Year-end Sale % OFF 
Abstract
Rock bolts have been widely used to enhance the structural stability of underground infrastructures. Careful tracking of rock bolt positions is highly significant since it assists with operational success of ground support and has applications to predictive maintenance practices. This paper presents a practical algorithm, CFBolt , to detect rock bolts from a 3D laser scanned point cloud. Considering that rock bolts are relatively tiny objects, CFBolt follows a two-step coarse-to-fine strategy. It first computes a single-scale proportion of variance (POV) for each point as the local point descriptor and filters out near 95% not-bolt points with a simple but effective classifier, Linear Discriminant Analysis (LDA), which allows for the pruned point cloud to be then used as a compatible input to a deep neural network, designed and trained to precisely detect rock bolts from the pruned point cloud. CFBolt was tested for detecting rock bolts from LiDAR scan data collected from Sydney’s civil tunnelling project site. The entire dataset contains more than 160 million points. The obtained scores of Intersection over Union (IoU) and precision for individual bolt points were 89.33% and 92.04%, respectively. For rock bolt objects, the precision and recall were 98.34% and 98.73%, respectively. The detection quality of CFBolt is superior to the state-of-the-art 3D object detection algorithms and the newest rock bolt detection algorithm, demonstrating the robustness and effectiveness of CFBolt .
Highlights
Rock reinforcement, which refers to the support that is intrusive to the rock mass, such as rock bolts [26], is an essential part of every tunnelling project as it consolidates the stability of the tunnel structure
Compared with the F1scores of point-wise classification and rock bolt detection reported in their paper, which are 0.64 and 0.90, our proposed algorithm improves the score by 38% and 9% to 0.88 and 0.98, respectively
To demonstrate that using single-scale proportion of variance (POV) is a good choice for the rough estimation step, we report the performance of single-scale POV and multi-scale POV
Summary
Rock reinforcement, which refers to the support that is intrusive to the rock mass, such as rock bolts [26], is an essential part of every tunnelling project as it consolidates the stability of the tunnel structure. Rock bolts are steel rods that are installed into a rock mass to provide geotechnical support and are an essential part of any underground operation [17], [21]. Rock bolts can meet a variety of geological conditions and requirements necessary for a modern ground support system in an underground mine or tunnel [23]. Careful tracking of rock bolt positions is highly significant since it can assist with operational success of ground support and has applications to predictive maintenance practices [6], [24], which can highlight problems with bolting pattern design and demonstrate areas with poor rock conditions that may have been overlooked. It is challenging to monitor rock bolts in-situ due to the limitation of lighting and unavailability of global navigation signals in the underground environment [32], [35]
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
