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.

中文翻译：

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ASTM C 215共振频率测试预测初始弦弹性模量及其应用

对于准确的设计，施工和维护，确定混凝土的弹性模量很重要。通常使用基于美国材料试验学会（ASTM）C469的破坏性试验来实现。然而，获得合适的静态弹性模量（Ec）需要许多样本，并且测试困难且耗时。因此，通常基于共振频率测试通过固有频率对于特定尺寸（例如，纵向（LT）或横向（TR）模式）获得动弹性模量（Ed）。但是，这种方法在应力-应变曲线的非常低应力部分使用了梯度，并假定其为完全弹性体。实际上，初始弦弹性模量（Ei）由于混凝土的非均质性和非弹性性，混凝土断裂试验中的应力-应变曲线不同于Ed。实验值的Ei可能更准确。在本研究中，使用自然频率的机器学习方法预测了Ei。通过LT和TR模式计算得出的f1-f4分析了Ei的预测精度。预测的Ei与实际的Ec和抗压强度（fc）的关系高于Ed。因此，可以更准确地预测混凝土的机械性能。