Computers and Concrete

  • 제10권2호
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  • Pages.197-217
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  • 2012
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  • 1598-8198(pISSN)
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  • 1598-818X(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Prediction of compressive strength of concrete using neural networks

  • Al-Salloum, Yousef A. (Specialty Units for Safety and Preservation of Structures, Department of Civil Engineering, King Saud University) ;
  • Shah, Abid A. (Specialty Units for Safety and Preservation of Structures, Department of Civil Engineering, King Saud University) ;
  • Abbas, H. (Specialty Units for Safety and Preservation of Structures, Department of Civil Engineering, King Saud University) ;
  • Alsayed, Saleh H. (Specialty Units for Safety and Preservation of Structures, Department of Civil Engineering, King Saud University) ;
  • Almusallam, Tarek H. (Specialty Units for Safety and Preservation of Structures, Department of Civil Engineering, King Saud University) ;
  • Al-Haddad, M.S. (Specialty Units for Safety and Preservation of Structures, Department of Civil Engineering, King Saud University)
  • 투고 : 2010.08.03
  • 심사 : 2012.03.07
  • 발행 : 2012.08.25
⟨ 이전 논문 다음 논문 ⟩

초록

This research deals with the prediction of compressive strength of normal and high strength concrete using neural networks. The compressive strength was modeled as a function of eight variables: quantities of cement, fine aggregate, coarse aggregate, micro-silica, water and super-plasticizer, maximum size of coarse aggregate, fineness modulus of fine aggregate. Two networks, one using raw variables and another using grouped dimensionless variables were constructed, trained and tested using available experimental data, covering a large range of concrete compressive strengths. The neural network models were compared with regression models. The neural networks based model gave high prediction accuracy and the results demonstrated that the use of neural networks in assessing compressive strength of concrete is both practical and beneficial. The performance of model using the grouped dimensionless variables is better than the prediction using raw variables.

키워드

참고문헌

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