• Register
  • Login

European Journal of Molecular & Clinical Medicine

  • Home
  • Browse
    • Current Issue
    • By Issue
    • By Subject
    • Keyword Index
    • Author Index
    • Indexing Databases XML
  • Journal Info
    • About Journal
    • Aims and Scope
    • Editorial Board
    • Publication Ethics
    • Indexing and Abstracting
    • Peer Review Process
    • News
  • Guide for Authors
  • Submit Manuscript
  • Contact Us
Advanced Search

Notice

As part of Open Journals’ initiatives, we create website for scholarly open access journals. If you are responsible for this journal and would like to know more about how to use the editorial system, please visit our website at https://ejournalplus.com or
send us an email to info@ejournalplus.com

We will contact you soon

  1. Home
  2. Volume 7, Issue 8
  3. Authors

Online ISSN: 2515-8260

Volume7, Issue8

PREDICTION OF STEEL FIBRE REINFORCED CONCRETE (SFRC) STRENGTH USING ARTIFICIAL NEURAL NETWORK (ANN) MODELS, RESPONSE SURFACE METHODOLOGY (RSM) MODELS AND THEIR COMPARATIVE STUDY

    A.M. Shende K.P. Yadav P.P. Bhad A.M. Pande

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 8, Pages 306-314

  • Show Article
  • Download
  • Cite
  • Statistics
  • Share

Abstract

There is various methodologies and mathematical models developed to predict the steel fiber reinforced concrete strength (SFRC) and these methods are prominently used in their time. Due to enhancement in the technology new mathematical models are developed and compared them with the old ones, as per their fit and comparative betterment, these methods become significant for the use by the scientists, researchers and mathematicians. In the research paper discussed here has an objective to develop a new mathematical approach to predict the SFRC strength using two newly introduced models namely Artificial Neural Network Simulation (ANN) and Response surface methodology (RSM) to analyse Aspect ratio, Aggregate-cement ratio, Water-cement ratio, Percentage of fibre and Control strength (referred to as five pi terms).
The comparison of these two methods with experimental strength shows the output for the best fit, the study further extended to compare between these two models with each other to find best fit out of these two models. The calculation of the influence of pi terms, mentioned above to predict the SFRC, make this study more fruitful.
Keywords:
    ANN model RSM model π -terms SFRC strength
  • PDF (594 K)
  • XML
(2020). PREDICTION OF STEEL FIBRE REINFORCED CONCRETE (SFRC) STRENGTH USING ARTIFICIAL NEURAL NETWORK (ANN) MODELS, RESPONSE SURFACE METHODOLOGY (RSM) MODELS AND THEIR COMPARATIVE STUDY. European Journal of Molecular & Clinical Medicine, 7(8), 306-314.
A.M. Shende; K.P. Yadav; P.P. Bhad; A.M. Pande. "PREDICTION OF STEEL FIBRE REINFORCED CONCRETE (SFRC) STRENGTH USING ARTIFICIAL NEURAL NETWORK (ANN) MODELS, RESPONSE SURFACE METHODOLOGY (RSM) MODELS AND THEIR COMPARATIVE STUDY". European Journal of Molecular & Clinical Medicine, 7, 8, 2020, 306-314.
(2020). 'PREDICTION OF STEEL FIBRE REINFORCED CONCRETE (SFRC) STRENGTH USING ARTIFICIAL NEURAL NETWORK (ANN) MODELS, RESPONSE SURFACE METHODOLOGY (RSM) MODELS AND THEIR COMPARATIVE STUDY', European Journal of Molecular & Clinical Medicine, 7(8), pp. 306-314.
PREDICTION OF STEEL FIBRE REINFORCED CONCRETE (SFRC) STRENGTH USING ARTIFICIAL NEURAL NETWORK (ANN) MODELS, RESPONSE SURFACE METHODOLOGY (RSM) MODELS AND THEIR COMPARATIVE STUDY. European Journal of Molecular & Clinical Medicine, 2020; 7(8): 306-314.
  • RIS
  • EndNote
  • BibTeX
  • APA
  • MLA
  • Harvard
  • Vancouver
  • Article View: 214
  • PDF Download: 363
  • LinkedIn
  • Twitter
  • Facebook
  • Google
  • Telegram
Journal Information

Publisher:

Email:  editor.ejmcm21@gmail.com

  • Home
  • Glossary
  • News
  • Aims and Scope
  • Privacy Policy
  • Sitemap

 

For Special Issue Proposal : editor.ejmcm21@gmail.com

This journal is licensed under a Creative Commons Attribution 4.0 International (CC-BY 4.0)

Powered by eJournalPlus