Multi-parameter sensing technology plays an important role in structural health monitoring. In this paper, a novel FBG sensor with a pendulum structure for simultaneous measurement of tilt and acceleration is proposed. The design, principle and performance of the FBG sensor is introduced and tested in detail. The results show that the tilt sensitivity and acceleration sensitivity of the proposed FBG sensor can reach 408.3 pm/° and 2.43 nm/(m/s2), respectively, and that it works below 32 Hz in weak vibration detection. The theory analysis and the test result are in good agreement with each other. A wavelet transform method is utilized to separate the tilt and acceleration from the original signal received by the proposed FBG sensor. Cross sensitization of tilt and acceleration can be avoided effectively using this method. Finally, a field test is conducted in a railway slope. The recorded data is analyzed, which confirms further that the proposed FBG sensor can detect tilt and acceleration simultaneously. This novel FBG sensor provides a new method for multi-parameter monitoring.

The automatic segmentation of leakage in rail tunnel linings is a useful and challenging task. Unlike other scenarios, the complex environment inside the tunnels makes it difficult to obtain accurate results for the segmentation of leakages. Some deep learning-based methods have been used to automatically segment tunnel leakage, but these methods ignore the interdependencies between feature channels, which are very important for extracting robust leakage feature representations. In this work, we propose an atrous channel pyramid attention network (ACPA-Net) for rail tunnel lining leakage segmentation. In ACPA-Net, the proposed atrous channel pyramid attention (ACPA) module is added into a U-shaped segmentation network. The ACPA module can effectively strengthen the representation ability of ACPA-Net by explicitly modeling the dependencies between feature channels. In addition, a deep supervision strategy that helps ACPA-Net improve its discrimination ability has also been introduced into ACPA-Net. A rail tunnel leakage image dataset consisting of 1370 images with manual annotation maps is built to verify the effectiveness of ACPA-Net. The final experiment shows that ACPA-Net achieves state-of-the-art performance on the Crack500 dataset and our rail tunnel leakage image dataset, and our method has the least number of parameters of all the methods.

In this paper, a soft fiber Bragg grating (FBG) strain sensor was constructed of a rubber strip, FBGs and steel plates, which exhibits the advantages of high precision and a small size. A series of FBGs was uniformly pasted on a flexible rubber strip which can monitor the slope deformation by measuring the bending deformation of the rubber strip. Most notably, this sensor can be used to monitor horizontal displacement in the subsurface of the slope model. The relationships among the bending angle of the rubber strip, the strain of the rubber strip, and the subsurface deformation of the slope model were established. In addition, the subsurface deformation of the slope model can be obtained by the FBG strain sensor monitoring. Since a rigid-flexible structure was formed by uniformly pasting a series of steel plates on the other side of the rubber strip, the sensitivity of the FBG strain sensor was improved to be 1.5425 nm/°. The measurement results verify that the FBG strain sensor shows good performance, and the model test results demonstrate that the FBG strain sensor can be used for monitoring the subsurface deformation of the slope model.

A large number of slopes appear along the line during railway construction, which will pose a threat to railway safety operation. Slope monitoring plays an important role in ensuring the safety of railway operation. Aiming at the difficulties of sensor multiplexing, low accuracy, and large disturbance by trains, this paper proposes a railway slope monitoring method based on integrated fusion detection of inclination and vibration. Instability and failure characteristics of the K3 slope in Shuohuang Railway and dynamic characteristics under the excitation of the train load are analyzed by the finite element method (FEM) analysis. Based on the above analysis, a slope monitoring system is established utilizing the self-developed dual-parameter fiber Bragg grating (FBG) sensor. The monitoring data of the past four years show that the slope is in a relatively stable state at present. The monitoring data are consistent with the results of the FEM. The feasibility of the damage identification method based on inclination and vibration characteristics is verified, which provides a new method for railway slope monitoring.

A highly integrated sensing technology, combining a stimulated Brillouin scattering-based distributed sensor with XFG (fiber Bragg grating (FBG) and long-period fiber grating (LPFG)) sensors on a single fiber, is proposed for the simultaneous measurement of fully distributed and multiple discrete dynamic strains/temperatures. A multiparameter monitoring scheme for slope safety is developed using this integrated sensing technology. An indoor simulation test is carried out to verify its ability to simultaneously monitor a slope’s surface displacement, an anchor reinforcement’s axial force, and rockfall vibration. The experimental results show that distributed static strain and discrete dynamic strain can be well-measured simultaneously with little interference. The results also demonstrate the XFG sensors’ capability for multi-type and multipoint multiplexing. In addition, the proposed hybrid sensor system has potential for the monitoring of multiple slope parameters simultaneously.