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160427

Prediction of Characteristic Strength of Sustainable Concrete Containing Mineral Additives by Artificial Neural Networks.

Article

Last updated: 22 Jan 2023

OverviewSimilar Items

Subjects

-

Tags

Civil Engineering

Abstract

It this study, the application of artificial neural networks (ANN) for estimating the characteristic strength of sustainable concrete that contains various amounts of fly ash, silica fume, slag and steel fiber have been investigated. Using ANN model, it is possible to establish a linear and nonlinear correlation between known input data like concrete ingredients and a certain output like characteristic strength, because ANN is an excellent tool to determine concrete properties. For the training of ANN models, an experimental data base (1410 concrete mixtures from earlier published papers) has been utilized. Then experimental tests were performed on some mixes of concrete to validate the model. The ANN model parameter statistics R2 is 0.888, 0.93, 0.9 for training, validation and test steps and indicate that ANN model makes effective prediction for characteristic strength of sustainable concrete. The application of ANN in predicting characteristic strength is considered to the quality assurance of manufacturing of concrete.

DOI

10.21608/bfemu.2021.160427

Keywords

artificial neural network, Sustainable Concrete, characteristic strength, Pozzolanic materials

Authors

First Name

Mohamed

Last Name

Elgamal

MiddleName

-

Affiliation

Master of Science Researcher., Structural Engineering Department., El-Mansoura University., Mansoura., Egypt.

Email

m.salamaelgamal@gmail.com

City

Mansoura

Orcid

-

First Name

Ahmed

Last Name

Tahwia

MiddleName

M.

Affiliation

Professor of Structural Engineering Department., Faculty of Engineering., El-Mansoura University., Mansoura., Egypt.

Email

atahwia@mans.edu.eg

City

Damietta

Orcid

-

First Name

Ashraf

Last Name

Heniegal

MiddleName

-

Affiliation

Professor., Civil Engineering Department., Suez University., Suez., Egypt.

Email

ashraf.heneagal@suezuni.edu.eg

City

Suez

Orcid

-

Volume

46

Article Issue

1

Related Issue

20965

Issue Date

2021-03-01

Receive Date

2020-11-06

Publish Date

2021-04-01

Page Start

79

Page End

81

Print ISSN

1110-0923

Online ISSN

2735-4202

Article File

BFEMU_Volume 46_Issue 1_Pages 79-81.pdf

PDF . 840.7kB

Link

https://bfemu.journals.ekb.eg/article_160427.html

Detail API

https://bfemu.journals.ekb.eg/service?article_code=160427

Order

21

Type

Research Studies

Type Code

1,205

Publication Type

Journal

Publication Title

MEJ. Mansoura Engineering Journal

Publication Link

https://bfemu.journals.ekb.eg/

MainTitle

-

Details

Type

Article

Created At

22 Jan 2023