Complex Terrain Conditions Research Articles

Identifying Alpine Lakes with Shoreline Features

Alpine lakes located in high-altitude mountainous regions act as vital sentinels of environmental change. Remote-sensing-based identification of these lakes is crucial for understanding their response to climate variations and for assessing associated disaster risks. However, the complex terrain and weather conditions in these areas pose significant challenges to accurate detection. This paper proposes a method that leverages the high precision of deep learning for small lake and lake boundary extraction combined with deep learning to eliminate noise and errors in the identification results. Using Sentinel-2 data, we accurately identified and delineated alpine lakes in the eastern Himalayas. A total of 2123 lakes were detected, with an average lake area of 0.035 km². Notably, 76% of these lakes had areas smaller than 0.01 km². The slope data is crucial for the lake classification model in eliminating shadow noise. The accuracy of the proposed lake classification model reached 97.7%. In the identification of small alpine lakes, the recognition rate of this method was 96.4%, significantly surpassing that of traditional deep learning approaches. Additionally, this method effectively eliminated most shadow noise present in water body detection results obtained through machine learning techniques.

Development and Evaluation of Machine Learning Models for Air-to-Land Temperature Conversion Using the Newly Established Kunlun Mountain Gradient Observation System

Mountainous land types are characterized by a scarcity of observational data, particularly in remote areas such as the Kunlun Mountains, where conventional Automatic Weather Stations (AWSs) typically do not record land surface temperature (LST) data. This study aims to develop and evaluate models for converting air temperature (TA) to LST using newly established meteorological station data from the Kunlun Mountain Gradient Observation System, thereby providing time-continuous LST data for AWSs. We constructed a conceptual model to explore the relationship between 1.5 m TA and LST and instantiated it using three machine learning algorithms: Support Vector Machine (SVR), Convolutional Neural Network (CNN), and CatBoost. The results demonstrated that the CatBoost algorithm outperformed the others under complex terrain and climatic conditions, achieving a coefficient of determination (R2) of 0.997 and the lowest root mean square error (RMSE) of 0.627 °C, indicating superior robustness and accuracy. Consequently, CatBoost was selected as the optimal model. Additionally, this study analyzed the spatiotemporal distribution characteristics of cloud cover in the Kunlun Mountain region using the MOD11A1 product and assessed the uncertainties introduced by the 8-day average compositing method of the MOD11A2 product. The results revealed significant discrepancies between the monthly average LST derived from polar-orbiting satellites and the hourly composite monthly LST measured on-site or under ideal cloud-free conditions. These differences were particularly pronounced in high-altitude regions (4000 m and above), with the greatest differences occurring in winter, reaching up to 10.2 °C. These findings emphasize the importance of hourly LST calculations based on AWSs for accurately assessing the spatiotemporal characteristics of LST in the Kunlun Mountains, thus providing more precise spatiotemporal support for remote sensing applications in high-altitude regions.

Mesoscale soil moisture measurements along the rover route using the mobile cosmic-ray neutron sensing in the eastern Tibetan Plateau

Water resources in the soil play an essential role in hydrological processes and ecosystem functions on the Tibetan Plateau. However, accurately measuring soil moisture distribution in this region presents challenges due to the diverse ecosystem types, complex terrain, and harsh environmental conditions. In this study, we introduce an approach for estimating mesoscale soil moisture in the Qilian Mountains (QLM) region of the eastern Tibetan Plateau using a cosmic-ray neutron rover. Soil moisture estimates derived from neutron count rates, newly adjusted by vegetation effects, demonstrated good agreement with soil moisture measurements obtained through soil sampling at 26 calibration sites across the region (RMSE = 0.025 g g−1). The calibration parameter N0_NDVI was 443 cpm in the QLM. Utilizing NDVI as vegetation correction method showed potential improvements in the accuracy of converting neutron counts to soil moisture across the diverse mountainous ecosystem types. The newly developed calibration equation provided a high-precision, high spatial resolution soil moisture transect across various landscapes measured by the rover. The average mesoscale soil moisture along the rover route varied by ecosystem types, with values of 0.10 g/g in deserts, 0.17 g/g in grasslands, 0.13 g/g in forests, 0.18 g/g in subalpine shrublands, and 0.20 g/g in croplands. Land cover types emerged as crucial determinants of mesoscale soil moisture variability in the QLM region. These findings offer valuable mesoscale soil moisture data and new insights into soil water information at the transect scale across diverse ecosystem types in the Tibetan Plateau.

