Evidence map›Paper›PMID 39379613›Full record

ArticleScientific reports2024

Optimization and prediction of paver block properties with ceramic waste as fine aggregate using response surface methodology.

G Uday Kiran, Nakkeeran G, Dipankar Roy, George Uwadiegwu Alaneme

Erratum issuedAbstract read
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Article
  5. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors.

G Uday KiranDepartment of Civil Engineering, Madanapalle Institute of Technology and Science, Madanapalle, Andhra Pradesh, 517325, India.
Nakkeeran GDepartment of Civil Engineering, Madanapalle Institute of Technology and Science, Madanapalle, Andhra Pradesh, 517325, India. drnakkeerang@mits.ac.in.
Dipankar RoyDepartment of Civil Engineering, Madanapalle Institute of Technology and Science, Madanapalle, Andhra Pradesh, 517325, India.
George Uwadiegwu AlanemeDepartment of Civil Engineering, School of Engineering and Applied Sciences, Kampala International University, Kampala, Uganda. alanemeg@kiu.ac.ug.ORCID https://orcid.org/0000-0003-4863-7628

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The ceramic industry produces a significant volume of ceramic waste (CW), representing around 20-30% of its the entire output. The waste mostly comes from challenges noticed in the manufacturing process, overproduction, and damage to products. Considering the substantial worldwide production of ceramics, it is crucial to efficiently handle and recycle this waste to promote sustainability efforts. This study explores the conversion of ceramic waste into fine aggregates suitable for the production of paver blocks. Currently, a variety of assessments are being conducted to determine the effectiveness of these enhanced paver blocks. The evaluations involve aspects like compressive strength, water absorption (WA), dry density, flow table measurements, ultrasonic pulse velocity (UPV), and rebound hammer tests. The results indicate that replacing natural aggregates with up to 30% CW significantly improves compressive strength (CS) and Rebound results from tests. This study provides useful information into optimising the content of CW in paver blocks, contributing to the development of sustainable and economical construction materials. Furthermore, it focusses on minimising landfill waste and preserving natural resources.

Indexed as

Alternative new building materialsCeramic wasteOptimization and predictionPaver blockRSMSustainability

Identifiers

PMID39379613
PMCPMC11461831

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.