1 Department of Civil Engineering, Government Polytechnic, Harapanahalli, Karnataka, India.
2 Department of Civil Engineering, Government Polytechnic, Kudligi, Karnataka, India.
3 Department of Computer Science, Engineering, Government Polytechnic, Kudligi, Karnataka, India.
Research Article
World Journal of Advanced Research and Reviews, 2020, 05(01), 128-135
Received on 13 January 2020; Revised 25 January 2020; accepted on 29 January 2020
The aim of the present study is to develop a compressive strength machine learning model that matches the conventional laboratory technique by means of machine learning. The entire operation consists of casting cubes of the 150mm dimension of geopolymer concrete based on the mixture of various Molarities of Ground Granulated Oven Slag. Cube has been evaluated by various laboratory techniques under compression. Data were utilized in machine learning modelling. 80% of the actual data examined were utilized for training and 20% for testing. The modelling is performed in the Python language using linear regression and artificial neural network.
Ground Granulated Blast Furnace Slag (GGBS) pellets; Alkali activators (Sodium silicate and Sodium hydroxide); GC (Geopolymer Concrete);Artificial Intelligence (AI); Machine learning (ML); Linear Regression (LR); Artificial Neural Network (ANN)
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Shruthi H C, Harapanahalli Harsha and Akkasali Neelakantachari. Prediction of GGBS based geopolymer concrete strength by using machine learning. World Journal of Advanced Research and Reviews, 2020, 5(1), 128-135. Article DOI: https://doi.org/10.30574/wjarr.2020.5.1.0016