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- W3199519072 abstract "Different from conventional methodology, this study presents an intelligent approach to fast identify the grain-size distribution (GSD) of granular soils using a convolutional neural network (CNN) under a deep learning framework. A database including 279 images of granular soils with their GSDs is first created. Then, the framework of the CNN is tailored to identify GSD. The CNN-based model is trained to predict 11 grain sizes corresponding to 1%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, and 100% of granular soils passing (i.e., d1, d10, …, d100) using 80% of images, followed by the model testing using the rest 20% of images. By feeding an image of a soil sample into the proposed CNN-based model, the GSD can be predicted within several seconds. The predicted GSD exhibits excellent agreement with the measured one with an average error of 2.29% on the testing sets. It can be concluded that the proposed CNN-based model successfully provides a new intelligent way to fast, accurately, and conveniently identify the GSD of granular soils through images of soils." @default.
- W3199519072 created "2021-09-27" @default.
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- W3199519072 date "2021-12-01" @default.
- W3199519072 modified "2023-10-08" @default.
- W3199519072 title "CNN-Based Intelligent Method for Identifying GSD of Granular Soils" @default.
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- W3199519072 doi "https://doi.org/10.1061/(asce)gm.1943-5622.0002214" @default.
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