RM2D: An automated and robust laser-based framework for mobile tunnel deformation detection

Abstract

As mining operations extend to greater depths, the risk of deformation in high-stress tunnels increases significantly, posing a substantial threat. This study introduces a novel framework known as “robust mobility deformation detection” (RM2D), designed for real-time tunnel deformation detection. RM2D employs mobile LiDAR scanner to capture real-time point cloud data within the tunnel. This data is then voxelized and analyzed using covariance matrices to create a voxel-based multi-distribution representation of the rugged tunnel surface. Leveraging this representation, we assess deformations and scrutinize results through machine learning models to swiftly pinpoint tunnel deformation locations. Extensive experimental validation confirms the framework’s capacity to successfully detect deformations, including floor heave, side rib spalling, and roof fall, with remarkable accuracy. For deformation levels at 0.15 m, RM2D was able to successfully detect deformations with an area greater than 2 m². For deformation areas of (3 ± 0.5) m², RM2D successfully detected deformations of levels at (0.05 ± 0.01) m, and its detection capability meets the standard criteria for mining tunnel deformation detection. When compared to two conventional methods, RM2D demonstrates its real-time deformation detection capability in complex environments and on rough surfaces with precision, all at speeds below 10 km/h. Furthermore, we evaluated the predictive performance using multiple evaluation metrics and provided insights into the decision mechanism of the machine learning employed in our research, thereby offering valuable information for practical engineering applications in tunnel deformation detection.

Keywords

Deformation detection / LiDAR scanning / Distribution modeling / Machine learning / Point clouds

CRediT authorship contribution statement

Boxun Chen: Writing - original draft, Visualization, Validation, Methodology, Investigation, Formal analysis, Data curation. Ziyu Zhao: Validation, Methodology, Data curation. Lin Bi: Writing - review & editing, Funding acquisition, Conceptualization. Zhuo Wang: Writing - review & editing, Conceptualization.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgement

This work is supported in part by the National Key Research and Development Program of China (Grant No. 2023YFC2907305).

Data availability

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

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