基于神经网络的离散元细观参数标定方法研究——以甘肃黑方台黄土滑坡为例

doi:10.13722/j.cnki.jrme.2019.0531

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摘要离散元细观力学参数与宏观力学参数之间存在着很高的非线性特征，目前人工试算的参数标定方法存在着很大的随机性和盲目性，标定过程效率低，标定结果可重复性差。因此为了找到一种新的准确、高效的参数标定方法，以黑方台2015～2017年发生的7起典型黄土滑坡为研究对象，采用离散元数值试验及神经网络等方法，首先根据前人力学试验及相关研究确定出6个本征参数的取值，利用室内休止角试验以及GEMM材料数据库，确定出剩余的6个待标定参数的取值范围；然后通过对剩余6个待标定参数的敏感性进行分析，确定出JKR模型能量密度的取值为100 J/m2，并设计出其中6起滑坡以剩余5个待标定参数为变量的系列数值模拟试验，得到各组参数及其对应的模拟形态(滑源区横向宽、滑源区纵向宽、滑坡的壁高、滑坡整体落差、滑距、堆积区横向宽)；然后以此作为样本进行神经网络训练，确定出模拟形态与参数之间的映射关系；进而利用此映射关系反演标定出这6起滑坡的最优模拟参数，并以这6组最优模拟参数作为该地区黄土滑坡数值模拟参数的取值范围；最后通过对第7个滑坡的正演结果，验证了该参数取值范围的可靠性和准确性，避免相同地质条件下人工标定参数所带来的复杂性和随机性。

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	周小棚1
	2
	许强1
	赵宽耀1
	陈婉琳1
	巨袁臻1
	周琪1

关键词 ：边坡工程, 离散元, 细观参, 神经网络, 参数反演

Abstract：There are high nonlinear characteristics between the micro-mechanical parameters and the macro- mechanical parameters of the discrete element. At present，the manual calibration of the parameter calibration method has great randomness and blindness，the calibration process is inefficient，and the calibration result is reproducible. Therefore，it is urgent to find a new accurate and efficient parameter calibration method. This paper takes 7 typical loess landslides occurred in Heifangtai from 2015 to 2017 as the research object. Firstly，according to the previous mechanical test and related literature，the values of six eigen parameters are determined. And then，the range of values of the remaining six parameters is calibrated by the indoor angle of repose test and the GEMM material database. Through the remaining six parameters sensitivity analysis，the energy density of the JKR model is determined to be 100 J/m2，and the series of orthogonal tests with 6 remaining landslides as variables are designed. Obtaining the parameters of each group and their corresponding simulation forms(horizontal width of the sliding source area，longitudinal width of the sliding source area，wall height of the landslide，overall landslide drop，slip distance，lateral width of the accumulation area). Then use this as a sample to carry out neural network training，and determine the mapping relationship between the simulated shape and the parameters. The optimal simulation parameters of the six landslides is inverted and calibrated by this mapping relationship，and use it as the value range of numerical simulation parameters of loess landslide in this area. Finally，the reliability and accuracy of the parameter range are verified by the forward performance of the 7th landslide，proving this method can avoiding the complexity and randomness of artificial calibration parameters under the same geological conditions.

Key words： slope engineering discrete element mesoscopic parameters neural networks parameter inversion

引用本文:

周小棚1，2，许强1，赵宽耀1，陈婉琳1，巨袁臻1，周琪1. 基于神经网络的离散元细观参数标定方法研究——以甘肃黑方台黄土滑坡为例[J]. 岩石力学与工程学报, 2020, 39(S1): 2837-2847.
ZHOU Xiaopeng1，2，XU Qiang1，ZHAO Kuanyao1，CHEN Wanlin1，JU Yuanzhen1，ZHOU Qi1. Research on calibration method of discrete element mesoscopic parameters based on neural network landslide in Heifangtai，Gansu as an example. , 2020, 39(S1): 2837-2847.

链接本文:

http://rockmech.whrsm.ac.cn/CN/10.13722/j.cnki.jrme.2019.0531 或 http://rockmech.whrsm.ac.cn/CN/Y2020/V39/IS1/2837

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