Monitoring of Glacier Area Changes in the Ili River Basin during 1992–2020 Based on Google Earth Engine

The Ili River Basin, a crucial transboundary river in the arid region of Central Asia, plays a significant role in the region’s ecology and water resources. However, current methods for monitoring glacier area changes in this region face challenges in automation and accuracy due to the complex terrain and climatic conditions. This study aims to evaluate the effectiveness of the Google Earth Engine (GEE) platform for monitoring glacier area changes in the Ili River Basin from 1992 to 2020, with a focus on improving data accuracy and processing efficiency. Utilizing the Landsat data series, we employed the random forest (RF) classification algorithm within the GEE platform to extract glacier areas, optimizing a multidimensional feature set using the Jeffries–Matusita (JM) distance method, and applied visual interpretation for data refinement. Our results demonstrated that the GEE platform, combined with the RF algorithm, provided high accuracy in glacier monitoring, achieving an overall accuracy of 89% and a kappa coefficient of 0.85. During the study period, the glacier area in the Ili River Basin decreased by 184.76 km2, with an average annual retreat rate of 6.84 km2, most notably between 3800 and 4400 m in elevation. The analysis revealed that temperature changes had a more pronounced impact on glacier dynamics than precipitation. This approach significantly enhances image utilization efficiency and data processing speed, offering a reliable tool for monitoring glacier dynamics. Future research should focus on integrating additional environmental variables and extending the temporal scope to further refine glacier dynamics modeling and predictions.

Verification of the accuracy of Sentinel-1 for DEM extraction error analysis under complex terrain conditions

The successful launch of the Sentinel-1 satellite in 2014 brought a large amount of free SAR images to researchers and scholars, and its application in the fields of ocean monitoring, land use change, natural disaster monitoring and emergency response is becoming increasingly mature and precise. The main applications of InSAR can be categorized into surface deformation monitoring and DEM generation. Sentinel-1 was initially designed for surface deformation monitoring; thus, there are fewer relevant studies on the use of Sentinel-1 data for DEM extraction. However, as the only SAR satellite whose data are currently free and openly available and whose data are constantly updated, it is highly important to study its sources of error in the DEM generation process and the accuracy of its products. In addition, the SAR data provided by the Sentinel-1 satellite has the advantages of high resolution, all-day, all-weather, providing a large data source for DEM production. Taking the Ankang area as an example, this paper analyzes the influence of the InSAR spatiotemporal baseline, ground cover, terrain factors, SAR imaging and other factors on the accuracy of the Sentinel-1-extracted DEM using multisource ground observation data to validate its feasibility for terrain mapping in complex terrain. Finally, we look forward to how to effectively improve the quality of Sentinel-1 DEM products to provide guidance and a reference for subsequent research on DEM extraction using Sentinel-1 SAR images and designation of Sentinel-1 C satellite’s parameters.

Research on the Characteristics and Evolution of Urban Spatial Structures under Complex Terrain Conditions: Taking 71 Typical Cities in China as an Example

Research on the Characteristics and Evolution of Urban Spatial Structures under Complex Terrain Conditions: Taking 71 Typical Cities in China as an Example

Study on Ground Motion Amplification in Upper Arch Bridge Due to “W”-Type Deep Canyon Using Boundary-Integral and Peak Frequency Shift Methods

