Predicting the strength of concrete made with stone dust and nylon fiber using artificial neural network.

Sourav Ray, Mohaiminul Haque, Tanvir Ahmed, Ayesha Ferdous Mita, Md Hadiuzzaman Saikat, Md Mafus Alom

Open access · goldAbstract read

In one paragraph

Article in Heliyon, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it

0cells of the map it votes in

4citing papers in PubMed

2.6field-weighted citation impact, top 11% of its field

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

4 citing papers in PubMed, 23 citations in OpenAlex.

Optimizing concrete sustainability with bagasse ash and stone dust and its impact on mechanical properties and durability.Scientific reports · 2025
Article
Physical, mechanical, and durability properties of concrete containing different waste synthetic fibers for green environment - A critical review.Heliyon · 2024
Review
Prediction and optimization of hardened properties of concrete prepared with granite dust and scrapped copper wire using response surface methodology.Heliyon · 2024
Article
Systematic literature review on the application of machine learning for the prediction of properties of different types of concrete.PeerJ. Computer science · 2024
Article

4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors at 1 institution in 1 country.

Sourav RayDepartment of Civil and Environmental Engineering, Shahjalal University of Science and Technology, Sylhet, Bangladesh.

Mohaiminul HaqueDepartment of Civil and Environmental Engineering, Shahjalal University of Science and Technology, Sylhet, Bangladesh.

Tanvir AhmedDepartment of Civil and Environmental Engineering, Shahjalal University of Science and Technology, Sylhet, Bangladesh.

Ayesha Ferdous MitaDepartment of Civil and Environmental Engineering, Shahjalal University of Science and Technology, Sylhet, Bangladesh.

Md Hadiuzzaman SaikatDepartment of Civil and Environmental Engineering, Shahjalal University of Science and Technology, Sylhet, Bangladesh.

Md Mafus AlomDepartment of Civil and Environmental Engineering, Shahjalal University of Science and Technology, Sylhet, Bangladesh.

Shahjalal University of Science and Technology · BD

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Excessive demand of concrete is causing depletion of natural sand resources. Especially, the extraction of river sand negatively affects its surrounding environment. A sustainable solution to this problem can be the proper utilization of waste materials and by-products like stone dust (SD) as fine aggregate replacement in concrete. The recycling of stone dust as a construction material lessens the use of natural resources and helps to solve landfill scarcity as well as environmental problems. Addition of nylon fiber (NF) as fiber reinforcement can also attribute to enhance the properties of concrete. This research aims at utilizing SD as fine aggregate along with NF, and assessing the compressive strength and splitting tensile strength of concrete. Although the individual effects of incorporating stone dust and nylon fiber in concrete have been investigated in previous researches, their combined effects, as well as effects of water cement (WC) ratio on concrete strength, have not been studied yet. In this study, volumetric percentages of stone dust (20%-50%) and nylon fiber (0.25%-0.75%) and three different water cement ratio (0.45, 0.50 and 0.55) have been considered as three independent variables to develop probabilistic models for compressive strength and splitting tensile strength of concrete using artificial neural network (ANN). The values of coefficient of determination (R

Indexed as

Artificial neural networkCompressive strengthNylon fiberSplitting tensile strengthStone dust

Identifiers

PMID35345396

DOI10.1016/j.heliyon.2022.e09129

PMCPMC8956880

OpenAlexW4225726255

What OpenQuestion holds

Textmetadata

LicenceCC BY-NC-ND

Read underepoch 390

Registered trials

None linked

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.