UGC Approved Journal no 63975(19)

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Published in:

Volume 10 Issue 8
August-2023
eISSN: 2349-5162

UGC and ISSN approved 7.95 impact factor UGC Approved Journal no 63975

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Published Paper ID:
JETIR2308544


Registration ID:
523758

Page Number

f390-f398

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Title

PREDICTION OF COMPRESSIVE STRENGTH OF CONCRETE CONTAINING USED ENGINE OIL USING ANN

Abstract

In an effort to lessen the negative effects that disposing of used engine oil (UEO) for the atmosphere has on marine, human, and underwater life as well as agricultural productivity, this study suggests using such waste material as a chemical mixture in concrete manufacturing. In order to ascertain the impact of UEO on the various new characteristics of concrete (consistency, the rate of slump loss, settling time, and air content), a study is originally presented. The effectiveness of concrete having UEO in the state of hardening will next be carefully examined through evaluation of the different material properties, namely the compressive strength after 3 days, 7 days, and 28 days. Artificial neural network (ANN) models are used to forecast the strength qualities of concrete mixes created with varying amounts of UEO (0%, 0.25%, 0.50%, 0.75 %, and 1%). The output layer, input layer, and hidden layer are the three layers of an ANN. The cement, fine aggregate, coarse aggregate, water content, and chemical admixture (UEO) percentage make up the input layer. Concrete's compressive strength is the result. 45 samples are utilized as training and testing data sets for creating an ANN model. One assessment looks at how many neurons are actually needed in the hidden layer to forecast the network system, while the other assesses how accurate the predicted network is under various load situations. Artificial neural networks typically learn through training and produce incredibly good outcomes. The experimental data can be advanced using ANN to figure out the compressive resistance of concrete. When results are compared to experimental findings and results from neural network training, high accuracy is seen.

Key Words

Compressive Strength, Used Engine Oil, Artificial Neural Networks (ANN).

Cite This Article

"PREDICTION OF COMPRESSIVE STRENGTH OF CONCRETE CONTAINING USED ENGINE OIL USING ANN", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 8, page no.f390-f398, August-2023, Available :http://www.jetir.org/papers/JETIR2308544.pdf

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2349-5162 | Impact Factor 7.95 Calculate by Google Scholar

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 7.95 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Cite This Article

"PREDICTION OF COMPRESSIVE STRENGTH OF CONCRETE CONTAINING USED ENGINE OIL USING ANN", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 8, page no. ppf390-f398, August-2023, Available at : http://www.jetir.org/papers/JETIR2308544.pdf

Publication Details

Published Paper ID: JETIR2308544
Registration ID: 523758
Published In: Volume 10 | Issue 8 | Year August-2023
DOI (Digital Object Identifier): http://doi.one/10.1729/Journal.35842
Page No: f390-f398
Country: Ananthapuramu, Andhra Pradesh, India .
Area: Engineering
ISSN Number: 2349-5162
Publisher: IJ Publication


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