Traditional landslide monitoring systems struggle to capture the spatiotemporal dynamics of rainfall-induced hydro-mechanical processes, with a significant risk of signal loss during critical “unsaturated-saturated” state transitions. To address this issue, we propose an integrated framework that utilizes FLAC3D numerical simulation to dynamically optimize multi-sensor deployments. Through coupled seepage-stress analysis under different rainfall scenarios in China’s Tianshan Mountains, this study achieved the following objectives: (1) risk-based sensor deployment by precisely identifying shallow shear strain concentration zones (5–15 m) through FLAC3D simulation (with FBG density of 0.5 m/point in the core sliding belt and GNSS spacing ≤ 50 m); (2) establishment of a multi-parameter cooperative early warning system (displacement > 50 mm/h, pore water pressure > 0.4 MPa, strain > 6400 με), where red alerts are triggered when at least two parameters exceed thresholds, reducing false alarm rates; and (3) development of an adaptive sampling framework based on three rainfall intensity scenarios, which increases measurement frequency during heavy rainfall to capture transient critical points (GNSS sampling rate enhanced to 10 Hz). This approach significantly enhances the capture capability of critical hydro-mechanical transition processes while reducing the monitoring redundancy. The framework provides a scientifically robust and reliable solution for slope disaster-risk prevention and management.

With the continuous progress of traffic technology and the continuous improvements in traffic infrastructure, the maintenance and management of highway mechanical and electrical equipment has become a key factor affecting highway operation efficiency. However, at present, most of the mechanical and electrical systems of expressways cannot monitor the equipment continuously in terms of operation and maintenance, and most of the equipment operation and maintenance stay only in the stage of equipment failure. In addition, there are many kinds of highway mechanical and electrical equipment, and there are significant differences in the levels of parameters, so the parameter levels of highway mechanical and electrical equipment cannot fully meet the operation requirements of the area. Therefore, based on the basic theory of the Markov chain and the concept of daily operation and maintenance, this paper constructs a multistate Markov fault prediction model considering maintenance. Based on the historical data, the model realizes the prediction of the equipment failure rate and the formulation of the optimal maintenance strategy for the equipment, taking video surveillance equipment as an example, and verifies the improvement in the value of the equipment under this strategy through the value engineering theory. Based on the prediction results, more reasonable technical parameters are customized for equipment with a high failure rate to improve the practicability and reliability of the mechanical and electrical equipment in the area.

Fixed indicator weights cannot fully capture changes in governance priorities as wind–sand hazards intensify. Using the Xinjiang section of the G30 Lianhuo Expressway as a case study, this study classified wind–sand events into three scenarios: warning and restricted traffic, high-risk traffic control and diversion, and extreme conditions with comprehensive rescue. A five-dimensional evaluation system containing 15 indicators was established. Scenario-specific weights were determined using the Best–Worst Method, while TOPSIS was applied to evaluate governance performance based on objective response records and independent text coding. The dominant dimensions across the three scenarios were risk monitoring and early warning, decision activation and traffic control, and rescue resources and coordination, with weights of 0.356, 0.392, and 0.421, respectively. The corresponding closeness coefficients were 0.577, 0.667, and 0.733. As hazard severity increased, governance priorities shifted from risk communication to traffic diversion, interdepartmental coordination, evacuation, medical assistance, and support for vulnerable groups. Compared with the fixed-weight model, the dynamic model improved the closeness coefficients by 0.029, 0.064, and 0.092. The results show that scenario-specific weighting can support staged response, resource allocation, and human safety protection on desert expressways.

Premature skid resistance deterioration is a critical issue limiting the long-term performance of thin asphalt overlays. To elucidate the meso-scale degradation mechanisms, this study employs the Discrete Element Method (DEM) implemented in Particle Flow Code (PFC) to compare a conventional Stone Matrix Asphalt (SMA-10) mixture with an optimized skeleton-reinforced design, termed Optimized Gradation-10. The optimized gradation was developed by introducing supplementary sieve sizes of 5.6, 6.7, and 8.0 mm within the critical range of 4.75–9.5 mm following the V–S gradation framework. Cross-sectional images of actual mixtures were vectorized using Python (Version: 3.11.5) and MATLAB (Version: R2024a) to reconstruct irregular aggregate clump models that accurately capture particle morphology and spatial arrangement. Meso-scale parameters were calibrated and validated through uniaxial compression tests, and the evolution of contact number, contact force, and stress transmission was analyzed under 2.3 × 10⁵ wheel load cycles. Compared with SMA-10, the optimized mixture increased effective aggregate contacts by 41.8%, enhanced stress transfer efficiency by 19.8%, and reduced rut depth by 10%. These findings confirm that synergistic gradation optimization through supplementary sieves and the V–S method markedly improves structural stability and deformation resistance, providing a meso-mechanical foundation for prolonging skid resistance in thin overlays.

Desert expressway corridors exposed to strong wind hazards often rely on single high-grade routes, with limited alternatives, high detour costs, and low network redundancy. These constraints make it difficult to maintain traffic operation resilience through route substitution alone. Taking the Hami–Tuyugou section of the G30 Lianhuo Expressway in Xinjiang, China, as a case study, this study investigates the formation and evaluation of traffic operation resilience in a wind-hazard-affected, low-redundancy desert expressway corridor. A hierarchical indicator system was constructed with four first-level, fourteen second-level, and thirty-one third-level indicators. Fuzzy DEMATEL(Decision Making Trial and Evaluation Laboratory)–ISM(Interpretive Structural Modeling) was used to identify causal relationships and hierarchical transmission paths; fuzzy DANP(DEMATEL-based Analytic Network Process)–AHP(Analytic Hierarchy Process) was applied to determine indicator weights; and a cloud model was employed to evaluate the overall resilience level. The results show that institutional adaptability, organizational learning, monitoring and information support, and multi-actor collaboration are the main upstream drivers. The corridor was evaluated as Grade IV, indicating a relatively high resilience level approaching Grade V. Sensitivity analyses confirm the robustness of the substantive conclusion. The findings suggest that, under low-redundancy conditions, resilience depends less on structural redundancy and more on adaptive governance, information support, and coordinated response.