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Record W4328138261 · doi:10.18280/acsm.470104

Assessment of the Durability Against a Chemical Attack of Fiber-Reinforced Lightweight Pouzzolanic Concrete under the Effect of Temperature

2023· article· en· W4328138261 on OpenAlexvenueno aff
Ahlem Houaria Mohammed Belhadj, Antonio José Tenza-Abril, Abdelkader Mahi

Bibliographic record

VenueAnnales de Chimie Science des Matériaux · 2023
Typearticle
Languageen
FieldEngineering
TopicFire effects on concrete materials
Canadian institutionsnot available
Fundersnot available
KeywordsDurabilityComposite materialMaterials scienceFiberForensic engineeringEngineering

Abstract

fetched live from OpenAlex

The study of the durability of Fiber-Reinforced Lightweight Pouzzolanic Concrete (FRLPC) is a topic of research that focuses on evaluating the skills of FRLPC, because exposure to certain chemicals can cause deterioration and reduce the lifespan of the concrete. Chemical attacks can occur due to exposure to acids, sulfates, and other substances commonly found in the environment. The work presented here aims to analyze the influence of different types of fibers on the behavior of lightweight concretes (LWC) based on pozzolanic aggregates in aggressive media such as acids and under the effect of temperatures. The choice of pozzolanic aggregates is to valorize natural pozzolan as lightweight aggregates in concrete, knowing that this material is abundant in Algeria. In this sense, different tests have been carried out using metal, polyethylene, and polypropylene fibers in LWC manufactured with pozzolanic aggregates. The LWC specimens were kept in water saturated with lime until the age of testing (28, 60, and 90 days). Then, the specimens were subjected to heating under two temperatures (200°C and 600°C). After cooling, the specimens were exposed to hydrochloric acid (Hcl) and sulfuric acid (H2SO4) solutions (5% w/w). The evaluation of the durability of these concretes and the mechanical behavior was obtained by the measurement of the mass loss and the compressive strength.

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)0.002
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 score0.066
Threshold uncertainty score0.507

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.014
GPT teacher head0.268
Teacher spread0.254 · 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

Citations3
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueAnnales de Chimie Science des MatériauxSame topicFire effects on concrete materialsFrench-language works237,207