Anchor bolt corrosion is a complex and dynamic system, and the prediction and identification of its corrosion degree are of significant importance for engineering safety. Currently, non-destructive testing using ultrasonic guided waves can be employed for its detection. Building upon the analysis of anchor bolt corrosion mechanisms, this paper proposes a method for evaluating the corrosion degree of anchor bolts based on multi-scale convolutional neural networks (MS-CNNs) that address the multi-mode propagation and dispersion effects of ultrasonic guided wave signals in non-destructive testing. Electrochemical experiments were conducted to simulate anchor bolt corrosion, and ultrasonic guided wave non-destructive testing was performed every 12 h to obtain waveform data. An MS-CNN was then utilized to accurately diagnose the corrosion degree of the anchor bolts. The test results demonstrate that this method effectively detects and diagnoses the extent of anchor bolt corrosion, facilitating timely troubleshooting and preventing potential safety accidents.

A rock slope in an unstable state usually needs bolt support to avoid major disasters, but due to the complexity and uncertainty of the factor of safety (FOS) and the attributes of bolting parameters are not unified, it is not easy to acquire a satisfactory scheme. In this article, a modelling optimization method based on feedforward neural network (FNN) and particle swarm optimization (PSO) algorithm is proposed. Firstly, the process of slope bolting is simulated to obtain the FOS and an FNN model with growth mechanism is established. Secondly, the PSO algorithm is improved to realize the comprehensive consideration of discrete and continuous parameters and reduce the computational complexity with high dimensions. Finally, the accuracy of the FNN model is tested by cross-validation, and the experimental results show that the optimized bolting scheme can enhance slope stability and provide reference values for the reinforcement of slopes in actual engineering situations.

With the rapid development of high-speed railways, the issue of train speed tracking has become increasingly important. A controller based on a Long Short-Term Memory (LSTM) module was designed to meet the high-precision tracking requirements of trains, and its convergence analysis was analyzed. At the same time, in order to further improve the control performance on the basis of the original controller, an adaptive proportional compensation module was designed, and its effectiveness was theoretically proven. Finally, simulation validation was conducted on a multi-particle model with a large number of perturbations for both methods. The results show that the LSTM controller reaches good control results under external interference, and the adaptive proportional compensation module can further improve the control performance of the original controller.

Electrical treeing is a defect of cable aging. Partial discharges within the insulation cause local electrical field anomalies and minor distortions, which act as initiation points for the formation of an electrical tree. As the electrical tree grows, it can eventually lead to insulation breakdown. Therefore, it is of practical importance to study the growth mechanisms of electrical treeing in cable insulation. The growth mechanism of electrical treeing in cross-linked polyethylene is analyzed from the perspective of its aggregation structure. Based on the growth morphology and the rate of electrical tree growth, the development process is divided into three stages: initial stage, rapid growth stage, and stagnant growth stage. The rapid growth stage is further categorized into the early and late stages. COMSOL finite element simulation software is used to model the entire to analyze the electric field distribution at each stage. In addition, the influence of carbon particles, varying in positions, sizes, and densities, on electrical treeing is studied to better understand its growth characteristics. The results showed that the electric field distortion is more serious when carbon particles are close to the tip of the needle and the tip of the electric treeing. At the same growth stage, the electric field intensity was negatively correlated with carbon particle size and positively correlated with density. When size and density are the same, the intensity of electric field increases gradually with the development of the growth stage. The effect of carbon particles on the insulation breakdown characteristics is further revealed.

As an important means of reinforcement and support, the bolt can effectively resolve the problem of slope instability. Micro-Negative Poisson Ratio (Micro-NPR) bolts are superior to conventional bolts in mitigating large deformations caused by geological shifts. A large number of bolt anchoring systems require non-destructive testing technology for quality inspection. This technology utilizes time-domain signal characteristics to detect internal defects in the bolt anchoring systems of support engineering. The combination of stress wave nondestructive detection technology and modular convolutional neural network method can identify the shear deformation in the case of the anchor slope support. Integrating the identification results of both the shear angle and shear location sub-modules improves the accuracy of detecting shear deformation in micro-NPR bolt anchoring system, which will be of great assistance in our future engineering applications.