Concrete has recently been modified to have various performance and properties. However, the conventional method for predicting the compressive strength of concrete has been suggested by considering only a few influential factors. so, In this study, nine influential factors (W/B ratio, Water, Cement, Aggregate(Coarse, Fine), Fly ash, Blast furnace slag, Curing temperature, and humidity) of papers opened for 10 years were collected at 4 conferences in order to know the various correlations among data and the tendency of data. The selected mixture and compressive strength data were used for learning the Deep Learning Algorithm to derive a prediction model. The purpose of this study is to suggest a method of constructing a prediction model that predicts the compression strength with high accuracy based on Deep Learning Algorithms.

Abstract
 1. 서 론
 2. 연구계획 및 방법
 3. 해석결과 및 고찰
 4. 결 론
 참고문헌

• 이승준(한양대학교 건축시스템공학과) | Lee Seung Jum
• 이한승(한양대학교 ERICA캠퍼스 건축학부) | Lee Han Seung 교신저자