Abstract
Non-destructive testing (NDT) methods are important alternatives when destructive tests are not feasible to examine the in situ concrete properties without damaging the structure. The rebound hammer test and the ultrasonic pulse velocity test are two popular NDT methods to examine the properties of concrete. The rebound of the hammer depends on the hardness of the test specimen and ultrasonic pulse travelling speed is related to density, uniformity, and homogeneity of the specimen. Both of these two methods have been adopted to estimate the concrete compressive strength. Statistical analysis has been implemented to establish the relationship between hammer rebound values/ultrasonic pulse velocities and concrete compressive strength. However, the estimated results can be unreliable. As a result, this research proposes an Artificial Intelligence model using support vector machines (SVMs) for the estimation. Data from 95 cylinder concrete samples are collected to develop and validate the model. The results show that combined NDT methods (also known as SonReb method) yield better estimations than single NDT methods. The results also show that the SVMs model is more accurate than the statistical regression model.
Highlights
To determine the actual compressive strength of cast concrete, destructive tests are the most reliable methods
In order to improve the non-destructive test (NDT) for concrete compressive strength estimation, an artificial intelligence-based approach is proposed for the combined NDT method (SonReb), which consists of rebound hammer and ultrasonic pulse velocity tests
Among the 95 test samples, 85 of them are randomly selected as the training dataset for the proposed support vector machines (SVMs) model development
Summary
To determine the actual compressive strength of cast concrete, destructive tests are the most reliable methods. Combined methods (combining rebound hammer tests and ultrasonic pulse velocity tests, known as SonReb) are proposed by researchers to improve the concrete compressive strength estimations and positive results are obtained [4,5,6,7,8,9]. Most of these previous researches attempted to relate the concrete compressive strength with rebound hammer values and ultrasonic pulse velocity using statistical regression (linear or non-linear) analysis. Results from SVMs model are compared with the results from traditional statistical regression analysis
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