Forecasting of Crushing Strength of Sustainable Concrete by Employing Deep and Random Forest Machine Learning

Open Access

Year : 2024 | Volume : 12 | Special Issue 07 | Page : 41 46
    By

    Pravesh Kumar Tiwari,

  • Manvendra Verma,

  1. Lecturer, Department of Civil Engineering, GLA University Mathura, Uttar Pradesh, India
  2. Assistant Professor, Department of Civil Engineering, GLA University Mathura, Uttar Pradesh, India

Abstract

Sustainable concrete is one of the milestone of the concrete industry. This concrete fulfills the requirements of concrete manufacturing industry such as strengthen, Durability, environment friendly and many of other. With this properties of concrete, sustainable concrete is an ideal substitute for ordinary concrete in the concrete industry. In the 21th century Machine learning is a tool which is use to employ the characteristics of sustainable concrete by using deep learning and random forest algorithm and comparing their error and coefficient of correlation. Finding the results and value of properties of concrete conventional method is so time taking and laborious. The goal of this study is to fortune telling of the strength of sustainable concrete by machine learning tool.

Keywords: Sustainable concrete, machine learning, deep learning, random forest algorithm, compressive strength.

[This article belongs to Special Issue under section in Journal of Polymer and Composites (jopc)]

How to cite this article:
Pravesh Kumar Tiwari, Manvendra Verma. Forecasting of Crushing Strength of Sustainable Concrete by Employing Deep and Random Forest Machine Learning. Journal of Polymer and Composites. 2024; 12(07):41-46.
How to cite this URL:
Pravesh Kumar Tiwari, Manvendra Verma. Forecasting of Crushing Strength of Sustainable Concrete by Employing Deep and Random Forest Machine Learning. Journal of Polymer and Composites. 2024; 12(07):41-46. Available from: https://journals.stmjournals.com/jopc/article=2024/view=181661


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Special Issue Open Access Original Research
Volume 12
Special Issue 07
Received 23/04/2024
Accepted 08/08/2024
Published 15/10/2024
Publication Time 175 Days
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