagasseCementComposite materialMaterials sciencePulp and paper industryWaste managementEnvironmental scienceEngineering

Abstract

fetched live from OpenAlex

In recent years the sugarcane bagasse ash (SCBA) has been used to produce environmentally-friendly cement-based composites. The SCBA is an agricultural waste which is obtained as by-product from the combustion of sugarcane bagasse in sugar mills. This ash is available in large quantities in emerging countries such as Brazil, India, Thailand and Mexico, and its disposal in open dumps is causing different environmental issues. A number of researchers report that the SCBA from sugar mills needs a post-treatment such as recalcination, grinding, sieving or the combination of these methods in order to enhance its pozzolanic activity. After processing, up to 30% of SCBA can be used as a partial Portland cement replacement in composites. This replacement may cause microstructural changes in the cementitious matrix and the improvement of the mechanical properties in composites containing it. However, studies on the durability of such composites are required. This paper focuses on the performance of cement-based composites containing treated SCBA. The microstructural, mechanical and some durability properties of those composites are discussed. This literature review is useful to carry out further researches about the durability of reinforcement embedded in cement-based composites containing SCBA (SCBAC).

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: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.343
Threshold uncertainty score0.751

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.039
GPT teacher head0.278
Teacher spread0.239 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations0
Published2019
Admission routes1
Has abstractyes

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