양방향 DNN 해석을 이용한 삼성분계 콘크리트의 배합 산정에 관한 연구

초록

The concrete mix design and compressive strength evaluation are used as basic data for the durabilityof sustainable structures. However, the recent diversification of mixing factors has created difficultiesin calculating the correct mixing factor or setting the reference value concrete mixing design. Thepurpose of this study is to design a predictive model of bidirectional analysis that calculates the mixingelements of ternary concrete using deep learning, one of the artificial intelligence techniques. For theDNN-based predictive model for calculating the concrete mixing factor, performance evaluation andcomparison were performed using a total of 8 models with the number of layers and the number ofhidden neurons as variables. The combination calculation result was output. As a result of the model’sperformance evaluation, an average error rate of about 1.423% for the concrete compressive strengthfactor was achieved. and an average MAPE error of 8.22% for the prediction of the ternary concretemixing factor was satisfied. Through comparing the performance evaluation for each structure of theDNN model, the DNN5L-2048 model showed the highest performance for all compounding factors. Using the learned DNN model, the prediction of the ternary concrete formulation table with therequired compressive strength of 30 and 50 MPa was carried out. The verification process through theexpansion of the data set for learning and a comparison between the actual concrete mix table and theDNN model output concrete mix table is necessary.

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