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논문 기본 정보

자료유형
학술저널
저자정보
Sangwoo Kim (Gyeongsang National University) Jinsup Kim (Gyeongsang National University) Jaeho Shin (Gyeongsang National University) Youngsoon Kim (Gyeongsang National University)
저널정보
국제구조공학회 Structural Engineering and Mechanics, An Int'l Journal Structural Engineering and Mechanics, An Int'l Journal Vol.91 No.6
발행연도
2024.9
수록면
583 - 589 (7page)

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초록· 키워드

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Accurately predicting the strength of concrete is vital for ensuring the safety and durability of structures, thereby contributing to time and cost savings throughout the design and construction phases. The compressive strength of concrete is determined by various material factors, including the type of cement, composition ratios of concrete mixtures, curing time, and environmental conditions. While mix design establishes the proportions of each material for concrete, predicting strength before experimental measurement remains a challenging task. In this study, Abrams’s law was chosen as a representative investigative approach to estimating concrete compressive strength. Abrams asserted that concrete compressive strength depends solely on the water-cement ratio and proposed a logarithmic linear relationship. However, Abrams’s law is only applicable to concrete using cement as the sole binding material and may not be suitable for modern concrete mixtures. Therefore, this research aims to predict concrete compressive strength by applying various conventional regression analyses and machine learning methods. Six models were selected based on performance experiment data collected from various literature sources on different concrete mixtures. The models were assessed using Root Mean Squared Error (RMSE) and coefficient of determination (R2) to identify the optimal model.

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