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Modeling properties of self-compacting concrete: support vector machines approach

  • Siddique, Rafat (Department of Civil Engineering, Thapar University) ;
  • Aggarwal, Paratibha (Department of Civil Engineering, N.I.T. Kurukshetra) ;
  • Aggarwal, Yogesh (Department of Civil Engineering, N.I.T. Kurukshetra) ;
  • Gupta, S.M. (Department of Civil Engineering, N.I.T. Kurukshetra)
  • Received : 2007.06.26
  • Accepted : 2008.07.29
  • Published : 2008.10.25

Abstract

The paper explores the potential of Support Vector Machines (SVM) approach in predicting 28-day compressive strength and slump flow of self-compacting concrete. Total of 80 data collected from the exiting literature were used in present work. To compare the performance of the technique, prediction was also done using a back propagation neural network model. For this data-set, RBF kernel worked well in comparison to polynomial kernel based support vector machines and provide a root mean square error of 4.688 (MPa) (correlation coefficient=0.942) for 28-day compressive strength prediction and a root mean square error of 7.825 cm (correlation coefficient=0.931) for slump flow. Results obtained for RMSE and correlation coefficient suggested a comparable performance by Support Vector Machine approach to neural network approach for both 28-day compressive strength and slump flow prediction.

Keywords

References

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