为解决现有算法在处理不同特征点云时存在普适性不足、效率低下、难以应用于工程实际的问题，提出一种基于神经网络的岩体结构面智能识别方法，具体包括4个步骤。首先，对原始点云进行标准化预处理操作，并人工选取具有代表性的特征区域，以构建高质量的训练样本集； 其次，采用CFSFDP（clustering by fast search and find of density peaks）聚类算法为样本生成标签； 再次，构建并训练多层感知机（multilayer perceptron，MLP）模型和多层卷积神经网络（multi-layer convolutional neural network，MCNN）模型，输入全尺度点云的点法向量进行结构面粗识别，并对2种模型进行比选分析； 最后，使用HDBSCAN（hierarchical density-based spatial clustering of applications with noise）算法对分类结果进行细化与产状计算。结果表明： 1）采用多层感知机模型处理简单结构面时具有较高的处理速度，而卷积神经网络模型在处理复杂、非均匀点云时展现出更高的分类精度。2）与聚类方法相比，该方法计算时间提升25%～50%，能够有效解决传统算法无法适用于不同复杂点云的问题，且具有很强的鲁棒性。
Existing algorithms for recognizing rock discontinuities exhibit several limitations, including inadequate universality across diverse point cloud features, low computational efficiency, and limited applicability in engineering practice. To address these issues, an intelligent method based on neural networks is proposed. First, raw point cloud data are standardized through preprocessing, and representative feature regions are manually selected to construct a high-quality training dataset. Second, the clustering by fast search and find of density peaks (CFSFDP) algorithm is employed to generate sample labels. Third, a multilayer perceptron (MLP) model and a multilayer convolutional neural network (MCNN) model are constructed and trained. These models use point normals from the full-scale point cloud as input for preliminary discontinuity classification, and their performance is comparatively analyzed. Finally, the hierarchical density-based spatial clustering of applications with noise (HDBSCAN) algorithm is applied to refine the classification results and to calculate the spatial orientations of the discontinuities. Experimental and comparative results demonstrate that (1) the MLP model provides higher processing speed for simple discontinuity structures, whereas the MCNN model achieves higher classification accuracy for complex and nonuniform point clouds; and (2) compared with traditional clustering methods, the proposed approach reduces computational time by 25%－50% while effectively overcoming the limitations of conventional algorithms in heterogeneous geological environments, demonstrating strong robustness.
