Improved CNN-based model for shape parameter determination of sand particles considering imbalanced dataset

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This study presents an artificial intelligence prediction model for determining the roundness and sphericity of sand particles using a convolutional neural network (CNN) regression model, which combines a CNN and a regression model. The dataset used for the model development consisted of 27,000 binary images of coarse grains with various shapes. To develop the prediction model, the validated algorithms, ResNet101 and ResNet152, were utilized and modified into a CNN regression model to predict the shape parameters. The performance of the developed model was evaluated using 3,000 randomly generated binary images, and the mean squared error and root mean squared error were calculated as evaluation metrics. The evaluation results indicated that the model effectively predicted the sphericity and roundness of sand particles, with a relatively low prediction performance observed for roundness.