Buy article PDF
The purchased file will be sent to you
via email after the payment is completed.
US$ 35
|
Computers and Concrete Volume 5, Number 5, October 2008 , pages 461-473 DOI: https://doi.org/10.12989/cac.2008.5.5.461 |
|
|
|
Modeling properties of self-compacting concrete: support vector machines approach |
||
Rafat Siddique, Paratibha Aggarwal, Yogesh Aggarwal and S. M. Gupta
|
||
| 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. | ||
| Key Words | ||
| 28-day compressive strength; slump flow; prediction; Support vector machines technique; neural network. | ||
| Address | ||
| Rafat Siddique : Department of Civil Engineering, Thapar University, Patiala, India Paratibha Aggarwal, Yogesh Aggarwal and S. M. Gupta : Department of Civil Engineering, N.I.T. Kurukshetra, India | ||