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Record W3208790524 · doi:10.3390/en14217110

Hygrothermal and Mechanical Behaviors of Fiber Mortar: Comparative Study between Palm and Hemp Fibers

2021· article· en· W3208790524 on OpenAlexaff
Younes Zouaoui, Ferhat Benmahiddine, Ammar Yahia, Rafik Belarbi

Bibliographic record

VenueEnergies · 2021
Typearticle
Languageen
FieldMaterials Science
TopicNatural Fiber Reinforced Composites
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsMaterials scienceMortarComposite materialFiberPorosityScanning electron microscopeMoistureAbsorption of waterCompressive strengthMicrostructureSynthetic fiberNatural fiber

Abstract

fetched live from OpenAlex

This paper presents an experimental investigation of the hygrothermal and mechanical properties of innovative mortar mixtures reinforced with natural fibers. Fibers extracted from palm stems (PS) and hemp (HF) were evaluated at different percentages. Scanning electron microscope (SEM) observations showed that the PS fibers have rough surfaces and very complex microstructures. Prior to their incorporation into the mortar, the fibers were subjected to different treatments to reduce their hydrophilic character. The employed treatments showed good efficiency in reducing the water absorption of both PS and HF fiber types. Furthermore, the mortar mixtures incorporating these fibers exhibited low thermal conductivity and excellent moisture buffering capacity. Indeed, the moisture buffer value (MBV) of the investigated mixtures ranged between 2.7 [g/(%HR·m2)] and 3.1 [g/(%HR·m2)], hence providing them excellent moisture regulator character. As expected, the fiber mortar mixtures showed very high porosity and low compressive strength ranging between 0.6 and 0.9 MPa after 28 days of age. The low-environmental footprint materials developed in this study are intended for thermal insulation and building filling.

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 score0.014
Threshold uncertainty score0.487

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 head0.023
GPT teacher head0.288
Teacher spread0.265 · 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

Citations22
Published2021
Admission routes1
Has abstractyes

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