Sichuan-Tibet Highway Research Articles

Remote Sensing Identification and Information Extraction Method of Glacial Debris Flow Based on Texture Variation Characteristics

The formation mechanism of glacial debris flows in alpine gorge mountain areas is complex, with varying characteristics across different regions. Due to the influence of mountain shadows and the accumulation and ablation of ice and snow, accurately identifying and rapidly extracting glacial debris flows using optical images remains challenging. This study utilizes the Random Forest method to develop a multi-feature spatiotemporal information extraction model based on Landsat-8 images and a glacial debris flow gully identification model. These models were applied to the Songzong–Tongmai section of the Sichuan–Tibet Highway to identify glacial debris flows. The results showed that (1) the multi-feature spatiotemporal extraction model effectively eliminated the interference of mountain shadows and ice–snow phase changes, resulting in a higher accuracy for identifying and extracting glacial debris flows in areas with significant information loss due to deep shadows. The total accuracy was 93.6%, which was 8.9% and 4.2% higher than that of the Neural Network and Support Vector Machine methods, respectively. (2) The accuracy of the glacial debris flow gully identification model achieved 92.6%. The proposed method can accurately and rapidly identify glacial debris flows in alpine gorge mountain areas, facilitating remote sensing dynamic monitoring. This approach reduces the damage caused by debris flows to both transportation and the environment, ensuring the safe passage of highways and promoting the sustainable development of the region.

Grading of Traffic Interruptions in Highways to Tibet Based on the Entropy Weight-TOPSIS Method and Fuzzy C-Means Clustering Algorithm

The interruption of transportation on the way to Tibet has brought great losses to the Tibetan region. The work proposed a model that integrated the entropy weight-TOPSIS method with the fuzzy C-means clustering algorithm to discuss the causes and characteristics of traffic interruptions on the four main highways to Tibet. This approach aimed to quantify and grade traffic interruption states. First, the entropy weight-TOPSIS method was used to mitigate dimensions among various indices and quantitatively evaluate the status values of traffic interruptions. Then, the fuzzy C-means clustering algorithm was employed to grade these values. The proposed model graded traffic interruption states into four levels by evaluating the duration, mileage, and severity of traffic interruptions. Moreover, the four-level classification scheme can reflect the severity of traffic blocking events more precisely while maintaining a lower PE (Partition Entropy) value. In the four-level classification, the Sichuan–Tibet Highway and Xinjiang–Tibet Highway experienced more level-3 and level-4 serious interruptions, while most high-level interruptions on the Qinghai–Tibet Highway were classified as level-2 ordinary interruptions. The Yunnan–Tibet Highway, with limited data and primarily level-1 classification, was not analyzed in detail. These findings provide a reference for highway management departments to formulate targeted maintenance and emergency measures, especially the Sichuan–Tibet highway, which needs more attention and resource investment to improve its disaster resistance and reduce the impact of traffic interruptions.

Research on the Markov-Chain State Interval Division Based on Predicted Data Correction

By 2022, the total length of roads in Tibet Autonomous Region reached 121,447 kilometers. Due to the unique geological conditions in Tibet, various natural disasters such as earthquakes, mudslides, landslides, avalanches, and strong winds frequently occur. Along the Sichuan-Tibet Highway alone, over 300 disasters happen each year, significantly impacting the region’s economic development. This study focuses on the complexity and randomness of natural disaster mechanisms and combines Markov chain theory to improve the accuracy of prediction data for mudslides, landslides, and earth subsidence etc. The main method is to modify the state interval of the prediction model parameter-Markov chain based on the distribution of discrete points on the number axis. The following state interval division methods are proposed: (1) If the relative error of the predicted value exceeds 50%, adjust the prediction model. (2) Obtain the lower bound of state E1 by taking the floor value downward. (3) The width of each interval does not need to be uniform. (4) Arrange continuous, dense, and close points on the number line in the same side in batches, and represent a state continuously, dividing it into one suitable interval or batches. Using this method, an improved RMSE of 0.28mm and MAPE of 0.87% were obtained for engineering examples, outperforming other models such as GM(1,1), Verhulst, DGM(2,1) with corresponding RMSE values of 0.86 mm, 0.69 mm, and 1.38 mm, and MAPE values of 2.75%, 2.53%, and 5.99%. The combined prediction results for five sets of data yielded an RMSE of 0.14 mm and MAPE of 0.56%, which are quite close to the results obtained using Markov selection correction with an RMSE of 0.37 mm and MAPE of 1.01%. Furthermore, comparing the four sets of case, the average reduction in RMSE and MAPE is 3.56mm and 1.72%, respectively, demonstrating that this method can further improve the performance of Markov chain prediction.

