Accurate and robust registration of high-speed railway viaduct point clouds using closing conditions and external geometric constraints

AbstractDue to the increasing population and resulting transportation needs, the number of subway and high-speed railway projects has also increased. The geometric constraints of such projects have caused many tunnels to be built in weak ground. Thus, weak ground tunnelling has attracted the attention of tunnel engineers and researchers. The main purposes of this study are to analyse the T4 tunnel excavated in weak ground and to compare the results obtained from the analytical solutions and 3D numerical analyses. This study specifically considers the T4 tunnel support system used in the Ankara İzmir High Speed Railway Project (Afyonkarahisar-Banaz Section). The T4 tunnel route encounters weak ground composed of layers of extremely weak mudstone, clayey sand, weakly cemented sandstone, and silty–clay matrix with pebbles. The tunnel overburden ranges from 10 to 35 m, which is shallow. After the excavation work of the T4 tunnel, severe deformation and critical stability problems in the shallow part (where the overburden is approximately 10 m) were encountered inside the tunnel, leading to a halt in construction. This was followed by revisions to the tunnel support system, leading to successful completion of the tunnel excavation. Numerical simulations of the low overburden section are performed using the commercially available FLAC3D program that uses the finite difference method. The characteristics of insufficient/ineffective support systems and adequate support systems for shallow tunnels excavated through weak ground are discussed in this study. Additionally, problems that pertain to the tunnel itself and its support system are discussed. The results of the 3D numerical analyses and analytical solutions are compared, and the advantages of 3D numerical analyses are discussed. The importance and necessity of tunnel face stability and roof stability for tunnel stability in weak ground is illustrated. Consequently, solutions based on analytical and numerical analyses are presented, and the analysis methodology and solutions proposed in the study can help guide weak ground tunnelling design and evaluation.

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Ground-Based Radar Interferometry for Monitoring the Dynamic Performance of a Multitrack Steel Truss High-Speed Railway Bridge

Remote Sensing ◽

10.3390/rs12162594 ◽

2020 ◽

Vol 12 (16) ◽

pp. 2594

Author(s):

Qihuan Huang ◽

Yian Wang ◽

Guido Luzi ◽

Michele Crosetto ◽

Oriol Monserrat ◽

...

Keyword(s):

Dynamic Behavior ◽

High Speed ◽

Point Clouds ◽

Vibration Monitoring ◽

Dynamic Monitoring ◽

Railway Bridge ◽

High Speed Railway ◽

Testing Procedures ◽

Microwave Radar ◽

High Speed Trains

With the continuous expansion of the high-speed railway network in China, long-span railway bridges carrying multiple tracks demand reliable and fast testing procedures and techniques. Bridge dynamic behavior analysis is a critical process in ensuring safe operation of structures. In this study, we present some experimental results of the vibration monitoring of a four-track high-speed railway bridge with a metro–track on each side: the Nanjing–Dashengguan high-speed railway bridge (NDHRB). The results were obtained using a terrestrial microwave radar interferometer named IBIS-S. The radar measurements were interpreted with the support of lidar point clouds. The results of the bridge dynamic response under different loading conditions, including high-speed trains, metro and wind were compared with the existing bridge structure health monitoring (SHM) system, underlining the high spatial (0.5 m) and temporal resolutions (50 Hz–200 Hz) of this technique for railway bridge dynamic monitoring. The detailed results can help engineers capturing the maximum train-induced bridge displacement. The bridge was also monitored by the radar from a lateral position with respect to the bridge longitudinal direction. This allowed us to have a more exhaustive description of the bridge dynamic behavior. The different effects induced by the passage of trains through different tracks and directions were distinguished. In addition, the space deformation map of the wide bridge deck under the eccentric load of trains, especially along the lateral direction (30 m), can help evaluating the running stability of high-speed trains.

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Real-Time Geometric Parameter Measurement of High-Speed Railway Fastener Based on Point Cloud from Structured Light Sensors

Sensors ◽

10.3390/s18113675 ◽

2018 ◽

Vol 18 (11) ◽

pp. 3675 ◽

Cited By ~ 2

Author(s):

Hao Cui ◽

Qingwu Hu ◽

Qingzhou Mao

Keyword(s):

Real Time ◽

Geometric Parameter ◽

Point Cloud ◽

High Speed ◽

Region Growing ◽

Point Clouds ◽

Parameter Measurement ◽

High Speed Railway ◽

Rail Track ◽

Accuracy And Repeatability

With the increase in the number of service years for high-speed railways, the foundation of the rail track suffers from settlement, which causes rail track irregularity. To adjust the position of the track and meet track regularity demands, several components of the fastening system will be replaced by different sized components. It is important to measure the exact geometric parameters for the components of a fastening system before adjusting the track. Currently, the measurement process is conducted manually, which is laborious and error-prone. In this paper, a real-time geometric parameter measurement system for high-speed railway fastener based on 2-D laser profilers is presented. Dense and precise 3-D point clouds of high-speed railway fasteners are obtained from the system. A fastener extraction method is presented to extract fastener point cloud and a region-growing algorithm is used to locate key components of the fastener. Then, the geometric parameter of the fastener is worked out. An experiment was conducted on a high-speed railway near Wuhan, China to verify the accuracy and repeatability of the system. The maximum root-mean-square-error between the manual measurement and the system measurement is 0.3 mm, which demonstrates adequate accuracy. This system can replace manual measurements and greatly improve the efficiency of geometric parameter measurements for fasteners.

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LiDAR Point Cloud Recognition of Overhead Catenary System with Deep Learning

Sensors ◽

10.3390/s20082212 ◽

2020 ◽

Vol 20 (8) ◽

pp. 2212 ◽

Cited By ~ 1

Author(s):

Shuai Lin ◽

Cheng Xu ◽

Lipei Chen ◽

Siqi Li ◽

Xiaohan Tu

Keyword(s):

Deep Learning ◽

Point Cloud ◽

High Speed ◽

Point Clouds ◽

Parameter Measurement ◽

Stable Operation ◽

Automatic Inspection ◽

High Speed Railway ◽

Single Frame ◽

Catenary System

High-speed railways have been one of the most popular means of transportation all over the world. As an important part of the high-speed railway power supply system, the overhead catenary system (OCS) directly influences the stable operation of the railway, so regular inspection and maintenance are essential. Now manual inspection is too inefficient and high-cost to fit the requirements for high-speed railway operation, and automatic inspection becomes a trend. The 3D information in the point cloud is useful for geometric parameter measurement in the catenary inspection. Thus it is significant to recognize the components of OCS from the point cloud data collected by the inspection equipment, which promotes the automation of parameter measurement. In this paper, we present a novel method based on deep learning to recognize point clouds of OCS components. The method identifies the context of each single frame point cloud by a convolutional neural network (CNN) and combines some single frame data based on classification results, then inputs them into a segmentation network to identify OCS components. To verify the method, we build a point cloud dataset of OCS components that contains eight categories. The experimental results demonstrate that the proposed method can detect OCS components with high accuracy. Our work can be applied to the real OCS components detection and has great practical significance for OCS automatic inspection.

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