An efficient semi-automated characterization of rock mass discontinuities from 3D point clouds based on Nutcracker Optimization Algorithm-improved probabilistic neural network

IF 3.7 2区 工程技术 Q3 ENGINEERING, ENVIRONMENTAL
Shuyang Han, Dawei Tong, Binping Wu, Jiajun Wang, Xiaoling Wang, Wanyu Zhang
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引用次数: 0

Abstract

Discontinuities in rock masses significantly influence their mechanical properties and are critical for engineering applications, making it essential to thoroughly understand their geometric parameters. 3D point clouds serve as fundamental data for efficiently and accurately analyzing discontinuity orientations. In this context, a novel semi-automated method that employs a Nutcracker Optimization Algorithm-improved Probabilistic Neural Network (NOA-PNN) is proposed. The NOA enables the PNN to quickly identify the optimal smoothing factor, balancing both accuracy and efficiency. This method utilizes not only normal vectors, but also point coordinates, curvature, and density, incorporating a broader set of features to accurately identify points on discontinuities. The NOA-PNN model, trained on manually selected samples, swiftly identifies discontinuity sets while efficiently filtering out noise. Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is then used to extract single discontinuities within each set. Each discontinuity is fitted to a plane using a Principal Component Analysis (PCA)-based least squares method, facilitating the measurement of their spatial geometric parameters. Validation through two cases demonstrated that the proposed method achieved an average deviation of less than 5° in both dip direction and dip angle, exhibiting potential advantages in terms of accuracy and efficiency when compared to other important studies or software. This method significantly improves computational efficiency and achieves satisfactory results with only a small number of randomly selected samples. Its low requirements for sample quality and operator expertise make it highly operable and easily adaptable for practical engineering applications.

基于胡桃钳优化算法改进概率神经网络的三维点云岩体结构半自动高效表征
岩体中的不连续面显著影响其力学特性,对工程应用至关重要,因此彻底了解其几何参数至关重要。三维点云是有效、准确分析不连续面方向的基础数据。在此背景下,提出了一种采用胡桃夹子优化算法改进概率神经网络(NOA-PNN)的半自动化方法。NOA使PNN能够快速识别最优平滑因子,平衡精度和效率。该方法不仅利用法向量,还利用点坐标、曲率和密度,结合更广泛的特征集来准确地识别不连续面上的点。在人工选择的样本上训练的noaa - pnn模型可以快速识别不连续集,同时有效地滤除噪声。然后使用基于密度的带噪声应用空间聚类(DBSCAN)来提取每个集合中的单个不连续点。利用基于主成分分析(PCA)的最小二乘法将每个不连续面拟合到一个平面上,便于测量其空间几何参数。通过两个案例的验证表明,该方法在倾角方向和倾角上的平均偏差均小于5°,与其他重要研究或软件相比,在精度和效率方面具有潜在优势。该方法大大提高了计算效率,并且只需要少量随机选择的样本就能获得满意的结果。它对样品质量和操作人员专业知识的要求低,使其具有很高的可操作性,易于适应实际工程应用。
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来源期刊
Bulletin of Engineering Geology and the Environment
Bulletin of Engineering Geology and the Environment 工程技术-地球科学综合
CiteScore
7.10
自引率
11.90%
发文量
445
审稿时长
4.1 months
期刊介绍: Engineering geology is defined in the statutes of the IAEG as the science devoted to the investigation, study and solution of engineering and environmental problems which may arise as the result of the interaction between geology and the works or activities of man, as well as of the prediction of and development of measures for the prevention or remediation of geological hazards. Engineering geology embraces: • the applications/implications of the geomorphology, structural geology, and hydrogeological conditions of geological formations; • the characterisation of the mineralogical, physico-geomechanical, chemical and hydraulic properties of all earth materials involved in construction, resource recovery and environmental change; • the assessment of the mechanical and hydrological behaviour of soil and rock masses; • the prediction of changes to the above properties with time; • the determination of the parameters to be considered in the stability analysis of engineering works and earth masses.
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