A Hybrid Random Forest and Least Squares Support Vector Machine Model Based on Particle Swarm Optimization Algorithm for Slope Stability Prediction: A Case Study in Sichuan–Tibet Highway, China

Slope stability estimation is an engineering problem that involves several parameters. The problems of low accuracy of the model and blind data preprocessing are commonly existent in slope stability prediction research. To address these problems, 10 quantitative indicators are selected from 135 field cases to improve the accuracy of the model. These indicators were analyzed and visualized to examine their reliabilities after preprocessing. Combined with random forest (RF), particle swarm optimization (PSO), and least squares support vector machine (LSSVM) algorithms, a hybrid prediction model that the RF–PSO–LSSVM model is proposed for identifying slope stability, and its reliability is verified by other prediction models that SVM, logistic regression, decision trees, k-nearest neighbor, naive Bayes, and linear discriminant analysis. Besides, the importance score of each indicator in the prediction of slope stability is discussed by employing the RF algorithm. The research results show that the proposed hybrid model exhibits the best accuracy and superiority in slope stability prediction than other models in this paper, which its values of the best fitness, area under the curve, T-measure, and accuracy are 98.15%, 96.4%, 96.55%, and 95.82%, respectively. The most influential factors affecting slope stability are precipitation and gravity, and the slope type and pore water ratio are identified as the least significant factors in this paper. The results provide a novel approach toward slope stability prediction in the field of geotechnical engineering.

Susceptibility Analysis of Glacier Debris Flow Based on Remote Sensing Imagery and Deep Learning: A Case Study along the G318 Linzhi Section.

Glacial debris flow is a common natural disaster, and its frequency has been increasing in recent years due to the continuous retreat of glaciers caused by global warming. To reduce the damage caused by glacial debris flows to human and physical properties, glacier susceptibility assessment analysis is needed. Most research efforts consider the effect of existing glacier area and ignore the effect of glacier ablation volume change. In this paper, we consider the impact of glacier ablation volume change to investigate the susceptibility of glacial debris flow. The susceptibility to mudslide was evaluated by taking the glacial mudslide-prone ditch of G318 Linzhi section of Sichuan-Tibet Highway as the research object. First, by using a simple band ratio method with manual correction, we produced a glacial mudslide remote sensing image dataset, and second, we proposed a deep-learning-based approach using a weight-optimized glacial mudslide semantic segmentation model for accurately and automatically mapping the boundaries of complex glacial mudslide-covered remote sensing images. Then, we calculated the ablation volume by the change in glacier elevation and ablation area from 2015 to 2020. Finally, glacial debris flow susceptibility was evaluated based on the entropy weight method and Topsis method with glacial melt volume in different watersheds as the main factor. The research results of this paper show that most of the evaluation indices of the model are above 90%, indicating that the model is reasonable for glacier boundary extraction, and remote sensing images and deep learning techniques can effectively assess the glacial debris flow susceptibility and provide support for future glacial debris flow disaster prevention.

Sliding Friction and Wear Behavior of Flash-Welded U75V Steel Joints in Air and Water Environments

U75V high-strength steel has been widely used to improve the service life of high-speed rails. Considering the consecutive cold and wet condition of the Sichuan–Tibet highway, water plays an important role in the wear process of U75V welded joints. In this study, we investigated the wear behaviors of flash-butt-welded U75V joints in dry air and water environments using a ball-on-disk sliding wear tester. The worn surfaces were tested using optical microscopy, scanning electron microscopy, and energy dispersive X-ray spectroscopy. The cross-sectional profiles of the wear scar were tested using a wear volume tester. The results show that the U75V welded joint has a lower wear resistance than the base metal owing to its nonuniform structure and exhibits a high friction coefficient (0.64) and wear loss (0.21 mm3) in dry air. Water exerts a lubricating function on the fretting wear of U75V welded joints and reduces the friction coefficient (0.36) and wear loss (0.15 mm3). Furthermore, abrasive wear and micropitting are predominant in water environment, accompanied by the formation of a protective-oxide layer on the surface. In contrast, microcutting and microcracking are the dominant wear mechanisms of U75V welded joints in dry air.

