Computers and Concrete

  • 제9권3호
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  • Pages.215-226
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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

Utilization of support vector machine for prediction of fracture parameters of concrete

  • Samui, Pijush (Centre for Disaster Mitigation and Management, VIT University) ;
  • Kim, Dookie (Department of Civil Engineering, Kunsan National University)
  • 투고 : 2010.07.19
  • 심사 : 2011.05.26
  • 발행 : 2012.03.25
⟨ 이전 논문 다음 논문 ⟩

초록

This article employs Support Vector Machine (SVM) for determination of fracture parameters critical stress intensity factor ($K^s_{Ic}$) and the critical crack tip opening displacement ($CTOD_c$) of concrete. SVM that is firmly based on the theory of statistical learning theory, uses regression technique by introducing ${\varepsilon}$-insensitive loss function has been adopted. The results are compared with a widely used Artificial Neural Network (ANN) model. Equations have been also developed for prediction of $K^s_{Ic}$ and $CTOD_c$. A sensitivity analysis has been also performed to investigate the importance of the input parameters. The results of this study show that the developed SVM is a robust model for determination of $K^s_{Ic}$ and $CTOD_c$ of concrete.

키워드

참고문헌

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피인용 문헌

  1. Support vector machine for prediction of the compressive strength of no-slump concrete vol.11, pp.4, 2013, https://doi.org/10.12989/cac.2013.11.4.337
  2. Support vector machines in structural engineering: a review vol.21, pp.3, 2015, https://doi.org/10.3846/13923730.2015.1005021
  3. Evaluation of the Productivity of Ready Mixed Concrete Batch Plant Using Artificial Intelligence Techniques vol.901, pp.None, 2012, https://doi.org/10.1088/1757-899x/901/1/012020