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Prediction of Engineered Cementitious Composite Material Properties Using Artificial Neural Network

Credit to Download: 1 | Page Numbers 9 | Abstract Views: 77
Year: 2019
COI code: JR_IJE-32-11_004
Paper Language: English

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Authors Prediction of Engineered Cementitious Composite Material Properties Using Artificial Neural Network

  Fariborz Nateghi-A - International Institute of Earthquake Engineering and Seismology, Tehran, Iran
  Mohammad Hossein Ahmadi - International Institute of Earthquake Engineering and Seismology, Tehran, Iran

Abstract:

Cement-based composite materials like Engineered Cementitious Composites (ECCs) are applicable in the strengthening of structures because of the high tensile strength and strain. Proper mix proportion, which has the best mechanical properties, is so essential in ECC design material to use in structural components. In this paper, after finding the best mix proportion based on uniaxial tensile strength and strain, the correlation between these parameters were calculated. Since material properties depend on the content ratios, six mixtures with different Fly Ash (FA) content were considered to find the best ECC mixture called Improved ECC (IECC). Also, The influence of local fine aggregates and FA on the tensile behavior of ECC was considered to introduce IECC which has the best tensile properties. To predict the mechanical properties of ECC based on experimental results, Artificial Neural Network (ANN) was used. Training and validation of the proposed model were carried out based on 36 experimental results to find the best results. Numerical analysis is utilized to find the best mix proportion of ECC in structural design. The results show that the effects of FA and fine aggregates are considerable. Also, The proposed ANN model predicts the tensile strength and strain of ECC with different FA ratios accurately. Furthermore, the model can estimate mechanical properties of ECC in previous experimental results.

Keywords:

Engineered Cementitious Composites Experimental Study, Artificial Neural Network, Local Admixtures, Mechanical properties

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https://www.civilica.com/Paper-JR_IJE-JR_IJE-32-11_004.html
COI code: JR_IJE-32-11_004

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Nateghi-A, Fariborz & Mohammad Hossein Ahmadi, 2019, Prediction of Engineered Cementitious Composite Material Properties Using Artificial Neural Network, International Journal of Engineering (IJE) 32 (11), https://www.civilica.com/Paper-JR_IJE-JR_IJE-32-11_004.htmlInside the text, wherever referred to or an achievement of this article is mentioned, after mentioning the article, inside the parental, the following specifications are written.
First Time: (Nateghi-A, Fariborz & Mohammad Hossein Ahmadi, 2019)
Second and more: (Nateghi-A & Ahmadi, 2019)
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