There has been increasing interest in UHPC (Ultra-High Performance Concrete) materials in recent years. Owing to the superior mechanical properties and durability, the UHPC has been widely used for the design of various types of structures. In this paper, machine learning based compressive strength prediction methods of the UHPC are proposed. Various regression-based machine learning models were built to train dataset. For train and validation, 110 data samples collected from the literatures were used. Because the proportion between the compressive strength and its composition is a highly nonlinear, more advanced regression models are demanded to obtain better results. The complex relationship between mixture proportion and concrete compressive strength can be predicted by using the selected regression method.

Abstract
1. 서론
2. 데이터 세트
    2.1 110 데이터 샘플
    2.2 데이터 샘플의 통계적 분석
3. 특성 엔지니어링
    3.1 특성 선별
    3.2 특성 구축
4. UHPC 데이터를 위한 회귀모델
    4.1 CatBoost
    4.2 평가 기법
    4.3 하이퍼파라미터 조정
5. 결과 및 고찰
6. 결론
References

• 이승혜(세종대학교 건축공학과) | Lee Seunghye (Dept. of Architectural Engineering, Sejong Univ.)
• 이재홍(세종대학교 건축공학과) | Lee Jaehong (Dept. of Architectural Engineering, Sejong Univ.) 교신저자