Characteristic Curve and Its Use in Determining the Compressive Strength of Concrete by the Rebound Hammer Test

Abstract

During the construction of concrete structures, it is often useful to know compressive strength at an early age. This is an amount of strength required for the safe removal of formwork, also known as stripping strength. It is certainly helpful to determine this strength non-destructively, i.e., without any invasive steps that would damage the structure. Second only to the ultrasonic pulse velocity test, the rebound hammer test is the most common NDT method currently used for this purpose. However, estimating compressive strength using general regression models can often yield inaccurate results. The experiment results show that the compressive strength of any concrete can be estimated using one’s own newly created regression model. A traditionally constructed regression model can predict the strength value with 50% reliability, or when two-sided confidence bands are used, with 95% reliability. However, civil engineers usually work with the so-called characteristic value defined as a 5% quantile. Therefore, it appears suitable to adjust conventional methods in order to achieve a regression model with 95% one-sided reliability. This paper describes a simple construction of such a characteristic curve. The results show that the characteristic curve created for the concrete in question could be a useful tool even outside of practical applications.

Design of Experiment on Concrete Mechanical Properties Prediction: A Critical Review

“…The experiment returned an R 2 value above 0.980 for all three conditions in the study, indicating that the regression model could be used for prediction with minimal deviation. A similar conclusion on non-destructive test was stated in the study by Kocáb, Misák, and Cikrle [ 32 ]; while a linear regression with high R 2 does not invalidate more complicated expression, it can be used for achieving great effects within the scope of the study, and it is often sufficient. In another study [ 33 ], a similar regression with a single variable, namely concrete age, is used to predict the concrete strength under different curing conditions.…”

Non-Destructive Testing of Materials in Civil Engineering

“…However, estimating compressive strength using general regression models can often yield inaccurate results. The experiment results in paper [39] show that the compressive strength of any concrete can be estimated using one’s own newly created regression model. A traditionally constructed regression model can predict the strength value with 50% reliability, or when two-sided confidence bands are used, with 95% reliability.…”

“…This paper describes a simple construction of such a characteristic curve. The results show that the characteristic curve created for the concrete in question could be a useful tool, even outside of practical application [39].…”

Adapting Artificial Intelligence to Improve In Situ Concrete Compressive Strength Estimations in Rebound Hammer Tests

“…Others have attempted different prediction methods to better correlate the RH value with actual compressive strength. Among them, traditional statistical regressions are the most popular methods adopted by the researchers (Hajjeh, 2012;Rojas-Henao et al, 2012;El Mir and Nehme, 2017;Xu and Li, 2018;Kocáb et al, 2019). In recent years, nontraditional statistical regression methods, such as ANNs, are reported to have better compressive strength estimations when compared to traditional regression methods (Yılmaz and Yuksek, 2008;Iphar, 2012;Asteris and Mokos, 2020).…”

“…Previous research has attempted different approaches to investigate the relationship between RH measurements and actual compressive strength. To achieve this goal, many researchers adopt linear and nonlinear statistical regressions to improve the concrete compressive strength estimation in the RH test (Hajjeh, 2012;Rojas-Henao et al, 2012;El Mir and Nehme, 2017;Xu and Li, 2018;Kocáb et al, 2019). In addition, some researchers have successfully adopted nontraditional statistical methods, such as artificial neural networks (ANNs), to improve concrete compressive strength estimations in RH tests (Yılmaz and Yuksek, 2008;Iphar, 2012;Asteris and Mokos, 2019).…”