A Study on the Mix Design Model of 40MPa Class High Strength Mortar with Rice Husk Powder Using Neural Network Theory

신경망 이론을 적용한 40MPa급 증해추출 왕겨분말을 혼입한 고강도 무시멘트 모르타르 배합설계모델에 관한 연구

The purpose of this study is to propose a 40MPa mortar mixed design model that applies the neural network theory to minimize wasted effort in trial and error. A mixed design model was applied to each of the 60 data using fly ash, blast furnace slag fine powder and thickened rice husk powder. And in the neural network model, the optimized connection weight was obtained by repeatedly applying it to the MATLAB. The completed mixed design model was demonstrated by analyzing and comparing the predicted values of the mixed design model with those measured in the actual compressive strength test. As a result of the mixed design verification experiment, the error rates of the double mixed non-cement mortar using blast furnace slag fine powder and rice husk powder at a height of 40MPa were 3.24% and 3.4%. Mixed with fly ash and rice husk powder had an error rate of 3.94% and 5.8%. The error rate of the triple mixed non-cement mortar of the rice husk powder, fly ash, and blast furnace slag fine powder was 2.5% and 5.1%.