Tunnel Point Cloud Research Articles

Tunnel Cross-Section Deformation Monitoring Based on Mobile Laser Scanning Point Cloud

Mobile laser scanning (MLS) has emerged as a pivotal tool for accurately collecting tunnel point cloud data and enabling the detection of tunnel deformation. This study introduces a novel approach for the precise monitoring of tunnel cross-section deformation, a critical factor in assessing stability and lining safety. The MLS system used in this study is the Self-mobile Intelligent Laser Scanning System (SILSS) for data acquisition. A comparison with corresponding data acquired by Leica P16 demonstrates that the data collected by SILSS are accurate. The methodology developed utilizes ellipticity parameters and deformation analysis indices based on the ellipse-fitting analysis of circular shield tunnel deformation. A key innovation is the robust denoising of data using the Random Sample Consensus (RANSAC) method, ensuring accurate ellipse fitting and extraction of tunnel lining. Subsequently, an algorithm segmented the tunnel cross-section lining into individual shield tunnels, enabling the calculation of ellipticity parameters for shield tunnels, which are the objects for deformation analysis. The experimental results underscore the novelty and effectiveness of this approach in monitoring deformation across different indices. The method proves to be a reliable tool for assessing tunnel health, providing a detailed evaluation of the cross-section’s condition through statistical and graphical visualization. This study significantly advances shield tunnel monitoring, offering a practical and precise methodology for tunnel deformation analysis based on MLS point cloud data.

Denoising algorithm for inclined tunnel point cloud data based on irregular contour features

Denoising algorithm for inclined tunnel point cloud data based on irregular contour features

Semantic segmentation of large-scale segmental lining point clouds using 3D deep learning

It is highly challenging to comprehensively measure the deformations of large-scale tunnel structures because of the numerous individual segments of tunnel linings. Laser scanning and 3D deep learning (DL) can provide abundant geometric data and the ability to interpret such data automatically. To fill the gap in applying 3D DL to the segment-wise segmentation of large-scale tunnel point clouds, a new 3D dataset and an optimized DL network are presented in this paper. The point cloud dataset was manually annotated by labeling every individual segment and element. Subsequently, a 3D DL network capable of handling large-scale point clouds (approximately 106 points) was developed to perform the data experiments. The influences of network parameters, namely, the number of neighbor points, layer number, down-sampling ratio, and channel number, were investigated in the data experiments. The results indicate that the number of neighboring points and channel number have a notable influence on the segmentation performance, with the best mean interaction over union reaching 85.5%. This study provides preliminary insights into the 3D DL network design for large-scale tunnel point clouds.

A subway tunnel image stitching method based on point cloud mapping relationships and high-resolution image

The paper presents an intelligent tunnel 3D laser image acquisition system. The system can quickly acquire the 3D point cloud and tunnel surface image of the whole tunnel section. The paper proposes the subway tunnel image stitching method based on point cloud mapping relationships and high-resolution image. Firstly, the method extracts the homonymous points from the mapped image generated from the 3D point cloud and the camera image based on the maximum information coefficient method. Secondly, the 3D point cloud positions corresponding to the homonymous points are determined. The optimization of camera parameters is achieved based on the gradient descent method. Then, the tunnel point cloud is expanded to generate a gray scale image based on spherical projection. Based on the optimized parameters, the point cloud is projected to the camera image to determine the depth value of each camera image pixel point. Finally, the pixel values of the camera image are projected based on the spherical projection to obtain the stitching result images. This method stitches individual images together to form a more complete tunnel surface image. The larger tunnel surface image helps in identifying tunnel defects.

Seg2Tunnel: A hierarchical point cloud dataset and benchmarks for segmentation of segmental tunnel linings

Point clouds provide a novel and effective alternative to understanding the structural behaviours of segmental tunnel linings. 3D deep learning (DL) has emerged as a promising technology capable of automatically deriving point-wise semantic and instance labels from point clouds. The utilisation of 3D DL in segment segmentation of tunnel point clouds has not been explored and the development of tailored 3D DL networks has been hindered by the absence of specialised datasets and benchmarks. To bridge this gap, this paper introduces a richly annotated hierarchical dataset: ‘Seg2Tunnel’, acquired from five tunnels and including 1,300 tunnel rings. Using the Seg2Tunnel dataset, the feasibility of applying 3D DL to the segment segmentation is demonstrated for the first time. Experiments are conducted to investigate the influences of training set size, data augmentation strategy, input size, and hyperparameter on the performance of trained 3D DL models and to provide benchmarks and insights for future uses of the Seg2Tunnel dataset. The 3D DL models trained by the Seg2Tunnel dataset outperform currently existing image- and voxel-based DL methods. The Seg2Tunnel dataset and benchmarks are fundamental in shaping the design of 3D DL networks tailored for tunnel point clouds. The study provides a novel paradigm for automatically understanding the tunnel structural elements in the point clouds, paving the way for unmanned construction and intelligent evaluation of segmental tunnel linings.

