Review paper on concrete mix design optimization using machine learning based algorithm

Open Access

Year : 2024 | Volume : | : | Page : –
By

Vaddeswaram Sangeetha

P Poluraju

  1. Research Scholar , Department of Civil Engineering, KLEF University Andhra Pradesh India
  2. Professor Department of Civil Engineering, KLEF University Andhra Pradesh India

Abstract

Concrete is an essential part of most construction works in civil engineering. The mix design of concrete is usually specified in terms of prescription or performance-based approach. One of the most important procedures is proportioning the concrete mix, which requires taking several safety precautions to get the proper amounts of elements like cement, aggregate, water, and admixtures. The current study offers a thorough analysis of the Artificial Neural Networks (ANN) Model’s suitability for approximating concrete design mix proportioning. The paper also investigates the research void on machine learning algorithm optimization of concrete mix design. Four input parameters, including cement, sand, coarse aggregate, and water, form the basis of the suggested ANN model. It is anticipated that the suggested concrete mix proportion design will decrease the number of experiments conducted in the field and the laboratory, save labor and material costs, and shorten turnaround times while offering increased accuracy. The concrete design is expected to have higher durability and hence is economical.

Keywords: Optimization, Artificial Neural Network, Concrete mix Design

How to cite this article: Vaddeswaram Sangeetha, P Poluraju. Review paper on concrete mix design optimization using machine learning based algorithm. Journal of Polymer and Composites. 2024; ():-.
How to cite this URL: Vaddeswaram Sangeetha, P Poluraju. Review paper on concrete mix design optimization using machine learning based algorithm. Journal of Polymer and Composites. 2024; ():-. Available from: https://journals.stmjournals.com/jopc/article=2024/view=0

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Ahead of Print Open Access Review Article
Volume
Received April 7, 2024
Accepted April 25, 2024
Published July 8, 2024
Views: 17

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