Proceedings of the International Conference on Computational Innovations and Emerging Trends (ICCIET- 2024)

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Prediction of Strength of Hybrid Fiber Reinforced Self Compacting Concrete Using Artificial Neural Network

Authors
Santosh Itani1, Kusuma Sundara Kumar2, *, B. Kameswari3
1M.Tech Student, Department of Civil Engineering, Bonam Venkata Chalamayya Engineering College, Odalarevu Konaseema, Andhra Pradesh, India
2Professor, Dept. of R &D, Bonam Venkata Chalamayya Engineering College, Odalarevu Konaseema, Andhra Pradesh, India
3Professor, Dept. of Civil Engineering, Bonam Venkata Chalamayya Engineering College, Odalarevu Konaseema, Andhra Pradesh, India
*Corresponding author. Email: skkusuma123@gmail.com
Corresponding Author
Kusuma Sundara Kumar
Available Online 30 July 2024.
DOI
10.2991/978-94-6463-471-6_62How to use a DOI?
Keywords
FRC; SCC; Hybrid concrete; Strength properties; Prediction; ANN
Abstract

A Hybrid Fiber-Reinforced Self-Compacting Concrete (HFRSCC) is a new type of building material that combines the benefits of SCC with the additional benefit of fibres. The brittle SCC was transformed into a ductile material with the ideal amount of fibres; as a result, it flows into the formwork’s interior with ease, passes through barriers, and compacts under its own weight. The Artificial Neural Network (ANN) has garnered increasing attention in the last several decades due to its capacity to handle multivariate analysis. As a result, the ANN model was created to ascertain the FRSCC’s mechanical characteristics. A new JO-m Sigmoid ANN is employed for predicting the mechanical characteristics of SCC concrete that is 80 MPa and 60 MPa with the adding of 0.75 and 0.75 percent hybrid steel fibre. Using experimental data and four distinct datasets (datasets-1, 2, 3, and 4) the suggested model was verified. Regarding the different datasets, the suggested model demonstrated enhanced prediction ability in the range of 0.01% to 15.56%.

Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the International Conference on Computational Innovations and Emerging Trends (ICCIET- 2024)
Series
Advances in Computer Science Research
Publication Date
30 July 2024
ISBN
10.2991/978-94-6463-471-6_62
ISSN
2352-538X
DOI
10.2991/978-94-6463-471-6_62How to use a DOI?
Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Santosh Itani
AU  - Kusuma Sundara Kumar
AU  - B. Kameswari
PY  - 2024
DA  - 2024/07/30
TI  - Prediction of Strength of Hybrid Fiber Reinforced Self Compacting Concrete Using Artificial Neural Network
BT  - Proceedings of the International Conference on Computational Innovations and Emerging Trends (ICCIET- 2024)
PB  - Atlantis Press
SP  - 639
EP  - 649
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-471-6_62
DO  - 10.2991/978-94-6463-471-6_62
ID  - Itani2024
ER  -