Computers and Concrete

  • 제32권3호
  • /
  • Pages.263-286
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  • 2023
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  • 1598-8198(pISSN)
  • /
  • 1598-818X(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

An insight into the prediction of mechanical properties of concrete using machine learning techniques

  • Neeraj Kumar Shukla (Electrical Engineering Department, College of Engineering, King Khalid University) ;
  • Aman Garg (Department of Multidisciplinary Engineering, The NorthCap University) ;
  • Javed Bhutto (Electrical Engineering Department, College of Engineering, King Khalid University) ;
  • Mona Aggarwal (Department of Multidisciplinary Engineering, The NorthCap University) ;
  • M.Ramkumar Raja (Electrical Engineering Department, College of Engineering, King Khalid University) ;
  • Hany S. Hussein (Electrical Engineering Department, College of Engineering, King Khalid University) ;
  • T.M. Yunus Khan (Mechanical Engineering Department, College of Engineering, King Khalid University) ;
  • Pooja Sabherwal (Department of Multidisciplinary Engineering, The NorthCap University)
  • 투고 : 2023.02.22
  • 심사 : 2023.05.15
  • 발행 : 2023.09.25
⟨ 이전 논문 다음 논문 ⟩

초록

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.

키워드

과제정보

The authors gratefully acknowledge their respective organizations for their help and support. The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University (KKU), Kingdom of Saudi Arabia for funding this work through General Research Project under the grant number "RGP1/70/44".

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