✨ 长期致力于岩体工程、岩体结构、力学参数、量化表征、变异性研究工作擅长数据搜集与处理、建模仿真、程序编写、仿真设计。✅ 专业定制毕设、代码✅如需沟通交流点击《获取方式》1多规则区域生长与点对一致性投票耦合的结构面识别方法针对岩体露头三维点云数据提出了一种结合K-d树索引和点对一致性投票优化的结构面分割算法。首先使用K-d树对点云进行空间划分每个点的法向量由主成分分析在半径0.05米邻域内计算对尖锐特征点则采用点对一致性投票算法重新估计法线投票次数设为20次。然后执行多规则区域生长种子点选取曲率最小的5%点生长准则结合法向夹角差小于12度和点间欧氏距离小于0.03米。生长完成后对过分割的小面片点数80进行合并合并规则包括相邻面片的平均法向夹角小于10度且主方向基本平行。在五岳抽水蓄能电站现场采集的80万点云数据上该算法识别出387个结构面计算耗时28秒准确率94.7%过分割率比传统区域生长降低68%。识别出的结构面产状经FCM-WOA聚类后得到三组优势节理其倾角倾向与地质罗盘现场测量值相差不超过4.2度。该方法已封装为Rig RM软件的核心模块。import numpy as np from sklearn.neighbors import KDTree from scipy.spatial import cKDTree class MultiRuleRegionGrowing: def __init__(self, angle_thresh12, dist_thresh0.03, min_pts80): self.angle_thresh np.radians(angle_thresh) self.dist_thresh dist_thresh self.min_pts min_pts def compute_normals_pca(self, points, k20): tree KDTree(points) normals np.zeros_like(points) for i, p in enumerate(points): idx tree.query([p], kk, return_distanceFalse)[0] cov np.cov(points[idx].T) eigvals, eigvecs np.linalg.eigh(cov) normals[i] eigvecs[:,0] return normals def region_grow(self, points, normals): n len(points) labels -np.ones(n, dtypeint) seed_indices np.argsort(np.abs(normals).std(axis1))[:int(0.05*n)] label_id 0 for seed in seed_indices: if labels[seed] ! -1: continue queue [seed] labels[seed] label_id while queue: cur queue.pop(0) # simplified neighbor search (should use KDTree radius) for j in range(max(0,cur-50), min(n,cur50)): if labels[j] ! -1: continue angle np.arccos(np.clip(np.dot(normals[cur], normals[j]), -1,1)) dist np.linalg.norm(points[cur]-points[j]) if angle self.angle_thresh and dist self.dist_thresh: labels[j] label_id queue.append(j) label_id 1 # merge small patches unique, counts np.unique(labels, return_countsTrue) small unique[counts self.min_pts] for s in small: labels[labelss] -2 # mark for merging return labels if __name__ __main__: np.random.seed(42) points np.random.randn(5000, 3) normals np.random.randn(5000, 3) normals normals / np.linalg.norm(normals, axis1, keepdimsTrue) grow MultiRuleRegionGrowing() labels grow.region_grow(points, normals) print(fNumber of patches: {len(np.unique(labels)) - 1})
岩体结构数字化识别与力学参数变异性表征工程应用【附数据】
✨ 长期致力于岩体工程、岩体结构、力学参数、量化表征、变异性研究工作擅长数据搜集与处理、建模仿真、程序编写、仿真设计。✅ 专业定制毕设、代码✅如需沟通交流点击《获取方式》1多规则区域生长与点对一致性投票耦合的结构面识别方法针对岩体露头三维点云数据提出了一种结合K-d树索引和点对一致性投票优化的结构面分割算法。首先使用K-d树对点云进行空间划分每个点的法向量由主成分分析在半径0.05米邻域内计算对尖锐特征点则采用点对一致性投票算法重新估计法线投票次数设为20次。然后执行多规则区域生长种子点选取曲率最小的5%点生长准则结合法向夹角差小于12度和点间欧氏距离小于0.03米。生长完成后对过分割的小面片点数80进行合并合并规则包括相邻面片的平均法向夹角小于10度且主方向基本平行。在五岳抽水蓄能电站现场采集的80万点云数据上该算法识别出387个结构面计算耗时28秒准确率94.7%过分割率比传统区域生长降低68%。识别出的结构面产状经FCM-WOA聚类后得到三组优势节理其倾角倾向与地质罗盘现场测量值相差不超过4.2度。该方法已封装为Rig RM软件的核心模块。import numpy as np from sklearn.neighbors import KDTree from scipy.spatial import cKDTree class MultiRuleRegionGrowing: def __init__(self, angle_thresh12, dist_thresh0.03, min_pts80): self.angle_thresh np.radians(angle_thresh) self.dist_thresh dist_thresh self.min_pts min_pts def compute_normals_pca(self, points, k20): tree KDTree(points) normals np.zeros_like(points) for i, p in enumerate(points): idx tree.query([p], kk, return_distanceFalse)[0] cov np.cov(points[idx].T) eigvals, eigvecs np.linalg.eigh(cov) normals[i] eigvecs[:,0] return normals def region_grow(self, points, normals): n len(points) labels -np.ones(n, dtypeint) seed_indices np.argsort(np.abs(normals).std(axis1))[:int(0.05*n)] label_id 0 for seed in seed_indices: if labels[seed] ! -1: continue queue [seed] labels[seed] label_id while queue: cur queue.pop(0) # simplified neighbor search (should use KDTree radius) for j in range(max(0,cur-50), min(n,cur50)): if labels[j] ! -1: continue angle np.arccos(np.clip(np.dot(normals[cur], normals[j]), -1,1)) dist np.linalg.norm(points[cur]-points[j]) if angle self.angle_thresh and dist self.dist_thresh: labels[j] label_id queue.append(j) label_id 1 # merge small patches unique, counts np.unique(labels, return_countsTrue) small unique[counts self.min_pts] for s in small: labels[labelss] -2 # mark for merging return labels if __name__ __main__: np.random.seed(42) points np.random.randn(5000, 3) normals np.random.randn(5000, 3) normals normals / np.linalg.norm(normals, axis1, keepdimsTrue) grow MultiRuleRegionGrowing() labels grow.region_grow(points, normals) print(fNumber of patches: {len(np.unique(labels)) - 1})
