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http://dx.doi.org/10.5345/JKIBC.2022.22.6.619

A Study on the Calculation of Ternary Concrete Mixing using Bidirectional DNN Analysis  

Choi, Ju-Hee (Department of Smart-City Engineering, Hanyang University)
Ko, Min-Sam (Department of ICT, Hanyang University)
Lee, Han-Seung (Department of Architectural Engineering, Hanyang University)
Publication Information
Journal of the Korea Institute of Building Construction / v.22, no.6, 2022 , pp. 619-630 More about this Journal
Abstract
The concrete mix design and compressive strength evaluation are used as basic data for the durability of sustainable structures. However, the recent diversification of mixing factors has created difficulties in calculating the correct mixing factor or setting the reference value concrete mixing design. The purpose of this study is to design a predictive model of bidirectional analysis that calculates the mixing elements of ternary concrete using deep learning, one of the artificial intelligence techniques. For the DNN-based predictive model for calculating the concrete mixing factor, performance evaluation and comparison were performed using a total of 8 models with the number of layers and the number of hidden neurons as variables. The combination calculation result was output. As a result of the model's performance evaluation, an average error rate of about 1.423% for the concrete compressive strength factor was achieved. and an average MAPE error of 8.22% for the prediction of the ternary concrete mixing factor was satisfied. Through comparing the performance evaluation for each structure of the DNN 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 the required compressive strength of 30 and 50 MPa was carried out. The verification process through the expansion of the data set for learning and a comparison between the actual concrete mix table and the DNN model output concrete mix table is necessary.
Keywords
deep-learning; concrete mixing proportions; concrete mix design; strength prediction;
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Times Cited By KSCI : 1  (Citation Analysis)
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