Prediction of Compressive Strength Using Support Vector Regression
At the design stage of a structure, the members of adequate dimension and strength is provided. But with passage of time, the strength of the members reduces gradually due to exposure to environmental conditions and unexpected loadings other than for which the structure is designed. Non Destructive Testing (NDT) method provides a convenient and rapid method of determination of existing strength of concrete without subjecting the member to any damage. In the present study, Support Vector Regression (SVR) in Python has been used for the prediction of compressive strength of concrete. Three different NDT techniques have been used as input for the SVR model. A good co-relation between predicted strength and strength determined after crushing the concrete cubes has been achieved. It has also been observed that accuracy in the predicted strength is more in case of inputs from more than one NDT technique is used.
Sanchez, K. and Tarranza, N. 2014. Reliability of Rebound Hammer Test in Concrete Compressive Strength Estimation. International Journal of Advances in Agricultural & Environmental Engineering 1, 2, pp. 198–202.
Szilágyi, K., Borosnyói, A., and Zsigovics, I. 2015. Understanding the rebound surface hardness of concrete. Journal of Civil Engineering & Management 21, 2, pp. 185–192.
Bogas, J. A., Gomes, M. G., and Gomes, A. 2013. Compressive strength evaluation of structural lightweight concrete by non-destructive ultrasonic pulse velocity method. Ultrasonics 53, 5, pp. 962–972.
Trtnik, G., Kavcic, F., and Turk, G. 2009. Prediction of concrete strength using ultrasonic pulse velocity and artificial neural networks. Ultrasonics 49, 1, pp. 53–60.
Nash't, I. H., A'bour, S. H., and Sadoon, A. A. 2005. Finding an Unified Relationship between Crushing Strength of Concrete and Non-destructive Tests. In Middle East Nondestructive Testing Conference & Exhibition. 27-30 Nov 2005 Bahrain, Manama.
Jain, A., Kathuria, A., Kumar, A., Verma, Y. and Murari, K. 2013. Combined Use of Non-Destructive Tests for Assessment of Strength of Concrete in Structure. Procedia Engineering 54, pp. 241–251.
Benyahia, K. A., Sbartaï, Z. M., Breysse, D., and Ghrici, S. K. M. 2017. Analysis of the single and combined non-destructive test approaches for on-site concrete strength assessment: General statements based on a real case-study. Case Studies in Construction Materials 6, pp.109–119.
Yeh, I. C. 1998. Modeling Of Strength Of High-Performance Concrete Using Artificial Neural Networks. Cement and Concrete Research 28, 12, pp. 1797–1808.
Topcu,I. B. and Sarıdemir, M. 2008. Prediction of compressive strength of concrete containing fly ash using artificial neural networks and fuzzy logic. Computational Materials Science 41, 3, pp. 305–311.
Pham, A. D., Hoang, N. H. and Nguyen, Q. T. 2015. Predicting Compressive Strength of High-Performance Concrete Using Metaheuristic-Optimized Least Squares Support Vector Regression. Journal of Computing in Civil Engineering 30, 3, No. 06015002.
Vapnik, V. N. 1995. The Nature of Statistical Learning Theory. Springer, New York.
Vapnik, V. N. 1999. An overview of Statistical Learning Theory. IEEE Transactions on Neural Networks 10, 5, pp. 988–999.
Copyright (c) 2019 MENDEL
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
MENDEL open access articles are normally published under a Creative Commons Attribution-NonCommercial-ShareAlike (CC BY-NC-SA 4.0) https://creativecommons.org/licenses/by-nc-sa/4.0/ . Under the CC BY-NC-SA 4.0 license permitted 3rd party reuse is only applicable for non-commercial purposes. Articles posted under the CC BY-NC-SA 4.0 license allow users to share, copy, and redistribute the material in any medium of format, and adapt, remix, transform, and build upon the material for any purpose. Reusing under the CC BY-NC-SA 4.0 license requires that appropriate attribution to the source of the material must be included along with a link to the license, with any changes made to the original material indicated.