Prediction of Strength of Hybrid Fiber Reinforced Self Compacting Concrete Using Artificial Neural Network
- 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.
Cite this article
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 -