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- W3197101373 abstract "A method is proposed in this paper to automatically estimate the workability of six different strength grades concrete by recording the mixing process based on deep learning. The concrete mixing videos were collected in a specially designed set up fixed on a mixer located in the mixing station. These videos were transformed into a series of image sequences to fit the deep learning model to predict the slump and slump flow values of concrete, with six groups in total and more than twenty thousand image sequence samples. The workability of six groups concrete with different strength grades learned by the DL model, was estimated. The results indicate that the trained deep learning model with CNN and LSTM can estimate concrete workability effectively. Our goal to estimate concrete workability in different strength grades is achieved." @default.
- W3197101373 created "2021-09-13" @default.
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- W3197101373 date "2021-12-01" @default.
- W3197101373 modified "2023-10-14" @default.
- W3197101373 title "Estimating workability of concrete with different strength grades based on deep learning" @default.
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- W3197101373 doi "https://doi.org/10.1016/j.measurement.2021.110073" @default.
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