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Abstract            Volume:10  Issue-7  Year-2022         Original Research Articles

Online ISSN : 2347 - 3215
Issues : 12 per year
Publisher : Excellent Publishers
Email : editorijcret@gmail.com

Modelling of Concrete Strength Properties using the Artificial Intelligence tool of MATLAB
Anaa Pathak1 and Raana Pathak2
1Manik Bagh, Indore, India
2Department of Civil Engineering, SVITS, SVVV, Indore, India
*Corresponding author

Two developing data mining approaches, Artificial Neural Networks (ANNs) and Genetic Programming, are used in this study to construct compressive strength prediction models (GP). Experiments were conducted in the laboratory under standard controlled settings at 7th, 14th and 28th day curing periods to collect data for analysis and model building. The created models were also put to the test using in-situ data from the literature. A comparison of the prediction results obtained using both models is studied, and it can be concluded that the ANN model with the training function Levenberg-Marquardt (LM) is the best prediction tool for the prediction of concrete compressive strength. Cement, water, and aggregates are the three main ingredients in concrete. The main goal in proportioning these elements is to make concrete that is strong enough. Because concrete is such a complex material, predicting compressive strength is a complex process. This research proposes an Artificial Intelligence model for predicting concrete strength at various ages, which will undoubtedly save time, material, and money. Cement, sand, water, coarse aggregates, fine aggregates, and fineness modulus are all inputs to the suggested model. The data used in this study was collected, and the network was trained using a backpropagation approach

Keywords: Artificial Intelligence, Genetic Programming, Properties of Concrete, Concrete compressive strength.
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How to cite this article:

Anaa Pathak and Raana Pathak. 2022. Modelling of Concrete Strength Properties using the Artificial Intelligence tool of MATLAB.Int.J.Curr.Res.Aca.Rev. 10(7): 70-80
doi: https://doi.org/10.20546/ijcrar.2022.1007.004
Copyright: This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike license.