In this study, we constructed a machine learning model to predict the compressive strength of standard cured specimens and core specimens in high-strength concrete from the material quality, material type, mix proportioning, and construction conditions, in addition to a discussion of factors affecting the strength of the model. The results showed that the machine learning model predicted compressive strength with high accuracy. Furthermore, the effects of factors such as air and maximum temperature of core specimens on the strength were consistent with previous studies, confirming that the model could learn the factors affecting the strength appropriately.
