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Open AccessArticle

Prediction of Static Modulus and Compressive Strength of Concrete from Dynamic Modulus Associated with Wave Velocity and Resonance Frequency Using Machine Learning Techniques

1
Department of Safety Engineering, Seoul National University of Science and Technology, Seoul 01811, Korea
2
School of Civil, Architectural Engineering and Landscape Architecture, Sungkyunkwan University, Suwon 16419, Korea
3
Department of Safety Engineering, Incheon National University, Incheon 22012, Korea
4
Research Institute for Engineering and Technology, Incheon National University, Incheon 22012, Korea
*
Authors to whom correspondence should be addressed.
Materials 2020, 13(13), 2886; https://doi.org/10.3390/ma13132886
Received: 27 May 2020 / Revised: 19 June 2020 / Accepted: 24 June 2020 / Published: 27 June 2020
(This article belongs to the Special Issue Testing of Materials and Elements in Civil Engineering)
The static elastic modulus (Ec) and compressive strength (fc) are critical properties of concrete. When determining Ec and fc, concrete cores are collected and subjected to destructive tests. However, destructive tests require certain test permissions and large sample sizes. Hence, it is preferable to predict Ec using the dynamic elastic modulus (Ed), through nondestructive evaluations. A resonance frequency test performed according to ASTM C215-14 and a pressure wave (P-wave) measurement conducted according to ASTM C597M-16 are typically used to determine Ed. Recently, developments in transducers have enabled the measurement of a shear wave (S-wave) velocities in concrete. Although various equations have been proposed for estimating Ec and fc from Ed, their results deviate from experimental values. Thus, it is necessary to obtain a reliable Ed value for accurately predicting Ec and fc. In this study, Ed values were experimentally obtained from P-wave and S-wave velocities in the longitudinal and transverse modes; Ec and fc values were predicted using these Ed values through four machine learning (ML) methods: support vector machine, artificial neural networks, ensembles, and linear regression. Using ML, the prediction accuracy of Ec and fc was improved by 2.5–5% and 7–9%, respectively, compared with the accuracy obtained using classical or normal-regression equations. By combining ML methods, the accuracy of the predicted Ec and fc was improved by 0.5% and 1.5%, respectively, compared with the optimal single variable results. View Full-Text
Keywords: concrete; static elastic modulus; dynamic elastic modulus; compressive strength; machine learning; P-wave; S-wave; resonance frequency test; nondestructive method concrete; static elastic modulus; dynamic elastic modulus; compressive strength; machine learning; P-wave; S-wave; resonance frequency test; nondestructive method
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Park, J.Y.; Sim, S.-H.; Yoon, Y.G.; Oh, T.K. Prediction of Static Modulus and Compressive Strength of Concrete from Dynamic Modulus Associated with Wave Velocity and Resonance Frequency Using Machine Learning Techniques. Materials 2020, 13, 2886.

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