Discontinuous deformation analysis (DDA) is a discontinuum-based and implicit method for investigating the deformational behavior of block systems. The constant acceleration integration (CAI) scheme characterized by unconditional stability is employed in the traditional DDA. In this study, the problems of the CAI scheme regarding the time step in DDA are pointed out. A too large or too small time step size adopted in the CAI scheme will have adverse effects on the DDA computation. To overcome the weaknesses, an alternative composite implicit time integration (CITI) scheme, which is a combination of the trapezoidal rule and the three-point backward Euler method, is implemented in the three-dimensional (3D) DDA method. Verification examples and slope numerical simulations are presented to illustrate the accuracy and effectiveness of the proposed methodology. The results showed that the CITI scheme can overcome the numerical error caused by the large time step size, and the algorithm damping is closely related to the time step size and the selected splitting parameter.

Due to the harsh climatic conditions in high altitude and cold regions with large temperature differences, asphalt pavement is generally prone to cracking, and the cracks propagate rapidly, which reduces the service life and service level of the road. The factors influencing the fracture characteristics of asphalt mixtures were analyzed in this paper, and the mixtures with different aggregate gradations from various types of asphalt were prepared. The fracture characteristics were explored using the thermal stress restrained specimen test (TSRST) and low-temperature bending test, and the good consistency of the low-temperature fracture performance was identified according to the results of frost-break temperature, flexural strength, and fracture toughness. The frost-break temperature was confirmed as the best indicator of the material crack resistance and could be used as the index to evaluate the performance of asphalt mixtures at low temperatures. The frost-break temperature of matrix asphalt mixture is 8–10 °C higher than that of modified asphalt mixture, and AC asphalt mixture is 2–4 °C higher than that of SMA asphalt mixture. The excellent asphalt performance has a more important influence on the fracture characteristics of asphalt mixture. The asphalt mixture of the same type had similar fracture toughness at varying notch depths, the most deviation is 3.78% which shows that the initial crack depth has little effect on the fracture toughness of asphalt mixture at low temperature. The results of the study can provide a basis for the selection of asphalt pavement surface materials and the optimization of mixtures in high altitude and cold regions with large temperature differences.

In recent years, the socio-economic development in the Tibet region of China has experienced substantial growth. However, transportation increasingly strains the region’s fragile ecological environment. Most studies overlook the accurate measurement and analysis of factors influencing traffic carbon emissions in Tibet due to data scarcity. To address this, this paper applies an improved traffic carbon emissions model, using transportation turnover data to estimate emissions in Tibet from 2008 to 2020. Simultaneously, the estimated traffic carbon emissions in Tibet served as the predicted variable, and various machine learning algorithms, including Radial Basis Function Support Vector Machine (RBF-SVM), eXtreme Gradient Boosting (XGBoost), Random Forest, and Gradient Boosting Decision Tree (GBDT) are employed to conduct an initial comparison of the constructed prediction models using three-fold cross-validation and multiple evaluation metrics. The best-performing model undergoes further optimization using Grid Search (GS) and Real-coded Genetic Algorithm (RGA). Finally, the central difference method and Local Interpretable Model-Agnostic Explanation (LIME) algorithm are used for local sensitivity and interpretability analyses on twelve core variables. The results assess each variable’s contribution to the model’s output, enabling a comprehensive analysis of their impact on Tibet’s traffic carbon emissions. The findings demonstrate a significant upward trend in Tibet’s traffic carbon emissions, with road transportation and civil aviation being the main contributors. The RBF-SVM algorithm is most suitable for predicting traffic carbon emissions in this region. After GS optimization, the model’s R² value exceeded 0.99, indicating high predictive accuracy and stability. Key factors influencing traffic carbon emissions in Tibet include civilian vehicle numbers, transportation land-use area, transportation output value, urban green coverage areas, per capita GDP, and built-up area. This paper provides a systematic framework and empirical support for measuring, predicting, and analyzing factors influencing traffic carbon emissions in Tibet. It employs innovative measurement methods, optimized machine learning models, and detailed sensitivity and interpretability analyses. The results can guide regional carbon reduction targets and promote green sustainable development.

