Prediction of Compressive Strength Using Support Vector Regression

Sai, Goutham J; Singh, Vijay Pal

Prediction of Compressive Strength Using Support Vector Regression

Files

2019-1-051.pdf(1.56 MB)

Date

2019-06-24

Authors

Sai, Goutham J

Singh, Vijay Pal

Publisher

Institute of Automation and Computer Science, Brno University of Technology

Altmetrics

Abstract

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.

Keywords

rebound hammer, ultrasonic pulse velocity, penetration, support vector regression, NDT

Citation

Mendel. 2018 vol. 25, č. 1, s. 51-56. ISSN 1803-3814
https://mendel-journal.org/index.php/mendel/article/view/78

Document type

Peer-reviewed

Document version

Published version

Language of document

en

Document licence

Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International license
http://creativecommons.org/licenses/by-nc-sa/4.0