The study of the dynamic response characteristics of “W”-type deep canyon terrain to double-span concrete arch bridges under earthquake action holds great practical significance. In this research, a bridge in Sichuan Province is taken as the object of study. The boundary-integral equation method and peak frequency shift method are combined to apply an embedded linear time–history analysis algorithm to the finite element spatial dynamic calculation model of the entire bridge, resulting in an improved model. By comparing these two methods with model test results, the seismic response characteristics of the middle part of a “W” concrete arch bridge under different foundation depths and seismic intensities are examined. The boundary integral equation method was utilized to calculate ground motion response at any point on site, revealing a significant amplifying effect of increased seismic wave intensity on acceleration response at the top of the arch bridge. When input seismic wave intensity increased from 0.1 g to 0.3 g, maximum acceleration at buried depths of 3 m and 8 m in the middle of the arch bridge foundation increased by 102.63% and 79.16%, respectively, indicating that shallow buried depth structures are more sensitive to seismic wave intensity. Furthermore, using peak frequency shift rules for analyzing seismic wave propagation characteristics in “W”-type deep canyon topography confirms the sensitivity of shallow buried depth structures to seismic wave intensity and reveals the mechanism through which topography influences seismic wave propagation. This study provides a helpful method for understanding the propagation law and energy distribution characteristics of seismic waves in complex terrain. It was observed that the displacement at the top of the arch bridge increased significantly with an increase in seismic intensity. When subjected to 0.1 g, 0.2 g, and 0.3 g EI-Centro seismic waves, the maximum displacement at the top of the arch bridge model with a foundation buried depth of 3 m was 8 mm, 32 mm, and 142 mm, respectively. For arch bridge models with an 8-m foundation buried depth, these displacements were measured at 6.2 mm, 21 mm, and 68 mm, respectively. The results from model tests verified that increasing the depth of foundation burial effectively reduces the displacement at the top of the structure. Furthermore, by combining a boundary-integral equation method and peak-frequency shift method, this study accurately predicted significant influences on W-shaped double deep canyon topography from seismic response, and successfully captured stress concentration and seismic wave amplification/focusing effects on arch foot structures. The calculated results from both methods align well with model test data which confirm their effectiveness and complementarity when analyzing seismic responses under complex terrain conditions for bridge structures.

Numerical Simulation of Terrain-Adaptive Wind Field Model Under Complex Terrain Conditions

Numerical Simulation of Terrain-Adaptive Wind Field Model Under Complex Terrain Conditions

Design of an Adaptive Height Control System for Sugarcane Harvester Header

This study addresses the issue of low control accuracy and harvesting efficiency resulting from the manual adjustment of the header height during the sugarcane harvesting process in hilly and mountainous regions. An adaptive header height adjustment system was designed and implemented. A test bench for the sugarcane harvester header was designed and constructed, incorporating a LiDAR to measure the ground height at the sugarcane growth point in front, and a draw-wire displacement sensor to monitor the real-time height of the header. I/O ports were allocated, and the control program was developed in the TIA Portal environment. The PLC control system achieves the precise adjustment of the cutting height based on the collected data. The experimental results indicate that the system can quickly respond and adjust the cutting height under complex terrain conditions. When the cutting height into the soil is 0 mm, the adaptive control system’s average cutting height error is 0.28 cm, and the average response time is 2.3 s. When the cutting depth into the soil is 2 cm, the average cutting height error is 0.21 cm, and the average response time is 2.31 s.

An Integrated Route and Path Planning Strategy for Skid–Steer Mobile Robots in Assisted Harvesting Tasks with Terrain Traversability Constraints

This article presents a combined route and path planning strategy to guide Skid–Steer Mobile Robots (SSMRs) in scheduled harvest tasks within expansive crop rows with complex terrain conditions. The proposed strategy integrates: (i) a global planning algorithm based on the Traveling Salesman Problem under the Capacitated Vehicle Routing approach and Optimization Routing (OR-tools from Google) to prioritize harvesting positions by minimum path length, unexplored harvest points, and vehicle payload capacity; and (ii) a local planning strategy using Informed Rapidly-exploring Random Tree (IRRT*) to coordinate scheduled harvesting points while avoiding low-traction terrain obstacles. The global approach generates an ordered queue of harvesting locations, maximizing the crop yield in a workspace map. In the second stage, the IRRT* planner avoids potential obstacles, including farm layout and slippery terrain. The path planning scheme incorporates a traversability model and a motion model of SSMRs to meet kinematic constraints. Experimental results in a generic fruit orchard demonstrate the effectiveness of the proposed strategy. In particular, the IRRT* algorithm outperformed RRT and RRT* with 96.1% and 97.6% smoother paths, respectively. The IRRT* also showed improved navigation efficiency, avoiding obstacles and slippage zones, making it suitable for precision agriculture.

