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Record W4412754708 · doi:10.11159/iccste25.176

Effect of the Replacement of Cement by Fly Ash and Calcined Clay on the Mechanical-Physical Properties of Conventional Mortar

2025· article· en· W4412754708 on OpenAlexvenueno aff
Aracelly Saravia, Gladys Vela

Bibliographic record

VenueProceedings of the International Conference on Civil, Structural and Transportation Engineering · 2025
Typearticle
Languageen
FieldEngineering
TopicRecycling and utilization of industrial and municipal waste in materials production
Canadian institutionsnot available
Fundersnot available
KeywordsFly ashCalcinationMortarCementMaterials scienceMechanical strengthComposite materialGeotechnical engineeringGeologyChemistry

Abstract

fetched live from OpenAlex

This article explored the effects of replacing Portland cement with fly ash and calcined clay on the strength and durability of conventional mortar, focusing on improving its mechanical properties and reducing the carbon footprint generated by the construction industry.Tests were conducted on samples with 20% and 55% cement replacements ratios, evaluating parameters such as compressive strength, flexural strength, and porosity.The results indicated that mortars with cement replacements outperformed the control mortar in terms of strength and compaction.At 28 days, the mortar with a 20% replacement achieved a compressive strength of 24.67 MPa, surpassing the standard design.Lower porosity was also observed in mortars with replacements, contributing to greater durability and resistance to water absorption.This study concluded that using fly ash and calcined clay as a partial replacement for cement was viable for structural applications, particularly in seismic zones, due to improved cohesion and reduced formation of voids in the mix, enhancing its mechanical performance and reducing its environmental impact.

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 score0.065
Threshold uncertainty score0.202

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 head0.234
Teacher spread0.218 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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