Automatic tunnel point cloud creation using photogrammetry and deviation analysis of excavation and shotcrete surfaces

AbstractDuring new Austrian tunnelling method excavation, it is necessary to carry out an analysis of the compliance of the actual surfaces with the theoretical lines prescribed in the support types for each tunnel excavation step. With our own software solutions, we created point clouds of the excavation and shotcrete of the primary tunnel support through the automated process of photogrammetric data. Together with the tunnel axis and theoretical profiles, the georeferenced and filtered point clouds were then used in the analysis of the deviation from the theoretical lines of excavation and primary support shotcrete and shotcrete thickness. The final products of the independent analysis are deviation profiles and unwrapped extrados views with numerical results and a color legend.

Combining Cylindrical Voxel and Mask R-CNN for Automatic Detection of Water Leakages in Shield Tunnel Point Clouds

Water leakages can affect the safety and durability of shield tunnels, so rapid and accurate identification and diagnosis are urgently needed. However, current leakage detection methods are mostly based on mobile LiDAR data, making it challenging to detect leakage damage in both mobile and terrestrial LiDAR data simultaneously, and the detection results are not intuitive. Therefore, an integrated cylindrical voxel and Mask R-CNN method for water leakage inspection is presented in this paper. This method includes the following three steps: (1) a 3D cylindrical-voxel data organization structure is constructed to transform the tunnel point cloud from disordered to ordered and achieve the projection of a 3D point cloud to a 2D image; (2) automated leakage segmentation and localization is carried out via Mask R-CNN; (3) the segmentation results of water leakage are mapped back to the 3D point cloud based on a cylindrical-voxel structure of shield tunnel point cloud, achieving the expression of water leakage disease in 3D space. The proposed approach can efficiently detect water leakage and leakage not only in mobile laser point cloud data but also in ground laser point cloud data, especially in processing its curved parts. Additionally, it achieves the visualization of water leakage in shield tunnels in 3D space, making the water leakage results more intuitive. Experimental validation is conducted based on the MLS and TLS point cloud data collected in Nanjing and Suzhou, respectively. Compared with the current commonly used detection method, which combines cylindrical projection and Mask R-CNN, the proposed method can achieve water leakage detection and 3D visualization in different tunnel scenarios, and the accuracy of water leakage detection of the method in this paper has improved by nearly 10%.

Three-Dimensional Point Cloud Denoising for Tunnel Data by Combining Intensity and Geometry Information

At present, three-dimensional laser scanners are used to scan subway shield tunnels and generate point cloud data as the basis for extracting a variety of information about tunnel defects. However, there are obstacles in the tunnel such as pipelines, tracks, and signaling systems that cause noise in the point cloud. Usually, the data of the tunnel point cloud are huge, and the efficiency of artificial denoising is low. Faced with this problem, based on the respective characteristics of the geometric shape and reflection intensity of the tunnel point cloud and their correlation, this paper proposes a tunnel point cloud denoising method. The method includes the following three parts: reflection intensity threshold denoising, joint shape and reflection intensity denoising, and shape denoising. Through the experiment on the single-ring segment point cloud of a shield tunnel, the method proposed in this paper takes 2 min to remove 99.77% of the noise in the point cloud. Compared with manual denoising, the method proposed in this paper takes two fifteenths of the time to achieve the same denoising effect. The method proposed in this paper meets the requirements of a tunnel point cloud data survey. Thus, it provides support for the efficient, accurate, and automatic daily maintenance and surveys of tunnels.

Novel Feature Preserving Method for Simplifying the Surface Point Cloud of Underground Space Tunnel

Novel Feature Preserving Method for Simplifying the Surface Point Cloud of Underground Space Tunnel

A Point Cloud Segmentation Method for Dim and Cluttered Underground Tunnel Scenes Based on the Segment Anything Model