Research on intelligent monitoring technology for roof damage of traditional Chinese residential buildings based on improved YOLOv8: taking ancient villages in southern Fujian as an example

In the process of preserving historical buildings in southern Fujian, China, it is crucial to provide timely and accurate statistical data to classify the damage of traditional buildings. In this study, a method based on the improved YOLOv8 neural network is proposed to select aerial photographs of six villages in Xiamen and Quanzhou cities in Fujian Province as the dataset, which contains a total of 3124 photographs. Based on the high-resolution orthophotographs obtained from UAV tilt photography, the YOLOv8 model was used to make predictions. The main task in the first stage is to select the buildings with historical value in the area, and the model's mAP (Mean Accuracy Rate) can reach 97.2% in the first stage task. The second stage uses the YOLOv8 model to segment the images selected in the first stage, detecting possible defects on the roofs, including collapses, missing tiles, unsuitable architectural additions, and vegetation encroachment. In the second stage of the segmentation task, the mAP reaches 89.4%, which is a 1.5% improvement in mAP50 (mean accuracy) compared to the original YOLOv8 model, and the number of parameters and GFLOPs are reduced by 22% and 15%, respectively. This method can effectively improve the disease detection efficiency of historical built heritage in southern Fujian under complex terrain and ground conditions.

PRISM and Radar Estimation for Precipitation (PREP): PRISM enhancement through ANN and radar data integration in complex terrain

Site-specific estimates of precipitation can be used to assess crop productivity and identify areas vulnerable to crop damages caused by extreme weather events such as droughts and floods. Spatial interpolation of precipitation such as Parameter-elevation Regressions on Independent Slopes Model (PRISM) has been used to estimate precipitation in an area of interest. However, the reliability of spatial interpolation is often affected by the availability of precipitation measurements from weather stations in a given region especially under complex terrain conditions. Here we propose an alternative approach for site-specific estimation of precipitation using both radar reflectivity data and topographic features. At first, radar reflectivity data are used as inputs to an artificial neural network (ANN) for estimation of precipitation. These radar precipitations at each grid cell are used to represent the observations at virtual weather stations for spatial interpolation using PRISM. Furthermore, the radar precipitations are compared with the observations at actual weather stations for their bias correction. This approach is referred to as PRISM and Radar Estimation for Precipitation (PREP). A case study was conducted in Jeollabuk-do, South Korea to compare the degree of agreement between PREP and PRISM. It was found that PREP had higher degree of agreement for the daily estimates of precipitation than PRISM in the given region with a complex terrain including coast and mountains. For example, the root mean square error (RMSE) of precipitation estimates for PREP was 22.1% less than that for PRISM in 2020. PREP also had greater value of the critical success index (CSI) than PRISM especially under heavy precipitation events, e.g.,>180 mm, and no rainfall conditions. These findings indicate that the PREP would improve the reliability of site-specific estimates of precipitation, which would facilitate decision-making in agriculture and early warning of extreme weather events.

Exploring the effect of mountainous terrain on weather-related crashes

Mountainous areas pose unique challenges to transportation safety, with their complex terrain and harsh weather conditions. The presence of steep terrain can lead to winding roads with a high degree of azimuth change per mile, thereby increasing the potential for hazardous driving conditions. Additionally, steep terrain is often associated with fog formation, which can obscure roads by clouds. Thus, there is a need to study weather-related crashes and identify associated risk factors in mountainous areas. To address this issue, the Tobit, latent class Tobit (LCT), and two-part truncated log normal (TPTLN) models were employed to investigate the potential interactions between topography and weather-related crash rates, while accounting for the censored nature of crash data. Data from the western region of North Carolina, which is home to numerous mountains experiencing a wide range of weather conditions was used for this purpose. This region comprises a 3570-mile road network across seven cities, with elevations ranging from 1000 ft. to >6158 ft. above mean sea level. Crash data from 2015 to 2017 was obtained from the Highway Safety Information System (HSIS). The results revealed that topography significantly affect weather-related crashes in mountainous areas. Higher elevations and steeper slopes are associated with lower crash rates. Also, the study highlights the importance of considering topography when assessing transportation safety in mountainous areas. The findings help develop transportation safety policies and interventions aimed at improving safety in these areas.

