Real-time, accurate, and robust positioning system plays a crucial role in many vehicular applications for automatic driving system and Vehicular Ad-hoc Network (VANET). In the tunnel, the positioning accuracy of Global Navigation Satellite System (GNSS) decreases due to blocked satellite signals. In order to estimate the exact location of vehicles in tunnel environments, many positioning systems have been presented. However, there is a lack of effort in systematically comparing, organizing and analyzing these existing positioning systems, and identifying the strengths and weaknesses of different technologies and applicable scenarios. Therefore, this paper undertakes a thorough investigation into current vehicle localization technologies and methods for tunnel scenarios. The analysis starts with discussing various application scenarios for vehicle positioning system. Then, various vehicle positioning technologies are investigated, the advantages and drawbacks of each technology are illustrated. Thereafter, we discuss some widely used positioning methods in terms of range-based localization method, range-free localization method, multi-sensor fusion localization method, and cooperative positioning (CP) method. Finally, we discuss some challenges faced in vehicle positioning for tunnel environments, and propose some potential research topics for future research work.

The selection of calculation parameters for slope excavation support design and the analysis of seepage stability is a significant challenge. This difficulty also hinders the development of slope support engineering. This study examined the right binary structure slope engineering of the K5 + 220–K5 + 770 section of the TJ1A mark of the Jiangkou-Weng’an Highway in Guizhou province. In this study, we propose and use the deep displacement monitoring data and p value test method to check the simulation parameters. Furthermore, the superposition calculation method for steady-state seepage analysis of slope geotechnical structure is proposed. A comparative analysis of the displacement, strain, stress, and safety factor of the slope after the application of pore water pressure was carried out for three slope conditions. The analysis showed that steady-state seepage has a significant effect on the displacement of the slope during the completion of excavation. As a result, a continuous distribution of strain arises on the slope along the interface between the potential sliding surface and the rock–soil layer, and then forms a continuous sliding zone. Additionally, steady-state seepage has a significant effect on the position of the displacement distribution during the initial support of the slope, leading to a significant increase in the extreme value of the shear outlet displacement of the potential slip surface of the slope and in the extreme value of equivalent strain. Finally, steady-state seepage reduces the displacement and equivalent strain upon construction of the secondary slope support. The steady-state seepage has a limited effect on the stress concentration, but reduces the safety factor calculated using the strength reduction method, in all three stages of slope excavation and support. This study enriches the analysis methods for determining the stability of a dual-structure slope during the rainy season, and provides new ideas for the safety and control of slope support projects.

Rockfalls on mountainous roads pose significant safety risks to pedestrians and vehicles, particularly in remote areas with underdeveloped communication infrastructure. To enable efficient detection, this study proposes a rockfall detection system based on embedded technology and an improved Yolov8 algorithm, termed Yolov8-GCB. The algorithm enhances detection performance through the following optimizations: (1) integrating a lightweight DeepLabv3+ road segmentation module at the input stage to generate mask images, which effectively exclude non-road regions from interference; (2) replacing Conv convolution units in the backbone network with Ghost convolution units, significantly reducing model parameters and computational cost while improving inference speed; (3) introducing the CPCA (Channel Priori Convolution Attention) mechanism to strengthen the feature extraction capability for targets with diverse shapes; and (4) incorporating skip connections and weighted fusion in the Neck feature extraction network to enhance multi-scale object detection. Experimental results demonstrate that Yolov8-GCB improves AP@0.5 and AP@0.75 by 1.2% and 1%, respectively, while reducing the number of parameters by 14.1% and the GFLOPs by 16.1% and increasing inference speed by 20.65%. This method provides an effective technological solution for real-time rockfall detection on embedded devices and can be extended to other disaster scenarios, such as landslides and debris flows, in regions with limited infrastructure.

This study is aimed at the complex deformation response characteristics of deep soil in the processes of excavation and support of round sand foundation pits. The displacement of the diaphragm wall during the excavation and support of the foundation pit was monitored in a round foundation pit project at the west anchorage of Humen Second Bridge. A full-cycle model of foundation pit excavation was established, and a new method for parameter checking based on the principle of three-factor (c, φ, E) multi-level orthogonal experimental design was proposed. Dynamic calibration parameters based on construction monitoring data were obtained. The characteristics of the uplift at the bottom of the foundation pit and the effective plastic strain of the soil before and after the steady-state seepage conditions were applied and analyzed. The most dangerous working conditions (Conditions 5–6: construction of the fourth layer of lining and excavation of the fifth layer; construction of the fifth layer of lining and excavation at the sixth layer) existed during the excavation and support of the foundation pit. The research method can provide a great practical guiding significance for covering lack of monitoring data at the construction site and early warning analysis on foundation pit excavation.

As a sort of worldwide renewable energy, wind energy can reduce environmental pollution and relieve the energy shortage. In order to reduce the adverse effect with the integration of wind energy into electricity grids and the operating cost of power supply system, it is becoming increasingly significant to acquire accurate short-term wind speed forecasts. In this paper, based on the analysis of the measured wind speed data in the Donghai Bridge wind farm, we suggest that the short-term wind speed series has volatility clustering effect and asymmetric effect, and the volatility feed-back effect is not significant. And then the possible causes for this phenomenon are elucidated in detail from the viewpoint of physics. In addition, in order to select the forecast model which is appropriate to the Donghai Bridge wind farm, we use these indexes of error: mean absolute error (MAE), mean absolute percentage error (MAPE) and root mean square error (RMSE) as the criterion, and compare the prediction accuracy of the five models by quantitative analysis. The results show that ARMA-EGARCH model and ARMA-EGARCH-M model are very close to each other in both single-step and multi-step forecasting, and they are superior to other models. What’s more, with the increase of the number of advance forecasting steps, error growth rate of these two models is low.