In recent years, point cloud segmentation technology has increasingly played a pivotal role in tunnel construction and maintenance. Currently, traditional methods for segmenting point clouds in tunnel scenes often rely on a multitude of attribute information, including spatial distribution, color, normal vectors, intensity, and density. However, the underground tunnel scenes show greater complexity than road tunnel scenes, such as dim light, indistinct boundaries of tunnel walls, and disordered pipelines. Furthermore, issues pertaining to data quality, such as the lack of color information and insufficient annotated data, contribute to the subpar performance of conventional point cloud segmentation algorithms. To address this issue, a 3D point cloud segmentation framework specifically for underground tunnels is proposed based on the Segment Anything Model (SAM). This framework effectively leverages the generalization capability of the visual foundation model to automatically adapt to various scenes and perform efficient segmentation of tunnel point clouds. Specifically, the tunnel is first sliced along its direction on the tunnel line. Then, each sliced point cloud is projected onto a two-dimensional plane. Various projection methods and point cloud coloring techniques are employed to enhance SAM’s segmentation performance in images. Finally, the semantic segmentation of the entire underground tunnel is achieved by a small set of manually annotated semantic labels used as prompts in a progressive and recursive manner. The key feature of this method lies in its independence from model training, as it directly and efficiently addresses tunnel point cloud segmentation challenges by capitalizing on the generalization capability of foundation model. Comparative experiments against classical region growing algorithms and PointNet++ deep learning algorithms demonstrate the superior performance of our proposed algorithm.

Dual attention-based deep learning network for multi-class object semantic segmentation of tunnel point clouds

Aiming to automatically segment multi-class objects on the tunnel point cloud, a deep learning network named dual attention-based point cloud network (DAPCNet) is developed in this paper to act on point clouds for segmentation. In the developed model, data normalization and feature aggregation are first processed to eliminate data discrepancies and enhance local features, after which the processed data are input into the built network layers based on the encoder-decoder architecture coupled with an improved 3D dual attention module to extract and learn features. Furthermore, a custom loss function called Facal Cross-Entropy (“FacalCE”) is designed to enhance the model's ability to extract and learn features while addressing imbalanced data distribution. To validate the effectiveness and feasibility of the developed model, a dataset of tunnel point clouds collected from a real engineering project in China is employed. The experimental results indicate that (1) the developed model has excellent performance with Mean Intersection over Union (MIoU) of 0.8597, (2) the improved 3D dual attention module and “FacalCE” contribute to the model performance, respectively, and (3) the developed model is superior to other state-of-the-art methods, such as PointNet and DGCNN. In summary, the DAPCNet model exhibits exceptional performance, offering effective and accurate results for segmenting multi-class objects within tunnel point clouds.

GL-Net: Semantic segmentation for point clouds of shield tunnel via global feature learning and local feature discriminative aggregation

has gradually become the first choice of modern urban public transportation due to its advantages of safety and high-efficiency. Shield tunnel is an important type of subway tunnel, and its structural stability and safety play an important role in subway operation. The shield tunnels are prone to problems such as water leakage and tunnel collapse, which affect the safe operation of subways. Efficient monitoring methods are required to detect the status of subway tunnels. The data collection and accurate segmentation of key components of shield tunnels are the basis and key to the automatic monitoring of subway tunnels. This research presents a novel semantic segmentation method of three-dimensional (3-D) point clouds of typical structural elements (e.g., longitudinal joint, circumferential joints, bolt hole and grouting hole) in shield tunnel based on deep learning. In this method, we focus on how to make the network learn robust global features and complex local distribution patterns. Further, we propose a global and local feature encoding block (namely GL-block) to discriminatively aggregate local features while learning global representation. After multiple encodings by the GL-block, we design a global correlation modeling (GCM) module to establish a global awareness of each point. Finally, a weighted cross-entropy loss function is designed to solve the problem of unbalanced number of samples in each category of shield tunnel. In the experiments, we make a dataset of shield tunnel point clouds with a length of about 1,000 m collected by CNU-TS-1 (DU et al., 2018) mobile tunnel monitoring system, and use the dataset to train and test the segmentation ability of our method on the typical structural elements of shield tunnels. Experiments verify the effectiveness of our method by comparing with the other state-of-the-art 3-D point cloud semantic segmentation methods, and our method has an mIoU score of 73.02 %, which is at least 14.54 % higher than the other compared state-of-the-art networks. Also, we further verify the adaptability of our method to different tunnels and different laser scanning equipment, such as FARO, Leica and Z + F, and achieve very advanced performance.

