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Open AccessArticle

Prediction of Concrete Strength with P-, S-, R-Wave Velocities by Support Vector Machine (SVM) and Artificial Neural Network (ANN)

1
Department of Safety Engineering, Seoul National University of Science and Technology, Seoul 01811, Korea
2
Department of Safety Engineering, Incheon National University, Incheon 22012, Korea
3
Research Institute for Engineering and Technology, Incheon National University, Incheon 22012, Korea
*
Authors to whom correspondence should be addressed.
Appl. Sci. 2019, 9(19), 4053; https://doi.org/10.3390/app9194053
Received: 13 August 2019 / Revised: 18 September 2019 / Accepted: 25 September 2019 / Published: 27 September 2019
(This article belongs to the Special Issue Structural Health Monitoring & Nondestructive Testing 2020)
Mechanical waves, such as ultrasonic waves, have shown promise for use in non-destructive methods used in the evaluation of concrete properties, such as strength and elasticity. However, accurate estimation of the concrete compressive strength is difficult if only the pressure waves (P-waves) are considered, which is common in non-destructive methods. P-waves cannot reflect various factors such as the types of aggregates and cement, the fine aggregate modulus, and the interfacial transition zone, influencing the concrete strength. In this study, shear waves (S-waves) and Rayleigh waves (R-waves) were additionally used to obtain a more accurate prediction of the concrete strength. The velocities of three types of mechanical waves were measured by recent ultrasonic testing methods. Two machine learning models—a support vector machine (SVM) and an artificial neural network (ANN)—were developed within the MATLAB programming environment. Both models were successfully used to model the relationship between the mechanical wave velocities and the concrete compressive strength. The machine learning model that included the P-, S-, and R-wave velocities was more accurate than the model that included only the P-wave velocity. View Full-Text
Keywords: ultrasound; P-wave; S-wave; R-wave; non-destructive method; concrete strength; support vector machine; artificial neural network ultrasound; P-wave; S-wave; R-wave; non-destructive method; concrete strength; support vector machine; artificial neural network
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Park, J.Y.; Yoon, Y.G.; Oh, T.K. Prediction of Concrete Strength with P-, S-, R-Wave Velocities by Support Vector Machine (SVM) and Artificial Neural Network (ANN). Appl. Sci. 2019, 9, 4053.

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