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Record W4317387755 · doi:10.1108/wje-04-2022-0161

New approach for the mix design of high-strength concretes valorization of local aggregates

2023· article· en· W4317387755 on OpenAlexaboutno aff
Sabah Ben Messaoud

Bibliographic record

VenueWorld Journal of Engineering · 2023
Typearticle
Languageen
FieldEngineering
TopicConcrete and Cement Materials Research
Canadian institutionsnot available
Fundersnot available
KeywordsAbsorption of waterCompressive strengthAggregate (composite)Materials scienceCementSilica fumeSofteningComposite materialWater–cement ratio

Abstract

fetched live from OpenAlex

Purpose The purpose of this paper is to make a contribution to understanding the influence of factors such as the water/cement (W/C) ratio and the granular class on the mechanical and physical properties of high-strength concretes (HSCs). In the formulations of HSC, aggregates by their high mass and volume proportion play an important role. When selecting aggregates, it is necessary to know their intrinsic properties. These properties influence the performance of concrete, in particular the quality of the granulate cimentary adhesion. Design/methodology/approach This experimental study focused on the effect of W/C ratio (0.25, 0.30, 0.35), the effect of replacing a part of cement by silica fume (SF) (8%), the effect of fraction of aggregate on properties of fresh and hardened concrete, the effect of different environment conversation like drinking water and sea water on compressive strength and the study of absorption of water and softening using the mix design method of the University of Sherbrooke combined with the Dreux-Gorisse method which gives good results. Findings At the end of our work, the examination of the results obtained made it possible to establish the correlations between the formulations studied and the physicomechanical characteristics of the concrete compositions (HSC25, HSC16, HSC8). The results of this study show that the use of three granular classifications (DMAX8, DMAX16 and DMAX25) and three report W/C (0.25, 0.30 and 0.35) in two different conservation environment (drinking water and sea water) give HSCs, HSC25 with an W/C = 0.25 ratio has reached the largest mechanical strength of 90 MPa for different environments of conservation. For selecting aggregates, it is necessary to know their intrinsic properties, these properties influence the strength of concrete. In general, there is a slight decrease in the compressive resistance of the specimens stored in seawater, it can be said that the conservation life has not had effect on the resistance (28 days). The effect of aggressive environment can appear in the long term. Research limitations/implications Mixed design and concrete fabrication with a 28-day compressive strength of up to 68 MPa or more of 90 MPa can now be possible used in Jiel (Algeria), and it should no longer be considered to be used only in an experimental domain. Addition of SF in concrete showed good development of strength between 7 and 28 days, depending on the design of the mix. Practical implications Concrete containing 8% SF with W/B of 0.25 has higher compressive strength than the other concretes, and concretes with SF are more resistant than concretes without SF, so it is possible to have concrete with a compressive strength of 82 MPa for W/C 0.25 without SF. Like as a result, we can avoid the use of SF to affect the strength of concrete at compressive strength of 68 MPa, and a slump of 21 cm, because the SF is the most expensive ingredient used in the composition of concrete and is therefore very important economically. One of the main factors of production of HSC above 90 MPa is use of aggregate DMAX25, which is stronger with W/B of 0.25 and 0.30. Social implications This mixtures leads to a very dense microstructure and low porosity and produces increased permeability of HSC and is able to resist the penetration of aggressive agents. This combination has a positive effect on the economy of concrete. Originality/value The combination of the Dreux-Gorisse method with the Sherbrook method is very beneficial for determining the percentage of aggregates used, and the use of coarse aggregates of Jijel to obtain HSC with 90 MPa and 16 cm of workability.

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)0.001
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.023
GPT teacher head0.236
Teacher spread0.213 · 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

Citations3
Published2023
Admission routes1
Has abstractyes

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