3D Laser Scanning Acquisition and Modeling of Tunnel Engineering Point Cloud Data

For tunnel deformation analysis using traditional measurement methods to obtain tunnel section data, there are problems such as small data coverage and low efficiency. 3D laser scanning technology has the advantages of automatic, high precision and high efficiency in collecting the point cloud data of the target object, and can completely and accurately express the target entity. Based on the tunnel point cloud acquired by 3D laser scanner, the tunnel engineering modeling research is carried out in this paper. Firstly, the engineering survey and 3D reconstruction technology of shield tunnel were carried out based on 3D laser scanning technology. The total station layout control was used to scan the subway platform and tunnel interval, and the high-precision laser point cloud data were obtained. Secondly, a random sampling consistency algorithm is proposed to extract engineering measurement results such as tunnel axis and cross section. Finally, the 3D modeling of the tunnel is established by using the stretching setting out modeling method. Taking Yuzhu Tunnel planning and acceptance project as an example, the experimental results show that the method can effectively visualize the overall deformation of the tunnel, and provide accurate and scientific spatial data for tunnel engineering measurement, operation and maintenance.

Direct generation of finite element mesh using 3D laser point cloud

An approach for the finite element (FE) mesh generation of tunnel structures considering the lack of detail, low automation, and intermediate link transformation in the FE mesh generation of tunnels based on the 3D laser point cloud is presented in this paper. To this end, we acquired the tunnel point cloud model using terrestrial laser scanning or mobile laser scanning, and uniform nodes from the raw point cloud using a clock simulation extraction algorithm we developed. In addition, we assign topology information to these nodes. Further, we generated an FE model of the tunnel structure suitable for numerical simulation analysis using Abaqus. We applied the developed method to three types of tunnels; the mesh satisfied the evaluation standards of practical engineering applications. We examined the impact of mesh size and used an engineering example to confirm the method's accuracy. Finally, we provide recommendations for future research considering the future trend of FE mesh generation based on the 3D laser point cloud and its improvements.

Attention-enhanced sampling point cloud network (ASPCNet) for efficient 3D tunnel semantic segmentation

Laser scanning is used as a modern means to capture data from tunnels to assess their condition, but automated processing requires robust component detection and deterioration characterization. In order to segment 3D tunnel point clouds aiming at more accurate results with high time efficiency, this paper describes a point cloud technique that collects actual tunnel scenes and develops an attention-enhanced sampling point cloud network named ASPCNet. In the developed model, the feature embedding module is responsible to process the point cloud data for local features followed by the attention module for enhancing the feature extraction and learning. Additionally, the point downsampling-upsampling structure fully assists the model to strengthen the capability to process point clouds for time efficiency. In the training process, a weighted focal loss is designed to enhance the model learning by eliminating the effect of data imbalance. The developed ASPCNet is trained and then tested on a dataset collected from a cross-river metro tunnel section in China, demonstrating its efficiency and effectiveness. In comparison with different sampling ratios, state-of-the-art methods, and sampling methods, the ASPCNet with a uniform sampling rate of 2 exhibits the best performance, achieving an overall accuracy of 0.9758, a mean Intersection over Union (MIoU) of 0.8988, and an inference time of 4.1 s, demonstrating that the sampling structure involved in this research boosts the time efficiency, the developed model has superior performance, and the sampling method adopted is beneficial to strengthen the model performance.

Semi-supervised learning-based point cloud network for segmentation of 3D tunnel scenes

Automatic identifying target multi-class objects in tunnel scenes from 3D point clouds is widely thought to be critical for maintaining the healthy condition of the tunnel using deep learning methods. However, those methods require extensive data with labels, which is time-consuming and labor-intensive. Targeting effective multi-class tunnel point cloud segmentation for practical applications, this research proposes a deep learning method named semi-supervised learning-based point cloud network (SPCNet) to boost segmentation by alleviating labeling tasks. It contains a supervised learning module, a self-training module, a mean teacher-based learning module, loss functions, and evaluation metrics. To validate the effectiveness and reliability of the proposed method SPCNet, a point cloud collected from a real tunnel is implemented. The results indicate that the proposed method SPCNet performs excellently with MIoU of 0.8741 and 0.8583 in Scenarios I and II, respectively; as well as superior to the supervised learning method with MIoU of 0.8152 and 0.7552 in Scenarios I and II and other state-of-the-art methods such as ST++ and ST. Accordingly, the proposed method SPCNet has superior performance, beneficially contributing to multi-class object segmentation of 3D tunnel point clouds with great potential for applications in practice.

