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Computers and Concrete
  Volume 25, Number 5, May 2020, pages 433-445

Estimating the workability of self-compacting concrete in different mixing conditions based on deep learning
Liu Yang and Xuehui An

    A method is proposed in this paper to estimate the workability of self-compacting concrete (SCC) in different mixing conditions with different mixers and mixing volumes by recording the mixing process based on deep learning (DL). The SCC mixing videos were transformed into a series of image sequences to fit the DL model to predict the SF and VF values of SCC, with four groups in total and approximately thirty thousand image sequence samples. The workability of three groups SCC whose mixing conditions were learned by the DL model, was estimated. One additionally collected group of the SCC whose mixing condition was not learned, was also predicted. The results indicate that whether the SCC mixing condition is included in the training set and learned by the model, the trained model can estimate SCC with different workability effectively at the same time. Our goal to estimate SCC workability in different mixing conditions is achieved.
Key Words
    deep learning; self-compacting concrete; workability; mixing condition; mixer; mixing volume; slump flow and V-funnel test; convolutional neural network; recurrent neural network
Liu Yang and Xuehui An: State Key Laboratory of Hydro Science and Engineering, Tsinghua University, 100084-Beijing, China

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