Risk assessment of debris flow along the northern line of the Sichuan-Tibet highway

Debris flow occurs frequently in mountainous areas due to the special geographical and geological environment, causing significant damage to linear infrastructure. However, a systematic assessment of debris flow risk to the national highway is limited by the lack of regional vulnerability data. Based on a detailed inventory of debris flow and highway structure characteristics, this study presents a comprehensive regional risk assessment framework on the northern line of the Sichuan-Tibet Highway in China. The proposed framework consists of three main procedures: (1) debris flow hazard analyses by the weight-of-evidence method, (2) analyses of physical vulnerability using 12 vulnerability indexes, and (3) risk calculation which considers the natural attributes of debris flow and the social characteristics of a linear highway. The predictive performance was evaluated through receiver operating characteristic (ROC) curves, and the validation results showed that areas under the ROC curves were 83.77%. The risk zoning map indicated that the high-risk areas were particularly distributed in Dege County and Daofu County, with a high disaster frequency and a low highway protection standard. The regional risk assessment framework supports decision-making for disaster prevention and highway rehabilitation in mountainous areas.

Generating accurate negative samples for landslide susceptibility mapping: A combined self-organizing-map and one-class SVM method

The accuracy of data-driven landslide susceptibility mapping (LSM) is closely affected by the quality of non-landslide samples. This research proposes a method combining a self-organizing-map (SOM) and a one-class SVM (SOM-OCSVM) to generate more reasonable non-landslide samples. We designed two steps: first, a random selection, a SOM network, a one class SVM model, and a SOM-OCSVM model were used to generate non-landslide sample datasets. Second, four machine learning models (MLs)—namely logistic regression (LRG), multilayer perceptron (MLP), support vector machine (SVM), and random forest (RF)—were used to verify the effects of four non-landslide sample datasets on LSM. From the region along the Sichuan-Tibet Highway, we selected 11 conditioning factors and 1186 investigated landslides to perform the aforementioned experiments. The results show that the SOM-OCSVM method achieves the highest AUC (>0.94) and minimum standard deviation (<0.081) compared with other methods. Moreover, RF achieves the best performance in different datasets compared with other ML models. The aforementioned results prove that the proposed method can enhance the performance of ML models to produce more reliable LSM.

Dynamic numerical simulation and risk predictive assessment of the slope debris flow for the rear mountain at the management office of the Erlang Mountain Tunnel

The Erlang Mountain Tunnel Management Office is located in Luding County, Sichuan Province, China. A long-term open-pit limestone mine is located on the rear mountain, 1 km from the west entrance of the Erlang Mountain Tunnel Management Office for the Sichuan-Tibet Highway. Dangerous rock masses and a large accumulation of mine waste slag are present o-n the hillside, which can easily produce slope debris flow disasters. This paper analyzes the formation causes of slope debris flow through field investigation and uses RAMMS (Rapid mass movement simulation) software to study the influence of base friction coefficient μ and ξ on slope debris flow. Numerical simulation predicted level of danger of the movement process from the aspects of Velocity, deposition height, flow, topography. When the dry Coulomb friction value μ increased from 0.3 to 0.4, the debris velocity decreased and began to spread out along the slope. The flow process can be divided into four parts, and found that the velocity and discharge are different in the upstream and downstream of the slope constriction. The slope constriction has a significant amplification effect on the velocity and discharge. The velocity is amplified by 31.1%, and the discharge is amplified by 14.5%. In addition, based on the dynamic characteristics and the frequency of rainstorms, the risk of debris flow is divided into four levels: low, medium, high, and extremely high. The hazard map of slope debris flow in the rainstorm return period (20 years) is established, which provides a basis for the assessment and prediction of debris flow.