Water leakage mapping in concrete railway tunnels using LiDAR generated point clouds

Detection of water leakage areas is one of the most important tasks of regular underground tunnel inspections. Current manual visual assessments are plagued by reduced efficiency and poor accuracy, requiring personnel to physically access tunnels to conduct assessments. This form of monitoring is no longer sustainable and thus a more cost-effective method is required. Light detection and ranging (LiDAR) is one such method and commonly known for its ability to generate high-density 3D coordinated point clouds of scanned environments. In addition to point coordinates an intensity value, highly dependent on surface properties and the backscattered energy of the laser beam, is recorded. In this study properties such as surface colour, roughness and state of saturation are reviewed. Results of systematic tests of different colour and concrete targets, scanned using a mobile LiDAR scanner, are discussed. The aim of the research was to prove the aptitude for remote sensing of water leakage areas in underground tunnels and provide an automated workflow to extract quantitative information of each leak. Field tests demonstrated the ability to rapidly capture, identify, extract and record areas of water leakage based on the intensity and spatial information of tunnel point cloud data. This is particularly useful as water ingress is known to degrade concrete, resulting in the earlier onset of corrosion, spalling and loss of strength. The LiDAR scanner used here proved capable of reducing survey time and provided inspectors with a complete 3D model of the tunnel which was supplemented with quantitative leakage information (location and area).

Dislocation Detection of Shield Tunnel Based on Dense Cross-Sectional Point Clouds

Tunnel dislocation affects the stability and waterproof of the structure and endangers its service life. This paper presents a dislocation calculation method of shield tunnel based on cross sectional point clouds, which contain three main steps. First, a longitudinal joint detection method is proposed based on the circumferential joints detected by the existing gradient accumulation method. The point clouds containing capping block in each ring are converted into gray image to obtain the center line position of the block, and the longitudinal joints are calculated through combining with the tunnel design data. Second, tunnel appendages are removed quickly through unfolding the tunnel point clouds by cylinder projection and adjusting the parameters of the cloth simulation filtering algorithm. Finally, the circumferential dislocations are calculated by selecting multiple denoised sections on both sides of the joint and clustering the points at each angle. Meanwhile, the longitudinal dislocations are calculated through fitting the segments in each ring separately. Experimental results show that the automatic extraction rate of longitudinal joints is higher than 94%. The point clouds filtering method can quickly separate the very long tunnel lining from the close appendages attached to it. Meanwhile, the RMSE of the repeated circumferential and longitudinal dislocation are evaluated to be 1.56 mm and 0.57 mm respectively. Compared with the existing state of art methods, these values reach 1.75 mm and 0.63 mm. Through the method proposed, the dislocation value at any mileage and any angle of tunnel can be displayed intuitively.

3D RECONSTRUCTION AND RENDERING VISUALIZATION OF TUNNEL POINT CLOUD

Abstract. This paper presents a method for rapid and automatic visualisation of over-undercutting based on 3D reconstruction and rendering of tunnel point clouds. The method uses model filtering and our improved voxel filtering for point cloud lite to maximise the preservation of the original tunnel point cloud features while streamlining the point cloud. The 3D reconstruction is then completed according to our proposed alpha-shape algorithm with low reconstruction parameters combined with a fast model optimisation model, which achieves high reconstruction accuracy while significantly improving computational efficiency to meet the requirements of the tunneling project. The rendering method is calculated by selecting the RGB values. The darker the colour, the heavier the degree of over-excavation or under-excavation. According to the calculated over-excavation or under-excavation value, the ratio of the point in the over-excavation or under-excavation interval is obtained in turn, and the gradient is rendered by increasing the base colour of each interval proportionally; the kd-tree is then introduced to calculate and visualise the colour information of the reconstructed model. In addition, this paper uses real-world tunnel point cloud data and demonstrates that the method meets expectations through qualitative and quantitative evaluation. Visualisation of the tunnel point cloud is accomplished while maintaining speed, visualising the over-under-excavation situation and providing an outlook for future work.

Adaptively unsupervised seepage detection in tunnels from 3D point clouds

To address the low efficiency and subjectiveness in manual visual inspection in operational tunnels, a hybrid approach that integrates the 3D Otsu method and K-nearest neighbors are developed for seepage detection from point clouds. Besides, the Delaunay boundary distance threshold method is used to adaptively extract the tunnel’s inner surface. The 3D Otsu method preliminarily segments the metro tunnel point cloud between seepage and non-seepage parts according to the RGB value. The KNN method is followed to denoise the misidentified seepage points by the 3D Otsu method. A real tunnel case in the Wuhan metro system, China, is used to demonstrate the applicability and efficiency of the developed approach. Results indicate that the proposed approach is effective and stable for noise reduction, where 33.26% of the point clouds are identified as noise data and are later eliminated. Compared to the 3D Otsu method and the KNN method alone, the integrated 3D Otsu-KNN method can achieve a higher detection accuracy. The proposed approach can also achieve a comparable detection accuracy to the supervised learning method (e.g. support vector machine). It can be used as a decision tool to automate seepage detection and quantify the severity of seepage in underground tunnels.