Intelligent Recognition of Rock Mass Discontinuities on the Basis of RGB-Enhanced Point Cloud Features

Cui, Honghai; Chen, Junqi; Wang, Xinyue; Zhao, Zihan; Han, Jiali; Sun, Qi; Zhang, Wen

doi:10.3390/app15126510

Open AccessArticle

Intelligent Recognition of Rock Mass Discontinuities on the Basis of RGB-Enhanced Point Cloud Features

by

Honghai Cui

¹,

Junqi Chen

^2,*,

Xinyue Wang

²,

Zihan Zhao

²,

Jiali Han

²,

Qi Sun

² and

Wen Zhang

²

¹

Jilin Provincial Transportation Planning and Design Institute, Changchun 130021, China

²

College of Construction Engineering, Jilin University, Changchun 130015, China

^*

Author to whom correspondence should be addressed.

Appl. Sci. 2025, 15(12), 6510; https://doi.org/10.3390/app15126510

Submission received: 21 April 2025 / Revised: 27 May 2025 / Accepted: 4 June 2025 / Published: 10 June 2025

(This article belongs to the Section Earth Sciences)

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Abstract

Rock slopes, composed of intact rock masses and relatively weak discontinuities, exhibit stability primarily governed by the spatial distribution of these discontinuities. Under the framework of structural control theory, acquiring discontinuity information is a fundamental prerequisite for rock slope stability analysis. However, advancements in measurement methods have significantly enhanced slope modeling precision while paradoxically reducing the efficiency of discontinuity data acquisition. To address this challenge, this study proposes a novel discontinuity identification method on the basis of high-precision UAV (unmanned aerial vehicle) point clouds, integrating principal component analysis (PCA), multi-channel gradient fusion, and cascaded edge detection techniques. Applying this approach, a high-resolution UAV-derived 3D model was constructed, and surface discontinuities were systematically identified for a slope case study in the North Qinling Belt, Shanxi Province, China. Results demonstrate that the proposed method achieves effective discontinuity identification performance, cumulatively detecting 1401 discontinuities. Statistical analysis of the identified discontinuities reveals three dominant orientation groups: I: S085° E/80°, II: S015° W/15°, and III: S005° W/85°.

Keywords: rock mass; RGB point cloud; photogrammetry; UAV-based structural analysis

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MDPI and ACS Style

Cui, H.; Chen, J.; Wang, X.; Zhao, Z.; Han, J.; Sun, Q.; Zhang, W. Intelligent Recognition of Rock Mass Discontinuities on the Basis of RGB-Enhanced Point Cloud Features. Appl. Sci. 2025, 15, 6510. https://doi.org/10.3390/app15126510

AMA Style

Cui H, Chen J, Wang X, Zhao Z, Han J, Sun Q, Zhang W. Intelligent Recognition of Rock Mass Discontinuities on the Basis of RGB-Enhanced Point Cloud Features. Applied Sciences. 2025; 15(12):6510. https://doi.org/10.3390/app15126510

Chicago/Turabian Style

Cui, Honghai, Junqi Chen, Xinyue Wang, Zihan Zhao, Jiali Han, Qi Sun, and Wen Zhang. 2025. "Intelligent Recognition of Rock Mass Discontinuities on the Basis of RGB-Enhanced Point Cloud Features" Applied Sciences 15, no. 12: 6510. https://doi.org/10.3390/app15126510

APA Style

Cui, H., Chen, J., Wang, X., Zhao, Z., Han, J., Sun, Q., & Zhang, W. (2025). Intelligent Recognition of Rock Mass Discontinuities on the Basis of RGB-Enhanced Point Cloud Features. Applied Sciences, 15(12), 6510. https://doi.org/10.3390/app15126510

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Intelligent Recognition of Rock Mass Discontinuities on the Basis of RGB-Enhanced Point Cloud Features

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