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Artificial Neural Network (ANN) Models for Prediction of Steel Fibre-Reinforced Concrete Strength

Shende, A. M. / Yadav, K. P. / Pande, A. M.

The objective of the present research paper is to develop artificial neural network simulation and analyse the most important π-term from five independent pi terms (aspect ratio, aggregate–cement ratio, water–cement ratio, percentage of fibre and control strength) for prediction of SFRC strength. The output of this network can be evaluated by comparing it with experimental strength and the predicted ANN simulation strength. The study becomes more fruitful when the most influencing π-term is calculated for the prediction of SFRC strength.

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Artificial Neural Network (ANN) Models for Prediction of Steel Fibre-Reinforced Concrete Strength

Shende, A. M. / Yadav, K. P. / Pande, A. M.

The objective of the present research paper is to develop artificial neural network simulation and analyse the most important π-term from five independent pi terms (aspect ratio, aggregate–cement ratio, water–cement ratio, percentage of fibre and control strength) for prediction of SFRC strength. The output of this network can be evaluated by comparing it with experimental strength and the predicted ANN simulation strength. The study becomes more fruitful when the most influencing π-term is calculated for the prediction of SFRC strength.

Artificial Neural Network (ANN) Models for Prediction of Steel Fibre-Reinforced Concrete Strength

Shende, A. M. / Yadav, K. P. / Pande, A. M.

The objective of the present research paper is to develop artificial neural network simulation and analyse the most important π-term from five independent pi terms (aspect ratio, aggregate–cement ratio, water–cement ratio, percentage of fibre and control strength) for prediction of SFRC strength. The output of this network can be evaluated by comparing it with experimental strength and the predicted ANN simulation strength. The study becomes more fruitful when the most influencing π-term is calculated for the prediction of SFRC strength.

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Titel:

Artificial Neural Network (ANN) Models for Prediction of Steel Fibre-Reinforced Concrete Strength

Weitere Titelangaben:

Lecture Notes in Civil Engineering

Beteiligte:

Laishram, Boeing (Herausgeber:in) / Tawalare, Abhay (Herausgeber:in) / Shende, A. M. (Autor:in) / Yadav, K. P. (Autor:in) / Pande, A. M. (Autor:in)

Kongress:

International Conference on Advances in Civil Engineering ; 2020

Erschienen in:

Recent Advancements in Civil Engineering ; Kapitel: 21 ; 223-230

Lecture Notes in Civil Engineering ; 172

Erscheinungsdatum:

15.12.2021

Format / Umfang:

8 pages

ISBN:

978-981-16-4396-5 , 978-981-16-4395-8

ISSN:

2366-2565 , 2366-2557

DOI:

https://doi.org/10.1007/978-981-16-4396-5_21

Medientyp:

Aufsatz/Kapitel (Buch)

Format:

Elektronische Ressource

Sprache:

Englisch

Schlagwörter:

ANN model , 5 independent π terms , Predicted SFRC strength Engineering , Construction Management , Transportation Technology and Traffic Engineering , Building Construction and Design

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