Prediction of Strength of High Volume Fly ash Concrete Using Artificial Neural Networks

DOI

10.2991/978-94-6463-471-6_19How to use a DOI?

Keywords

ANN; Strength; Durability; High volume Fly ash; concrete

Abstract

The purpose of this research project is to evaluate the strength and durability of concrete that has been prepared using groundwater and treated water. Concrete's strength is evaluated using a variety of strength metrics, including split tensile strength, bond strength, and compression test results. By evaluating the corrosion in the steel reinforcement, the durability of the concrete is examined. This experiment also sought to predict strength using artificial neural networks. In this study, we aim to use artificial neural networks (ANN) to predict the values of compressive strength of 28 days, 56 days and 90 days, 100 h half-cell potential and water absorption,. The main goal is to anticipate compressive strength. Regarding laboratory outcomes, the ANN model's output yields both positive and negative variations. The range of the positive variances is 3.44% to 8.22%. The negative variances are between 2.02% and 11.35% of the total. The outputs of the ANN model may be used to calculate the 28-day, 56-day, 90-day, water absorption of concrete in hardened state, 100 h half-cell potential from properties of fresh concrete and 3-day compressive strength. Since the outcomes are good, the ANN prediction model can be adopted as a reference to forecast the strength properties of concrete three days after the concrete has started to be laid.

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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TY  - CONF
AU  - Nunna Raj Kumar
AU  - Kajuluri Sai Anitha
AU  - Kusuma Sundara Kumar
PY  - 2024
DA  - 2024/07/30
TI  - Prediction of Strength of High Volume Fly ash Concrete Using Artificial Neural Networks
BT  - Proceedings of the International Conference on Computational Innovations and Emerging Trends (ICCIET- 2024)
PB  - Atlantis Press
SP  - 190
EP  - 200
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-471-6_19
DO  - 10.2991/978-94-6463-471-6_19
ID  - Kumar2024
ER  -