期刊名称：IOP Conference Series: Earth and Environmental Science
印刷版ISSN：1755-1307
电子版ISSN：1755-1315
出版年度：2019
卷号：304
期号：3
页码：1-8
DOI：10.1088/1755-1315/304/3/032062
出版社：IOP Publishing
摘要：A macroscopic parameter calibration method for the particle flow code (PFC) using a uniform experimental design and deep learning network is proposed to explore the relationship between the micro parameters and macro response of rocks. First, a sensitivity analysis was used to identify which micro parameters influence the macro factors. A uniform experimental design was applied to achieve this goal in this study. Then, a neural network based on batch normalization was constructed. Through a large number of numerical simulation tests, the relationship between the input (micro parameters) and output (macro response) can be obtained by deep neural network (DNN). Finally, the DNN model was used to perform validation by using marble in Jinping. The average error for the best results was only 4.211%. The main advantage of this method is that it can achieve fast parameter calibration compared with traditional calibration methods, and the calibration results can be obtained in only a few seconds through the designed program.
