GEOMETRIC FEATURE–BASED IDENTIFICATION OF ROCK MASS DISCONTINUITIES USING ADAPTIVE HIERARCHICAL CLUSTERING

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Discontinuities such as joints, fractures, and bedding planes play a decisive role in controlling the stability and safety of underground engineering structures. Accurate identification and grouping of these discontinuities are essential for tunnel excavation, slope stabilization, and foundation design. Conventional identification methods primarily depend on normal vector estimation combined with directional clustering algorithms. However, these approaches often suffer from reduced accuracy under uneven orientation density, omission of critical discontinuities, and the need for manual parameter tuning. To address these limitations, this study proposes a novel discontinuity identification framework based on geometric feature analysis and adaptive statistical clustering.

Limitations of Conventional Directional Clustering Methods

Traditional approaches rely heavily on estimating surface normals from point cloud data and grouping them via clustering techniques such as k-means or density-based methods. These strategies encounter three key challenges:
(1) performance degradation when discontinuity orientations are unevenly distributed,
(2) loss of small or sparse structural sets, and
(3) dependence on manually predefined cluster numbers or thresholds.

Such limitations reduce reliability in complex geological environments, where discontinuities vary significantly in scale, persistence, and spatial arrangement.

Geometric Feature–Driven Discontinuity Detection

The proposed method shifts focus from purely directional clustering to geometric feature analysis of rock mass point clouds. By examining spatial distribution variability and structural continuity, the framework captures both local and global geometric characteristics. An adaptive region-growing algorithm is integrated to detect independent discontinuities even under irregular rock mass geometries. This enables accurate segmentation across diverse rock shapes and sizes while minimizing sensitivity to noise.

Adaptive Hierarchical Clustering Based on Fisher Distribution

Recognizing that rock mass orientations typically follow a Fisher distribution, the study introduces a statistically grounded adaptive hierarchical clustering algorithm. Unlike conventional methods requiring preset cluster numbers, this approach automatically determines the optimal number of structural sets through statistical analysis of orientation dispersion. By eliminating manual intervention, the method enhances automation, objectivity, and reproducibility in discontinuity grouping.

Noise Resistance and Multi-Scale Feature Integration

A key strength of the framework lies in its integration of local geometric attributes (e.g., curvature, point density variations) with global structural trends. This multi-scale feature fusion reduces interference from measurement noise and incomplete data. The method demonstrates robustness in handling complex geological conditions, including intersecting discontinuities and variable persistence lengths, ensuring reliable detection across heterogeneous rock masses.

Experimental Validation and Engineering Significance

The proposed approach was validated using three real-world rock mass models and benchmarked against three mainstream directional clustering algorithms. Results indicate superior accuracy, improved detection of optimal discontinuity sets, and enhanced robustness under uneven orientation distributions. By offering a reliable and efficient tool for automated discontinuity detection and grouping, this method significantly strengthens geotechnical analysis, supporting safer underground excavation design and construction decision-making.

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#EngineeringGeology
#3DModeling
#CivilEngineeringResearch
#GeometricAnalysis
#ConstructionSafety
#DigitalRockMass
#SmartGeotechnics
#InfrastructureDesign

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