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Record W7083314120 · doi:10.11159/ijci.2025.010

Processing of Recycled Cement Sacks as Cellulose Pulp into Pervious Concrete

2025· article· en· W7083314120 on OpenAlexvenueno aff

Bibliographic record

VenueInternational Journal of Civil Infrastructure · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicEthnobotanical and Medicinal Plants Studies
Canadian institutionsnot available
FundersConsejo Nacional de Ciencia, Tecnología e Innovación Tecnológica
KeywordsPulp (tooth)CellulosePervious concreteCement

Abstract

fetched live from OpenAlex

This study proposes and validates a methodology for incorporating cellulose fibres (CF) from recycled cement sacks into pervious concrete.The process involves mechanically converting the sacks into pulp and pre-saturating them to ensure integration into the mix.The CF obtained was evaluated in terms of composition, and its performance was assessed through rheology and the mechanical and permeability properties of pervious concrete at three dosages (3.9, 5.8, and 7.7 kg/m).A straightforward mechanical procedure was established to produce the CF, requiring a minimum presaturation period of 12 hours to secure adequate mix flowability and paste coating uniformity.XRF and XRD tests confirmed that the treatment reduced cement residues, although traces of chlorine remained.Saturated CF did not compromise matrix fluidity, ensuring an adequate coating.Additionally, CF improved permeability by up to 32.9% without compromising strength, acting as an internal curing agent and enhancing longterm compressive strength up to 10.4%.CF also strengthened matrix-aggregate bonding, improved post-crack integrity, and promoted a more ductile failure mode.In conclusion, processing cement sacks into cellulose fibres provides a practical and sustainable solution for improving cohesion and permeability in pervious concrete while maintaining mechanical performance.This approach supports sustainable construction by valorising packaging waste and offers a method adaptable to industry, depending on local conditions.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.560
Threshold uncertainty score0.523

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 head0.008
GPT teacher head0.265
Teacher spread0.257 · 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 designOther design
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

Citations1
Published2025
Admission routes1
Has abstractyes

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