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| Research on calibration method of discrete element mesoscopic parameters based on neural network landslide in Heifangtai，Gansu as an example |
| ZHOU Xiaopeng1，2，XU Qiang1，ZHAO Kuanyao1，CHEN Wanlin1，JU Yuanzhen1，ZHOU Qi1 |
| (1. State Key Laboratory of Geohazards Prevention and Geoenviroment Protection，Chengdu University of Technology，Chengdu，Sichuan 610059，China；2. Guangxi Transportation Research and Consulting Co.，Ltd. Institute，Nanning，Guangxi 530007，China) |
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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.
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2020, Vol. 39 
