Compressive Strength Prediction Model of High Strength Concrete by Destructive and Nondestructive Technique

¹ Department of Civil Engineering, IES College of Technology, Bhopal, Madhya Pradesh, India.
² Hilla University College, Babylon, Iraq.
³ Department of Information Technology, GRIET, Bachupally, Hyderabad, Telangana, India.
⁴ Department of Computer Science, New Horizon College of Engineering, Bangalore.
⁵ Lovely Professional University, Phagwara, India.
⁶ Lloyd Institute of Engineering & Technology, Knowledge Park II, Greater Noida, Uttar Pradesh, India.
⁷ Department of Mechanical Engineering, MLR Institute of Technology, Hyderabad, Telangana, India - 500065

^* Corresponding Author: inprint@yahoo.com

Abstract

Concrete’s compressive strength can be tested in a laboratory before construction begins. Since concrete is a natural material and cannot be destroyed, it is not possible to determine its compressive strength through destructive testing. Rebound hammers are typically used in the field to evaluate the structural elements’ ability to withstand hardened concrete. As part of the current study, a comparison was made between concrete’s compressive strength measured by destructive testing and its surface hardness measured by rebound hammering. Tests were conducted on laboratory-made concrete cubes in this study to determine destructive and non-destructive behavior. Minitab software was used for regression analysis. Schmidt rebound hammer tests, a type of nondestructive testing (NDT), were shown to have very strong relationships with concrete destructive compression tests. Schmidt rebound hammers are commonly used to measure the surface hardness of concrete, since the hammer rebound number and concrete strength are theoretically correlated. Utilising a Schmidt hammer, it was applied. Standard concrete cubes with crushing strengths between 20 and 30 MPa were created using various mix proportions. Using regression analysis, destructive and non-destructive values are correlated. The linear regression equation is well suited for obtaining the compressive strength using rebound value by using linear regression equation.