Application of Machine Learning in Prediction of Strength Properties of GGBS based Geopolymer Concrete

DOI

10.2991/978-94-6463-471-6_61How to use a DOI?

Keywords

Prediction; Strength Properties; GGBS; Geopolymer Concrete; Machine Learning

Abstract

The current study is one such initiative to analyze the effect of heat curing in geopolymer concrete made of Ground Granulated Blast Furnace Slag (GGBS) as base material. With higher sodium hydroxide concentrations (14M and 16M) and different alkaline activator ratios (1, 1.5, 2, and 2.5), the GGBS-based geopolymer concrete is examined to ascertain the strength, durability, flexural characteristics. The tests were conducted under exposed elevated temperature curing, which ranges from 100℃ to 800℃. The proposed mixture planned for this study was 1:1.40:3.28:0.40. The combination contains of GGBS, river sand, coarse aggregate and the alkaline activator solution. To maintain the workability, 3% of water and 1% of superplasticizer were used in the mixture. After casting, all the geopolymer samples were heat cured at 60℃ for 24 hours and kept in ambient temperature for 24 hours and exposed to 100ºC and 800ºC elevated temperature curing with an exposure of 1 to 6 hours using muffle furnace of 1000ºC capacity. The mechanical, durability and flexural properties of the specimens were studied and resulted the impact of elevated temperature and its exposure in the GGBS based geopolymer concrete. The theoretical relationships between mechanical properties have been developed with respect to elevated temperature and derived equations for split tensile strength (0.52 √fck for both GP14M and GP16M) and flexural strength (1.03 √fck for GP14M and 1.08 √fck for GP16M). Machine Learning concept has been adopted to predict the mechanical properties based on different dependent variables such as alkaline ratio, molar concentration, temperature exposure and elevated temperature. The predicted results were highly compatible with experimental and theoretical investigations.

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  - Uttam Baral
AU  - Rahul Kumar Singh
AU  - Kusuma Sundara Kumar
PY  - 2024
DA  - 2024/07/30
TI  - Application of Machine Learning in Prediction of Strength Properties of GGBS based Geopolymer Concrete
BT  - Proceedings of the International Conference on Computational Innovations and Emerging Trends (ICCIET- 2024)
PB  - Atlantis Press
SP  - 629
EP  - 638
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-471-6_61
DO  - 10.2991/978-94-6463-471-6_61
ID  - Baral2024
ER  -