AOC: An adaptive oriented contraction method for automatic trace recognition of rock tunnel excavation face based on 3D point cloud

Abstract

Trace recognition is essential for rock discontinuity characterization of tunnel excavation faces. Traditional methods of trace identification based on three-dimensional (3D) point cloud curvatures require manual fine-tuning for curvature detection and lack consistency with orientation grouping results. This paper proposes a new automatic method for trace identification from 3D point cloud. An adaptive vector method based on neighbor assignment is proposed to accurately generate both normal vectors and directional vectors on sharp points. A principal component analysis-based oriented contraction (PWI-OC) method is presented to extract point cloud skeletons with good iterative conformality. A sparse growing method is proposed to generate extensive trace segments. Two rock excavation face cases, from a mining tunnel and a railway tunnel, are adopted for analysis. The significance of adaptive normal vectors is validated for improving the quality of orientation grouping, and the iterative conformality of PWI-OC is validated to generate more accurate and robust trace skeletons than the traditional method. The results show that the proposed method can achieve a more accurate trace identification than traditional methods, consistent with orientation grouping results, robust to overlapping traces, and automates curvature point detection.

Keywords

Rock tunnel / Excavation face / Discontinuity trace / 3D point cloud / Adaptive vector / Oriented contraction

Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

CRediT authorship contribution statement

Keshen Zhang: Visualization, Software, Conceptualization, Writing - original draft, Validation, Methodology. Min Zhang: Funding acquisition, Supervision, Investigation. Lianyang Zhang: Writing - review & editing, Supervision, Resources. Wei Wu: Supervision, Resources, Writing - review & editing.

Declaration of competing interest

Dr. Lianyang Zhang is a managing editor for Underground Space and was not involved in the editorial review or the decision to publish this article. All authors declare that there are no competing interests.

Acknowledgement

This work was supported by the National Natural Science Foundation of China (Grant Nos. 52508444, 42272338, 41827807, and 41902275); Shandong Provincial Natural Science Foundation (Grant No. ZR2025QC1138); Shanghai Sailing Program (Grant No. 18YF1424400); Joint Fund for Basic Research of High-speed Railway of National Natural Science Foundation of China; China Railway Corporation (Grant No. U1934212); China State Railway Group Co., Ltd. (Grant No. P2019G038); Department of Transportation of Zhejiang Province (Project No. 202213); China Railway First Survey and Design Institute Group Co., Ltd. (19-21-1, 2022KY53ZD(CYH)-10); China Railway Tunnel Group Co., Ltd. (CZ02-02-08); PowChina Hebei Transportation Highway Investment Development Co., Ltd. (TH-201908); Sichuan Railway Investment Group Co., Ltd. (SRIG2019GG0004); The Science and Technology major program of Guizhou Province [2018]3011.

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