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Calibration Method for Mesoscopic Parameters of Hollow Cylindrical Sandstone Discrete Element Based on Machine Learning Algorithm
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Hai-chao PAN1, Jing-hong WU1, *, Yue JIANG1, 2, Wen-dong ZOU1
Science Technology and Engineering | 2025, 25(1) : 339 - 345
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Science Technology and Engineering | 2025, 25(1): 339-345
• Papers·Hydraulic Engineering •
Calibration Method for Mesoscopic Parameters of Hollow Cylindrical Sandstone Discrete Element Based on Machine Learning Algorithm
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Hai-chao PAN1, Jing-hong WU1, *, Yue JIANG1, 2, Wen-dong ZOU1
Affiliations
  • 1. School of Civil Engineering, Suzhou University of Science and Technology, Suzhou 215011, China
  • 2. State Key Laboratory of Geomechanics and Geotechnical Engineering, Institute of Rock and Soil Mechanics,Chinese Academy of Sciences, Wuhan 430071, China
Published: 2025-01-08 doi: 10.12404/j.issn.1671-1815.2400502
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Primary cracks and new cracks develop within the engineering rock mass, leading to the formation of macroscopic cracks. The hollow cylindrical discrete element simulation test enables the emulation of complex stress paths. In order to solve the problems existing in the simulation test of hollow cylindrical discrete element, such as numerous influencing factors and lengthy meso-parameter calibration, a method of mesoscale parameter calibration of hollow cylindrical sandstone discrete element based on machine learning algorithm was proposed. Through variations in input variables within the discrete element model, 210 sets of simulation data were obtained. A mesoscopic parameter calibration model based on random forest algorithm and extreme gradient boosting(XGBoost) algorithm was established, the prediction accuracy of the model was compared, the parameter sensitivity was analyzed, and the contribution of input parameters to the overall mechanical properties of the rock was quantified. Combined with the indoor triaxial test of hollow cylinder, the calibration results show that the XGBoost algorithm has the advantages of computing speed, and can quickly locate the range of discrete element mesoscopic parameters, which provides a new idea for the calibration of discrete element mesoscopic parameters of hollow cylinder, and has the value of engineering application.

machine learning  /  discrete elements  /  XGBoost  /  mesotropic parameters  /  hollow cylindrical sandstone
Hai-chao PAN, Jing-hong WU, Yue JIANG, Wen-dong ZOU. Calibration Method for Mesoscopic Parameters of Hollow Cylindrical Sandstone Discrete Element Based on Machine Learning Algorithm[J]. Science Technology and Engineering, 2025 , 25 (1) : 339 -345 . DOI: 10.12404/j.issn.1671-1815.2400502
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Article Info
doi: 10.12404/j.issn.1671-1815.2400502
  • Receive Date：2024-01-17
  • Online Date：2025-07-29
  • Published：2025-01-08
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  • Received：2024-01-17
  • Revised：2024-09-28
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Affiliations
    1. School of Civil Engineering, Suzhou University of Science and Technology, Suzhou 215011, China
    2. State Key Laboratory of Geomechanics and Geotechnical Engineering, Institute of Rock and Soil Mechanics,Chinese Academy of Sciences, Wuhan 430071, China
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表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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