基于机器学习算法的空心圆柱砂岩离散元细观参数标定方法研究
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1.苏州科技大学土木工程学院;2.苏州科技大学土木工程学院 中国科学院武汉岩土力学研究所,岩土力学与工程国家重点实验室

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TV61

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国家自然科学基金项目(41907232)


Research on the Calibration Method for Mesoscopic Parameters of Hollow Cylindrical Sandstone Discrete Element Based on Machine Learning Algorithm
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1.Suzhou University of Science and Technology, School of Civil Engineering;2.State Key Laboratory of Geomechanics and Geotechnical Engineering, Institute of Rock and Soil Mechanics, Chinese Academy of Sciences

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    摘要:

    工程岩体存在原生裂纹和新生裂纹最终形成宏观裂缝,空心圆柱离散元模拟试验可以实现复杂应力路径的模拟。对于空心圆柱离散元模拟试验中存在的问题,如细观参数标定的影响因素众多和耗时过长等,本文提出了一种基于机器学习算法的空心圆柱砂岩离散元细观参数标定方法。通过改变离散元模型中不同输入变量得到210组模拟数据,建立基于随机森林算法和XGBoost算法的细观参数标定模型,对比了模型预测精度,分析了参数敏感性,量化了输入参数对岩石整体力学特性的贡献,据此给出了微观参数修正的建议取值。结合空心圆柱室内三轴试验,标定结果表明:XGBoost算法具有计算速度优势,可快速定位离散元细观参数范围,为空心圆柱的离散元细观参数标定提供了新思路,具有工程应用价值。

    Abstract:

    There are primary cracks and new cracks in the engineering rock mass, and macroscopic cracks are formed, and the hollow cylindrical discrete element simulation test can realize the simulation of complex stress paths. In view of the problems existing in the hollow cylinder discrete element simulation experiment, such as the many influencing factors and the long time required for meso-parameter calibration, this paper proposes a meso-parameter calibration method for hollow cylindrical sandstone based on machine learning algorithm. By changing the different input variables in the discrete element model, 210 sets of simulation data were obtained, and a mesoscopic parameter calibration model based on random forest algorithm and XGBoost algorithm was established, the prediction accuracy of the model was compared, the sensitivity of the parameters was analyzed, and the contribution of the input parameters to the overall mechanical properties of the rock was quantified. Combined with the indoor triaxial test of the hollow cylinder, the calibration results show that the XGBoost algorithm has the advantage of calculation speed, which 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 engineering application value.

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潘海潮,吴静红,姜玥,等. 基于机器学习算法的空心圆柱砂岩离散元细观参数标定方法研究[J]. 科学技术与工程, , ():

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  • 收稿日期:2024-01-17
  • 最后修改日期:2024-05-17
  • 录用日期:2024-05-21
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