For accurate design, construction, and maintenance, it is important to identify the elastic modulus of concrete. This is usually achieved using a destructive test based on American Society for Testing and Materials (ASTM) C469. However, obtaining an appropriate static elastic modulus (
Ec
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For accurate design, construction, and maintenance, it is important to identify the elastic modulus of concrete. This is usually achieved using a destructive test based on American Society for Testing and Materials (ASTM) C469. However, obtaining an appropriate static elastic modulus (
Ec) requires many specimens, and the testing is difficult and time-consuming. Thus, a dynamic elastic modulus (
Ed) is often obtained through a natural frequency for a specific size (e.g., the longitudinal (LT) or transverse (TR) mode) based on a resonance frequency test. However, this method uses a gradient at a very low-stress part of the stress–strain curve and assumes a completely elastic body. In fact, the initial chord elastic modulus (
Ei) of the stress–strain curve in a concrete fracture test differs from the Ed, owing to the non-homogeneity and inelasticity of the concrete. The
Ei of the experimental value may be more accurate. In this study, the
Ei was predicted using machine learning methods for natural frequencies. The prediction accuracy for
Ei was analyzed based on f1–f4, as calculated through the LT and TR modes. The predicted
Ei had higher correlations with the actual
Ec and compressive strength (
fc) than
Ed. Thus, more accurate prediction of concrete mechanical properties is possible.
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