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Record W7116721104 · doi:10.1155/adce/6669748

Performance Analysis of Clay Bricks Baked With Sustainable and Eco‐Friendly Refuse‐Derived Fuel

2025· article· en· W7116721104 on OpenAlexaff
Utsav Sharma, Dayanand Sharma, Tushar Bansal, Flomo L. Gbawoquiya

Bibliographic record

VenueAdvances in Civil Engineering · 2025
Typearticle
Languageen
FieldEngineering
TopicRecycling and utilization of industrial and municipal waste in materials production
Canadian institutionsCarleton University
Fundersnot available
KeywordsBrickEfflorescenceFossil fuelSump (aquarium)Compressive strengthLoomingEnvironmentally friendly

Abstract

fetched live from OpenAlex

In this study, the potential production of refuse‐derived fuel (RDF) to supplement fossil fuel coal in the firing process of clay bricks is explored as a possible solution to the baked brick industries. RDF‐incorporated brick making may be regarded as a low‐carbon‐footprint and cost‐effective production system in view of the current looming fossil fuel depletion and anthropogenic CO 2 emissions. Results indicate that RDF‐fired bricks perform better than conventional bricks regarding the major parameters. The compressive strength of RDF‐fired bricks, 13.7 N/mm 2 , was higher than the minimum required value of 10.0 N/mm 2 . The water absorption was within the controlled limit of 14.2% (maximum 15%), confirming durability. No efflorescence was observed, indicating that the surface contained a low proportion of water‐soluble salts. Particulate matter (PM) as air emission was 39.4 mg/Nm 3 . NO 2 emissions were well below the 400 mg/Nm 3 limit at 259.2 mg/Nm 3 , while SO 2 and CO concentrations, 115.8 mg/Nm 3 and 82 mg/Nm 3 , respectively, were also within permissible limits. These results suggest that RDF is an environmentally friendly and cost‐effective alternative to conventional brick firing methods dependent on coal, aligning with the principles of the circular economy.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.075
Threshold uncertainty score0.490

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.005
GPT teacher head0.217
Teacher spread0.212 · 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 designSimulation or modeling
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

Explore more

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