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Record W4316810304 · doi:10.18280/ijdne.460605

Ultra-Fine Treated and Untreated Walnut Shell Ash Incorporated Cement Mortar: Properties and Environmental Impact Assessments

2022· article· en· W4316810304 on OpenAlexvenueno aff
Ayad S. Aadi, Ahmed Ali Mohammed, Taghreed Khaleefa Mohammed Ali, Nahla Hilal

Bibliographic record

VenueAnnales de Chimie Science des Matériaux · 2022
Typearticle
Languageen
FieldEngineering
TopicRecycled Aggregate Concrete Performance
Canadian institutionsnot available
Fundersnot available
KeywordsMortarCementMaterials scienceCompressive strengthComposite materialDurabilityPorosityShell (structure)

Abstract

fetched live from OpenAlex

The utilization of walnut shell (WS) as partial replacement of aggregates and cement is getting more attention in modern research on construction material. Though WS provides lightweight concrete, but less compressive strength and porous structure of concrete is still problematic for its utilization. Thus, this study is using untreated walnut shell powder (UWSP) and ultra-fine treated walnut shell ash (UFTWSA) as a substitute for cement. Different weight fractions of UWSP and UFTWSA (5, 10, 15 and 20%) were used to investigate the influence on fresh and hardened properties of cement mortar. Further, the durability of all mixes was evaluated by immersing them in different concentrations of MgSO4 (5, 10 and 20%). The experimental results revealed that the inclusion of UWSP and UFTWSA reduces the fresh and hardened properties of cement mortar. Moreover, UWSP addition has more negative impact on fresh and hardened properties of cement mortar as compared to UFTWSA. The hardened properties of mortar specimens remarkably decreased by immersing in MgSO4 solution. However, all mixes contained UWSP and UFTWSA and not exposed to acid attack achieved more than 30 MPa and can be classified as good type mortar depending on obtained ultra-sonic plus velocity values (UPV).

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.601
Threshold uncertainty score1.000

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 studies0.0010.001
Scholarly communication0.0000.001
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 head0.031
GPT teacher head0.239
Teacher spread0.208 · 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.

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
Published2022
Admission routes1
Has abstractyes

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