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Computers and Concrete
  Volume 32, Number 3, September 2023 , pages 263-286
DOI: https://doi.org/10.12989/cac.2023.32.3.263
 


An insight into the prediction of mechanical properties of concrete using machine learning techniques
Neeraj Kumar Shukla, Aman Garg, Javed Bhutto, Mona Aggarwal, M.Ramkumar Raja,Hany S. Hussein, T.M. Yunus Khan and Pooja Sabherwal

 
Abstract
    Experimenting with concrete to determine its compressive and tensile strengths is a laborious and time-consuming operation that requires a lot of attention to detail. Researchers from all around the world have spent the better part of the last several decades attempting to use machine learning algorithms to make accurate predictions about the technical qualities of various kinds of concrete. The research that is currently available on estimating the strength of concrete draws attention to the applicability and precision of the various machine learning techniques. This article provides a summary of the research that has previously been conducted on estimating the strength of concrete by making use of a variety of different machine learning methods. In this work, a classification of the existing body of research literature is presented, with the classification being based on the machine learning technique used by the researchers. The present review work will open the horizon for the researchers working on the machine learning based prediction of the compressive strength of concrete by providing the recommendations and benefits and drawbacks associated with each model as determining the compressive strength of concrete practically is a laborious and time-consuming task.
 
Key Words
    ANN; compressive strength; hybrid techniques; IoT; machine learning in concrete; SVM
 
Address
Neeraj Kumar Shukla, Javed Bhutto and M.Ramkumar Raja: Electrical Engineering Department, College of Engineering, King Khalid University, Abha 61421, Kingdom of Saudi Arabia
Aman Garg, Mona Aggarwal and Pooja Sabherwal: Department of Multidisciplinary Engineering, The NorthCap University, Gurugram, Haryana, India - 122017
Hany S. Hussein: 1) Electrical Engineering Department, College of Engineering, King Khalid University, Abha 61421, Kingdom of Saudi Arabia, 2) Electrical Engineering Department, Faculty of Engineering, Aswan University, Aswan 81528, Egypt
T.M. Yunus Khan: Mechanical Engineering Department, College of Engineering, King Khalid University, Abha 61421, Kingdom of Saudi Arabia
 

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