Prediction of Discrete Element Breakage Parameter for Ballast Particles Based on Genetic Algorithm–Back Propagation Neural Network Model
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摘要:
为优化有砟道床的劣化评估与养护维修，针对道砟颗粒破碎过程及破碎机理的研究具有重要价值. 通过对单个道砟颗粒进行单轴压碎实验，确定破坏所需的等效应力，依据道砟颗粒的破碎过程和加载力对其受载变形行为进行分析；通过激光光栅扫描道砟颗粒的几何外形，使用最小外接矩形法对其进行规定，同时，采用刚性块进行道砟颗粒填充，并与传统球颗粒填充方式作对比，分析了使用刚性块所构造道砟颗粒的破碎过程以及道砟颗粒内部微裂纹萌生情况；此外，研究不同几何外形道砟颗粒的离散元接触参数，采用遗传算法优化的神经网络模型（GA-BP）预测不同等效粒径道砟颗粒对应的黏结强度. 研究结果表明：在离散元中，道砟颗粒的黏结强度随着等效粒径的增加而增加， 当等效粒径为[25，39)、[39，48)、 [48，56)、[56，64)、[64，80) mm时，对应的平均黏结强度分别为151.85、159.45、166.71、175.29、185.29 MPa.
Abstract:To optimize the deterioration assessment and maintenance of ballasted tracks, it is of great value to study the breakage process and mechanism of ballast particles. Through a uniaxial breakage test on the single ballast particle, the equivalent stress required for its failure was determined. The deformation behavior under load was analyzed based on the ballast particle breakage process and loading force. Laser grating scanning of the ballast particle geometry was performed, and a minimum bounding rectangle method was used for specification. Rigid blocks were used for ballast particle packing, and a comparison was made with the traditional spherical particle packing method. The breakage process of ballast particles constructed with rigid blocks and the initiation of microcracks within the ballast particles were analyzed. In addition, the discrete element contact parameters for ballast particles with different geometries were studied, and a neural network model optimized by a genetic algorithm, namely GA-BP was used to predict the bond strength for ballast particles with different equivalent particle sizes. The results show that in the discrete element model, the bond strength of the ballast particles increases with the increase in its equivalent particle sizes. Specifically, for equivalent particle sizes in the ranges of [25, 39), [39, 48), [48, 56), [56, 64), and [64, 80) mm, the corresponding average bond strengths are 151.85, 159.45, 166.71, 175.29, and 185.29 MPa, respectively.
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Key words:
- discrete element method /
- rigid block /
- neural network /
- bond strength /
- ballast particle breakage
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图 1 道砟颗粒最小外接矩形尺寸
Figure 1. Definition of minimum bounding rectangle dimensions for ballast particles
图 4 等效粒径与等效应力关系
Figure 4. Relationship between equivalent stress and equivalent particle size
图 5 不同伸长率及扁平率对应的峰值载荷均值
Figure 5. Average peak load corresponding to different elongation and flatness ratios
图 6 激光光栅扫描系统和道砟颗粒构造
Figure 6. Laser grating scanning system and ballast particle structure
图 7 道砟颗粒建模方式以及单轴加载仿真构建
Figure 7. Ballast particle modeling method and uniaxial loading simulation
图 9 不同构造方式的道砟颗粒单轴压缩加载力曲线
Figure 9. Uniaxial compression loading force curves of ballast particles in different construction modes
图 10 道砟颗粒受压过程中的破碎形式
Figure 10. Breakage patterns of ballast particles during compression process
图 11 道砟颗粒破碎中所受加载力和微裂纹数量随轴向应变变化曲线
Figure 11. Variation of loading force and number of microcracks with axial strain during ballast particle breakage
图 12 隐含层节点数对应训练集MSE变化
Figure 12. MSE change with number of hidden layer nodes in training set
图 13 BP神经网络和GA-BP神经网络模型预测值与真实值的对比
Figure 13. Comparison of predicted values and actual values of BP and GA-BP neural network models
图 14 GA-BP与BP神经网络的预测误差对比
Figure 14. Comparison of prediction errors between GA-BP and BP neural networks
图 15 5组不同等效粒径的道砟颗粒
Figure 15. Five groups of ballast particles with different equivalent particle sizes
图 16 100个道砟颗粒的GA-BP神经网络模型黏结强度预测结果
Figure 16. GA-BP neural network model prediction results for bond strength of 100 ballast particles
图 17 单颗粒破碎仿真与实际实验加载力对比
Figure 17. Comparison of single particle breakage simulation and actual experimental loading force
表 1 道砟颗粒棱角特性仿真接触参数
Table 1. Simulation contact parameters for angular characteristics of ballast particle
微观参数 数值 杨氏模量/GPa 70 剪切刚度比 0.18 软化系数 1.0 软化阈值 0.8 摩擦系数 0.1 
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表 2 依据等效粒径分组的道砟颗粒黏结强度
Table 2. Bond strength of ballast particles grouped according to equivalent particle size
等效粒径
范围/mm黏结强度
范围/MPa黏结强度
均值/MPa[25，39） 130.37～155.34 151.85 [39，48） 132.22～186.58 159.45 [48，56） 138.58～195.69 166.71 [56，64） 164.99～202.46 175.29 [64，80） 177.84～201.76 185.29
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