Numerical Simulation and Hazard Analysis of Debris Flows in Guxiang Gully, Tibet, China

Guxiang Gully, located in Bome county in southwest Tibet, China, is a right-bank tributary of the Purlung Tsangpo River. Hanging glaciers are widely distributed upstream of the gully, and a large number of moraines can cause debris flows triggered by run-offs generated by the rainstorm and melting water of glaciers deposited in this gully. The debris flow in the Guxiang Gully can frequently pose a serious threat to the Sichuan–Tibet highway. Due to the lack of field observation data, in this study, the flood discharge method combining the run-off generated by rainstorms and melting water of glaciers was employed to determine the magnitudes of debris flows under once-in-a-century, once-in-two-century, and once-in-three-century flood return periods. Furthermore, a numerical simulation is implemented to determine the maximum flow depth and velocity of the debris flow in each grid cell and the inundated debris flow area in Guxiang Gully with different return periods. Subsequently, each grid cell’s maximum flow depth and velocity are used to assess buried hazards and impact hazards, respectively. The integrated hazard could be calculated by combining the buried hazard and the impact hazard. The result shows that the hazard of the top of the fan and Purlung Tsangpo River affected by the debris flow is highest, and the debris flow is likely to block the Purlung Tsangpo River.

Study on Dynamic Stability Prediction Model of Slope in Eastern Tibet Section of Sichuan-Tibet Highway

The stability of the slope along the middle section of Tibet controls the safety and smoothness of the Sichuan-Tibet highway, which is affected by multiple and uncertain factors such as rainfall. The slope dynamic stability is evaluated to the benefit of that salvager can prepare in advance and preserve timely and accurately. Therefore, engineering treatment scheme in different batches, stages, and grades can be proposed prospectively. Random Forest algorithm was used to rank 10 primary factors: precipitation, earthquake, human factors, groundwater, slope height, slope gradient, dense degree, weathering depth, vegetation, and slope shape. Considering precipitation and earthquake as dynamic factors, a wavelet and NARX dynamic neural network were used to predict the trend and quantity of precipitation and earthquake, followed by developing a dynamic stability evaluation model by combining a fuzzy neural network model with other indexes. Results show that (1) the superposition error in rainfall and earthquake prediction is 0.21%, proving that the ranking of influencing factors is reasonable, and (2) the back-judgment and test accuracy of the dynamic evaluation model are 93.98% and 91.67%, respectively, indicating that the model is accurate and applicable. The model can evaluate the dynamic stability of slopes and provide more reasonable engineering protection countermeasures so that Highway Public Works Department can deal with emergencies and disasters timely and precisely.

GIS-based spatial prediction of landslide using road factors and random forest for Sichuan-Tibet Highway

GIS-based spatial prediction of landslide using road factors and random forest for Sichuan-Tibet Highway

Shear creep behavior of red sandstone after freeze-thaw cycles considering different temperature ranges

A strong freeze-thaw (F-T) weathering effect influences the strength of rock in the high-altitude areas of the Qinghai-Tibet Plateau. To study the long-term deterioration characteristics of rock in an alpine F-T environment, shear creep experiments were conducted on red sandstone from the Sichuan-Tibet highway to analyze its shear creep characteristics under different temperature ranges. The results show that the rock’s stress level gradually decreases with increasing temperature under the same number of F-T cycles, while the creep displacement gradually increases. The long-term strength and long-term reduction factor present apparent decreasing trends with an increase in the F-T temperature range. The failure morphology evolves from shear failure with a single horizontal plane to that with multiple dipping shear planes. The greater the temperature range is, the greater the number of shear secondary cracks that intersect, resulting in more complicated failure morphologies. Then, a Burgers model considering F-T and time-dependent damage was established. The parameters of this model were identified via the experimental creep data of the red sandstone. The accuracy and applicability of the model were verified by comparing the experimental and model-predicted results. These results are significant for the construction of major engineering projects in the alpine region.

Safety risk Assessment for Construction of Highway on Plateau Mountainous

Under the condition of high altitude and complicated geological environment, lots of risks existing in the process of highway construction, how to control effectively safety risk has become the serious problem. Taking the construction project of Sichuan-Tibet highway as an example, combining with regional environment, safety risk indicators of construction project for plateau mountain highway is constructed. Method of construction safety risk assessment for plateau mountain highway is established. The feasibility of the evaluation method is verified by a case, and corresponding risk control measures are put forward.