This paper proposes an integrated tree model architecture and a low-cost data construction method based on an improved Stacking strategy. It systematically analyzes the importance of safety indicators for mountainous sharp bends in plateau regions and conducts safety evaluation and optimization-strategy research for ten typical sharp-bend road segments in Tibet. In response to the challenges of traditional data collection in Tibet’s unique geographical and policy constraints, we innovatively use drone aerial video as the data source, integrating Tracker motion trajectory analysis, SegFormer road segmentation, and CAD annotation techniques to construct a dataset covering multi-dimensional features of “human–vehicle–road–environment” for mountainous plateau sharp-bend highways. Compared with similar studies, the cost of this dataset is significantly lower. Based on the strong interpretability of tree models and the excellent generalization ability of ensemble learning, we propose an improved Stacking strategy tree model structure to interpret the importance of each indicator. The Spearman correlation coefficient and TOPSIS algorithm are used to conduct safety evaluation for ten sharp-bend roads in Tibet. The results show that the output of the improved Stacking strategy and the sensitivity analysis of the three tree models indicate that curvature variation rate and acceleration are the most significant factors influencing safety, while speed and road width are secondary factors. The study also provides a safety ranking for the ten selected sharp-bend roads, offering a reference for the 318 Quality Improvement Project. From the perspective of indicator importance, curvature variation rate, acceleration, vehicle speed, and road width are crucial for the safety of mountainous plateau sharp-bend roads. It is recommended to implement speed limits for vehicles and widen the road-bend radius. The technical framework constructed in this study provides a reusable methodology for safety assessment of high-altitude roads in complex terrains.

Affected by climatic factors (e.g., low temperature and intense ultraviolet radiation), high-altitude regions experience numerous pavement diseases, which compromise driving safety and negatively impact user travel experience. Timely planning and execution of pavement maintenance are particularly critical. In this paper, considering the characteristics of pavement maintenance in high-altitude regions (e.g., volatility of traffic volume, seasonality of maintenance timing, and fragility of the ecological environment), we aim to derive optimal monthly maintenance plans. We develop a multi-objective nonlinear optimization model that comprehensively accounts for minimizing maintenance costs, affected traffic volume and carbon emissions, and maximizing pavement maintenance effectiveness. Utilizing linearization methods, the model is reconstructed into a typical mixed-integer linear programming (MILP) model, enabling it to be solved directly using conventional solvers. We consider five types of decision strategies to reflect the preferences of different decision-makers. Given the uncertainty of maintenance costs, we also utilize the robust optimization method based on the acceptable objective variation range (AOVR) to construct a robust optimization model and discuss the characteristics of optimistic, robust, and pessimistic solutions. The results suggest that different decision strategies show differences in the indicators of maintenance costs, affected traffic volume, carbon emissions, and pavement performance. When multiple decision objectives are comprehensively considered, the indicators are between the maximum and minimum values, which can effectively balance the decision needs of maintenance effectiveness, maintenance timing, and environmental protection. The number of maintenance workers, the requirement of the minimum pavement condition index (PCI), and the annual budget influence the maintenance planning. The obtained robust solution can somewhat overcome the conservative nature of the pessimistic solution. The method proposed in this paper helps address the complexities of pavement maintenance decisions in high-altitude regions and provides guidance for pavement maintenance decisions in such areas.

The Qinghai–Xizang Plateau and its surrounding regions have experienced intense tectonic activity, resulting in complex geostress environments that cause frequent and distinctive rockburst disasters in plateau tunnel engineering. In this study, numerical simulations were conducted to investigate the distribution characteristics and patterns of tunnel rockbursts in high-altitude regions, using geostress orientation, lateral pressure coefficient, and tunnel depth as the primary independent variables. Secondary development of FLAC3D 7.00.126 was carried out using FISH language to enable the recording and visualization of tangential stress, the Russense rockburst criterion, and elastic strain energy. Based on this, the influence mechanisms of these key geostress parameters on the location, extent, and intensity of rockbursts within tunnel cross sections were analyzed. Results indicate that geostress orientation predominantly affects the location of rockbursts, with the surrounding rock in the direction of the minimum principal stress on the tunnel cross section being particularly prone to rockburst risks. The lateral pressure coefficient primarily influences the rockburst intensity and pit range within local stress concentration zones, with higher values leading to greater rockburst intensity. Notably, when structural stress is sufficiently large, rockbursts may occur even in tunnels with shallow burial depths. Tunnel depth determines the magnitude of geostress, mainly affecting the overall risk and potential extent of rockbursts within the cross section, with greater depths leading to higher rockburst intensities and a wider affected area.