UGC Approved Journal no 63975(19)

ISSN: 2349-5162 | ESTD Year : 2014
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Published in:

Volume 9 Issue 1
January-2022
eISSN: 2349-5162

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

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Published Paper ID:
JETIR2201465


Registration ID:
319621

Page Number

e470-e475

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Title

A COMPARATIVE STUDY ON THE COMPRESSIVE STRENGTH PREDICTION FOR NANOMATERIALS BY USING ARTIFICIAL NEURAL NETWORK AND SUPPORT VECTOR MACHINE

Abstract

Today's concrete constructions require structural members with higher mechanical properties and longer service life. This can be done by incorporating nanostructured elements into concrete to increase its mechanical qualities and reduce maintenance costs and eliminate the need for immediate replacement. The main objective was to study how microstructural changes affect the effectiveness of the compressive strength of concrete. New materials can be used to improve the mechanical properties of concrete and other materials. The improvement rate depends on the type and percentage of the nanomaterial.Another approach can be used, the Artificial Neural Network (ANN), which has recently gained popularity in the civil engineering field. ANN is a soft computer technique that simulates the characteristics of the human brain, learns from previous situations and adapts to new environments without any constraints. In this study, compressive strength (CS) of concrete containing nanomaterials, data collected in previous experimental surveys was used for ANN model. was developed with 5 input parameters as nanoSiO2, nanoAl2O3, nanoFe2O3, nanoTiO2, nanoZnO to predict CS of concrete nanomaterials; hidden layer knots as well as weights and biases are established through trial and error to achieve the best performing model.The correlation coefficient for training and testing data is suggesting that ANN can be used to predict CS of nanomaterials of specific strength is superior compared with support vector machine (SVM) .In the near future, understand A better understanding of the properties of nanomaterials will help define a better approach to creating innovative, livelihood-improving materials. Using specific nanostructured materials can help concrete structures perform better and last longer.

Key Words

Nano-materials(nano-SiO2,nano-Al2O3,nano-Fe2O3,nano-TiO2,nano-ZnO),Compressive strength,ANN,SVM(Support Vector Machine),MATLAB.

Cite This Article

"A COMPARATIVE STUDY ON THE COMPRESSIVE STRENGTH PREDICTION FOR NANOMATERIALS BY USING ARTIFICIAL NEURAL NETWORK AND SUPPORT VECTOR MACHINE", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 1, page no.e470-e475, January-2022, Available :http://www.jetir.org/papers/JETIR2201465.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

"A COMPARATIVE STUDY ON THE COMPRESSIVE STRENGTH PREDICTION FOR NANOMATERIALS BY USING ARTIFICIAL NEURAL NETWORK AND SUPPORT VECTOR MACHINE", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 1, page no. ppe470-e475, January-2022, Available at : http://www.jetir.org/papers/JETIR2201465.pdf

Publication Details

Published Paper ID: JETIR2201465
Registration ID: 319621
Published In: Volume 9 | Issue 1 | Year January-2022
DOI (Digital Object Identifier):
Page No: e470-e475
Country: -, -, India .
Area: Engineering
ISSN Number: 2349-5162
Publisher: IJ Publication


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