Hidden Layer의 개수가 Deep Learning Algorithm을 이용한 콘크리트 압축강도 추정 모델의 성능에 미치는 영향에 관한 기초적 연구

A Basic Study on the Effect of Number of Hidden Layers on Performance of Estimation Model of Compressive Strength of Concrete Using Deep Learning Algorithms

The compressive strength of concrete is determined by various influencing factors. However, the conventional method for estimating the compressive strength of concrete has been suggested by considering only 1 to 3 specific influential factors as variables. 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 learned using the Deep Learning Algorithm to derive an estimated function model. The purpose of this study is to investigate the effect of the number of hidden layers on the prediction performance in the process of estimating the compressive strength for an arbitrary combination.