Study on the mechanical model of footpad-terrain for walking robot moving in low gravity environment

Due to the low gravity environment and the influence of complex terrain condition in deep space exploration, wheeled mobile systems are prone to meet motion abnormalities. The excellent motion performance of walking robot is more suitable for the future deep space exploration, but the robots are prone to occur large sinkage in soft terrain. A mechanical model is built to describe a gait cycle of a walking robot under soft terrain and low gravity environment. The force on the footpad during actual movement in a gait cycle is obtained through a single-legged test bench under the simulated planet terrain. The effects of sizes of footpads, sinkage and other factors are explored. The results indicate that the larger the size of the footpad, the greater the horizontal force on the footpad, the better the motion performance is. But as the size of footpad increase, the vertical force decreases which indicates poor support performance. By comparing and analyzing the model values with the experimental values, for the horizontal force FT, the average errors for the average force and peak force are 10.05% and 7.76%. The average errors for average force and peak force are 5.19% and 5.86% for vertical force FN. The values are not significantly different from the model values and experimental values which indicates that the mechanical model has high accuracy. The obtained mechanical model can provide a reference for the motion of walking robots in complex low gravity environment.

A Real-Time Sinkage Detection Method for the Planetary Robotic Wheel-on-Limb System via a Monocular Camera

When traversing soft and rugged terrain, a planetary rover is susceptible to slipping and sinking, which impedes its movement. The real-time detection of wheel sinkage in the planetary wheel-on-limb system is crucial for enhancing motion safety and passability on such terrain. Initially, this study establishes a measurement of wheel sinkage under complex terrain conditions. Subsequently, a monocular vision-based wheel sinkage detection method is presented by combining the wheel–terrain boundary with the wheel center position (WTB-WCP). The method enables the efficient and accurate detection of wheel sinkage through two-stage parallel computation of the wheel–terrain boundary fitting and wheel center localization. Finally, this study establishes an experimental platform based on a monocular camera and the planetary rover wheel-on-limb system to experimentally validate and comparatively analyze the proposed method. The experimental results demonstrate that the method effectively provides information on the wheel sinkage of the planetary rover wheel-on-limb system, and the relative errors of the method do not exceed 4%. The method has high accuracy and reliability and is greatly significant for the safety and passability of planetary rovers in soft and rugged terrain.

Source reduction and end treatment of acid mine drainage in closed coal mines of the Yudong River Basin.

After the closure of the Yudong coal mine, the pH value was approximately 3.0, and the Fe and Mn concentrations reached 380 and 69 mg/L, respectively, in the acid mine drainage (AMD), causing serious pollution to the water bodies in the nearby watershed. Combined with the formation conditions of AMD, the comprehensive treatment technology of source reduction-end treatment is adopted to treat the AMD. The treatment area of the goaf is 0.3 km3, the filling and grouting volume is about 6.7 m3, and the curtain grouting volume is 4,000 m3. Through the grouting and sealing treatment in the goaf, the water volume is reduced to less than 85% of the initial volume (100 m3/h). After the end treatment, the pH value of the effluent is around 7.0, the content of Fe and Mn is less than 0.1 mg/L, and the removal rate is above 99%. The project was subsequently operated at RMB 0.85 yuan/t. This project is aimed at the treatment of AMD from small coal mines in complex terrain conditions. It has the characteristics of low cost and high efficiency and can provide an effective treatment technology for AMD in southwestern China and areas with the same geological conditions.

Study on the Driving Risk Reduction in the Mountain Highway Tunnel Group under the Perspective of Visual Load

More highway built in the mountains in recent years, the driving risk in the tunnel group is becoming a new issue. This paper analyzed the driving risk in the mountain highway tunnel group from the perspective of visual load. Based on vehicle test in the Pengshui‐Xiantang tunnel group in China, the evolution characteristics of MTPA were quantitatively analyzed, and the random forest model was constructed to discuss the effect factors of the maximum transient velocity value of the pupil area (MTPA) in different sections. The results are as follows: (1) The MTPA frequently presents a tendency of steep rise and fall in the tunnel group. MTPA in the second tunnel is significantly higher than the first tunnel. (2) The mountain tunnel group can be divided into nine sections; the velocity, design luminance, measured luminance, and location have different effects on MTPA in each section. Due to the complex terrain conditions, the location has a more significant impact on MTPA in the second tunnel. (3) The first tunnel entrance, the first tunnel exit to the second tunnel entrance, and the second tunnel exit are the areas with more significant pressure on drivers in the tunnel group. The visual load of drivers in the exit section of the last tunnel is the greatest. The driving risk reduction recommendations include improving the transition lighting design of the second tunnel, clarifying the tunnel group identification, and adding safety features at the tunnel connection section, in order to clarify the driver’s expectations and reduce the fear of the unknown mountain environment.

