PURPOSES : In this study, an optimal model for compressive strength prediction was derived by learning and directly comparing several machine learning models based on the same data. METHODS : Approximately 478 pieces of concrete compressive strength data were obtained to compare the performance of the machine learning models. In addition, five machine learning models were trained based on the obtained data. The performance of the learned model was compared using three performance indicators. Finally, the performance of the model trained using additional data was reviewed. RESULTS : As a result of comparing the performance of machine learning models, the XGB(eXtra Gradient Boost) model showed the best performance. In addition, as a result of the verification based on additional data, highly reliable results can be obtained if the XGB model is used to predict the compressive strength of concrete. CONCLUSIONS : If a concrete strength prediction model is derived based on a machine learning model, a highly reliable model can be derived.

1. 서론
2. 기계학습 모델 선정
    2.1. 데이터 분석
    2.2. 기계학습 모델
3. 기계학습을 활용한 압축강도 예측 성능 검토
    3.1. 비학습 데이터를 활용한 기계학습 모델의 성능 검토
    3.2. XGB 모델의 압축강도 예측 성능 검토
4. 결론
REFERENCES

• 이빛나(한국건설기술연구원 전임연구원, 한양대학교 공과대학 건설환경공학과 박사과정) | Lee Binna (researcher specialist Department of structural engineering research, KICT, 283, Goyangdae-Ro, Ilsanseo-Gu, Gyeonggi-Do 10223, Korea) Corresponding author
• 유재석(한양대학교 공과대학 건설환경공학과 교수) | Ryou JaeSuk