铁路道砟形态特征的统计分析与几何重构

Statistical Analysis and Reconstruction of Morphological Characteristics of Railway Ballast

摘要:

道砟颗粒几何形态特征对其力学特性影响显著。为了量化研究铁路道砟形态特征，以真实的铁路一级碎石道砟颗粒为例，采用3D激光扫描获取道砟颗粒点云数据，引入道砟整体形态特征指标（长轴、中轴、短轴、球度指数），提出道砟局部形态特征指标（曲率指数），统计并建立上述整体和局部形态特征指标的概率密度分布函数。在此基础上，基于本征正交分解（POD）及径向基（RBF）神经网络提出了基于颗粒形态指标概率密度分布的道砟样本的重生成算法，重构道砟颗粒样本库。采用上述重生成算法分别重构了600及4 000个颗粒道砟，结果表明：重构道砟样本形态特征指标的统计分布结果均与真实扫描试样结果接近，证明该方法能够快速实现基于颗粒形态指标概率密度分布的任意数量道砟样本的建立。

Abstract:

The geometrical shape characteristics of the particles have a significant influence on the mechanical properties of the ballast. To quantitatively study the morphological characteristics of railway ballast， this paper took the first-order ballast particles of the real scan as an example， used 3D laser scanning to obtain the point cloud data of the ballast particles， and introduced the description indices of overall morphological characteristics （long axis， middle axis， short axis， sphericity index）， and proposed the local morphological characteristics index （curvature index） of the ballast granules. The probability density distributions of above-mentioned overall and local morphological characteristic indicators were established. On this basis， based on the intrinsic orthogonal decomposition （POD） and radial basis function （RBF） neural networks， a regenerative algorithm based on the probability density distribution of particle morphological indicators was proposed to reconstruct the ballast particle sample library. The above-mentioned re-generation algorithm reconstructed 600 and 4 000 particle turnouts， respectively. The results show that the statistical distribution of the morphological characteristics of the rebuilt ballast sample were close to those of the scanned samples， indicating that the method can quickly establish any number of turnout samples based on the probability density distribution of the particle morphological indicators.