Detection and analysis of potential landslides based on SBAS-InSAR technology in alpine canyon region.

The Lancang River flows through the alpine canyon region of southwest China, an area that has experienced frequent geological disasters over the years. Early monitoring of geological hazards is essential for disaster prevention and mitigation. However, traditional ground monitoring techniques are limited by the complex terrain conditions in high-altitude valley regions. In contrast, interferometric synthetic aperture radar (InSAR) technology can provide a high-precision, wide-range monitoring of slow rock-slope deformation, making it an effective tool for studying geological hazards. Within the study area, multiple synthetic aperture radar (SAR) images from the Sentinel-1A satellite were collected, and surface deformation was obtained using the small baseline subset InSAR (SBAS-InSAR). The results demonstrate that combining ascending and descending orbit images can be successfully applied to landslide monitoring in complex mountainous areas. Over 30 potential landslides were identified by combining InSAR results with optical images. The Line-Of-Sight (LOS) direction deformation features and their relationship with precipitation were analyzed based on two typical landslides, and two-dimensional/three-dimensional (2D/3D) deformation decomposition was carried out to reveal its motion characteristics. It was found that the cumulative deformation fluctuation amplitude was higher during the rainy season, and the main movement direction of the landslide was east-west. In addition, based on the spatial distribution and statistical analysis of deformation points along with meteorological data, geological elements, human activities, and topographic conditions, it is inferred that factors such as low vegetation coverage, tectonic movements, human activities, and high-altitude glacier thawing may contribute to the occurrence of disasters. And it was found that areas with high vegetation cover, high rainfall, and snow cover exhibit lower coherence coefficients. This study offers valuable insights for investigating large-scale geological in alpine canyon regions.

Rapid Land Cover Classification Using a 36-Year Time Series of Multi-Source Remote Sensing Data

Long time series land cover classification information is the basis for scientific research on urban sprawls, vegetation change, and the carbon cycle. The rapid development of cloud computing platforms such as the Google Earth Engine (GEE) and access to multi-source satellite imagery from Landsat and Sentinel-2 enables the application of machine learning algorithms for image classification. Here, we used the random forest algorithm to quickly achieve a time series land cover classification at different scales based on the fixed land classification sample points selected from images acquired in 2022, and the year-by-year spectral differences of the sample points. The classification accuracy was enhanced by using multi-source remote sensing data, such as synthetic aperture radar (SAR) and digital elevation model (DEM) data. The results showed that: (i) the maximum difference (threshold) of the sample points without land class change, determined by counting the sample points of each band of the Landsat time series from 1986 to 2022, was 0.25; (ii) the kappa coefficient and observed accuracy of the same sensor from Landsat 8 are higher than the results of the TM and ETM+ sensor data from 2013 to 2022; and (iii) the addition of a mining land cover type increases the kappa coefficient and overall accuracy mean values of the Sentinel 2 image classification for a complex mining and forest area. Among the land classifications via multi-source remote sensing, the combined variables of Spectral band + Index + Terrain + SAR result in the highest accuracy, but the overall improvement is limited. The method proposed is applicable to remotely sensed images at different scales and the use of sensors under complex terrain conditions. The use of the GEE cloud computing platform enabled the rapid analysis of remotely sensed data to produce land cover maps with high accuracy and a long time series.

Research on the design of ballastless track in the subgrade section of high-speed railway in high earthquake-intensity area

According to the characteristics of complex terrain and bad geological conditions in the southwest mountainous area of China, it is proposed that cast-in situ double-block ballastless track with layers and blocks structure should be adopted preferentially in the subgrade section of high-speed railway, which is conducive to the construction, prolongation of service life and maintenance of the ballastless track. Based on the finite element model, the dynamic performance, structural strength and stability of double-block ballastless track under high earthquake-intensity action are analyzed. The analysis shows that the relative displacement between the base slab of ballastless track and the subgrade may occur under 9 degree earthquake action. A new CRTS double-block ballastless track structure with a concave-convex structure between the base slab and the subgrade is proposed in the subgrade section, and its additional stress and relative displacement under earthquake are analyzed. The results show that the additional stress and relative displacement of the new ballastless track structure and the subgrade under 9- degree earthquake actions are small, which meet the high stability requirements of high-speed railway.