Visual representation of a linear tourist destination based on social network photos: a comparative analysis of cross-cultural perspectives

ABSTRACT With the development of social networks, user-generated data now have a substantial impact on the visual representation of tourist destinations. As ‘the Belt and Road’ has become China’s long-term national development strategy, linear tourist destinations have gained increasing attention. Due to the peculiarities of the spatial form of linear tourist destinations and the mobile character of dynamic viewing, which make communication and collision between cultures more extensive and deeper, it is valuable to study visual representations of linear tourist destinations from a cross-cultural perspective. This study proposes a mixed methods based on RQDA and mutual information to explore the visual representations of linear tourist destination by Chinese and foreign tourists, sampling photos of the Sichuan Tibet Highway found on social networks. The results show that linear tourism destinations have unique visual representation characteristics due to their own spatial structure and changes in travel modes and tools. There are both similarities and differences in the representations of linear tourist destinations by tourists from different cultural backgrounds. On the one hand, common elements of visual representations by Chinese and foreign tourists are highlighted in the cross-cultural context; on the other hand, visual representation embeds personal spirit, cultural background, and ideology, forming a relatively subjective place image of the linear tourist destination.

Risk assessment of Sichuan–Tibet Highway susceptible to debris flows at Suotong Basin, southeastern Tibet

The planned Sichuan–Tibet Highway will pass through many debris flow-prone areas. Suotong catchment with a drainage area of 38.03 km2, a tributary of Parlung Zangbo River in the Bomi County of Tibet, has not experienced large-scale debris flows since 1991. Field survey and image interpretation show that glacial till in the catchment has volume of approximately 160 mm3 and is the primary source of loose materials for debris-flow reoccurrence. Once large glacial debris flows occur, they will cause harm to the highway. A comprehensive method including engineering geology, hydraulics, and computational mathematics is developed to quantitatively assess the debris-flow risk to the highway. The magnitude and peak discharge of glacial debris flows of a 100 year return period are calculated using empirical relationships in this region. The peak velocity at the outlet can reach 6.75 m/s, and the peak discharge is 6238 m3/s for such a return period. The Saint–Venant equations governing debris-flow movements are numerically solved by finite difference method. The simulated results are close to those by empirical methods. The hazard value at the alluvial fan is as high as 24.7 according to the numerical simulation. Two routing schemes (one goes through the tunnel and other pass the bridge) are compared with each other. Although the bridge scheme is cheaper than the tunnel scheme, the bridge’s section cannot fully discharge debris flows within the 100-year return period. Therefore, the tunnel scheme is safer and more strongly recommended than the bridge scheme.

Travellers’ meaning-making of the Sichuan-Tibet highway: from space of flows to place

The relationship between mobility and place-making in the context of tourism based on the new mobilities paradigm has been explored. Taking road travel on the Sichuan-Tibet Highway in southwestern China as a case, auto-ethnography and mobile ethnography are employed as the primary research methods, along with in-depth interviews, observations, personal travel log writings, and second-hand data collection. The question that how the space of road becomes a meaningful place through the mobility of road travellers thus has been answered. The mobility pattern of travellers is found to be stable in the time dimension and there are periodic repetitions in the spatial dimension, making ‘place-ballet’ on the highway. Accordingly, the road changes from space of flows into a place that is richly endowed with meaning. The travellers’ imagination of the road plays an important role in their interactions with the road, helping them making the meaning of the road. Multiple mobility practices shape the traveller’s ‘sense of the road’. In particular, the road is not a homogenous space in motion, but a ‘wrinkled’ one with numerous anchoring points. These moorings such as restaurants, hostels and landmarks shape travellers’ emotional connection with the road. Proactive or forced stillness not only affect the pace of the traveller’s mobility, but also broadens their understanding of the road and road travel. Therefore, the meaning attributed to the road is constructed by the mobility and the moorings. Accordingly, a comprehensive understanding of road travel, which is a distinct form of mobility practice, is generated. Moreover, the understanding of roads as a space of flows has been deepened and the significance of travel channels within mobility studies is highlighted. Finally, roads are regarded as a meaning place, which contributes to forming a ‘mobile concept’ of the place.

Impacts of geological conditions on instability causes and mechanical behavior of large-scale tunnels: a case study from the Sichuan–Tibet highway, China

Tunneling in complicated geological conditions frequently encounters problems with squeezing ground, large ground deformation, rockbursts, asymmetric deformation, non-linear rheology of surrounding rock, water inrush, and failure of supporting structures. In this paper, the Sichuan–Tibet highway, which is located at the north-west edge of the Sichuan basin and the eastern margin of the Qinghai–Tibet plateau, serves as a case study to investigate potential hazard sources for tunnels constructed in complex and difficult mountainous areas. Consideration first is given to the following 5 sets of conditions: crossing a fault zone, shallow tunnels in soft rock, crossing a landslide deposit, and deep tunnels in hard and soft rock formations. A program of field tests was performed to study the changes in rock pressure and the performance of support structures in different geological conditions. The results confirmed that the squeezing of rock depended upon a combination of the surrounding rock strength and the in situ stress field; fracture zones and water conditions reduced the integrity and mechanical properties of the surrounding rocks; topography and bedding structure can induce asymmetric mechanical behavior of the supporting structures during construction. Meanwhile, time-dependent rock deformation may have greater adverse impacts on the structural safety performance of tunnel during their operation.

Highway Planning and Design in the Qinghai–Tibet Plateau of China: A Cost–Safety Balance Perspective

Abstract Engineering designs for mountainous highways emphasize compliance checking to ensure safety. However, relying solely on compliance checking may lead designers to minimize costs at the expense of high risk indicators, since the overall risk level of the highway design is unknown to the designers. This paper describes a method for the simultaneous consideration of traffic safety risks and the associated cost burden related to the appropriate planning and design of a mountainous highway. The method can be carried out in four steps: First, the highway design is represented by a new parametric framework to extract the key design variables that affect not only the life-cycle cost but also the operational safety. Second, the relationship between the life-cycle cost and the operational safety risk factors is established in the cost-estimation functions. Third, a fault tree analysis (FTA) is introduced to identify the traffic risk factors from the design variables. The safety performance of the design solutions is also assessed by the generalized linear-regression model. Fourth, a theory of acceptable risk analysis is introduced to the traffic safety assessment, and a computing algorithm is proposed to solve for a cost-efficient optimal solution within the range of acceptable risk, in order to help decision-makers. This approach was applied and examined in the Sichuan–Tibet Highway engineering project, which is located in a complex area with a large elevation gradient and a wide range of mountains. The experimental results show that the proposed approach significantly improved both the safety and cost performance of the project in the study area.

A new approach to assessing vulnerability of mountain highways subject to debris flows in China

Mountain highways in China are located in various natural geographical areas with intense tectonic activity, steep topography and a high frequency of extreme precipitation events. These conditions make the highways vulnerable to the occurrence of multiple large debris flows simultaneously during heavy rainfall. To manage this hazard risk, a broader understanding of the hazard effects of debris flows and the vulnerability of highways is needed to reduce the losses resulting from these hazardous events. Accordingly, we analysed the effects of debris flow hazards on mountain highways and established an updated systematic indicator system to describe the vulnerability of highway infrastructure and movable hazard-affected objects. Next, we proposed a new integrated model of highway vulnerability based on the environmental sensibility, structural properties and functional effects of the highway infrastructure and on the exposure probability and quantity of movable hazard-affected objects. By analysing the characteristics of elements affected by debris flows, we developed a systematic and quantitative method of vulnerability assessment for mountain highways. Finally, this implemented method was applied to a case study in the Xiqu section of the Sichuan-Tibet Highway, an area seriously affected by debris flows during each rainy season. The hazard characteristics of disasters were analysed, and the affected highway sections were divided into four vulnerability levels. The analysis of the results indicated that the calculated vulnerability coincides with the actual effects of the disaster, which strongly suggests that the vulnerability assessment generated by the proposed method can serve as a pertinent guide for route selection, road rehabilitation and hazard mitigation of highways affected by debris flows in mountainous regions.