The long pipe shed advanced support has the characteristics of large construction difficulty, wide support range, and easy deviation. The difference in construction dip angle will produce a different plastic zone of surrounding rock and supporting effects, and the rationality of advanced support design plays an important role in the safety of tunnel opening excavation. Based on the Tianshan Tunnel project, this paper aims at the problems of the loose pebble layer structure, poor cohesion, strong permeability, shallow excavation is not easy to form a confined arch, and easy to causes surrounding rock deformation and failure. Combined with the new method theory, the finite difference software FLAC^3D was used to simulate seven excavation schemes of the shallow tunnel entrances. The mechanism and effect of advance support and the influence of construction angle on support effect are analyzed, and the simulation values are compared with the measured data. The results show that using long pipe shed advanced support can effectively reduce the disturbance of excavation to lose pebble rock mass and reduce the convergence value of surrounding rock. The plastic zone of surrounding rock produced by different angles can be divided into three parts. The structural stability difference of the C₁ zone is small, and that of the C₃ zone is large. The bending moment, shear force, and shape variables of the pipe shed decrease with the increase of the dip angle, while the axial force increases with the increase of the dip angle. According to the similarity between the simulated curve and the measured curve, the best supporting effect can be achieved when the dip angle of the pre-supporting pipe shed is set in the range of 0° ~ 3°.

Currently, research on employing finite difference method and discrete element method (FDM-DEM) coupling to assess the stability of tunnel lining structures is limited. This study utilized the FDM-DEM coupling approach, with the F2 fault of the East Tianshan Tunnel as a case study, to develop a numerical model in conjunction with PFC3D 6.0 and FLAC3D 6.0 software. We conducted a comprehensive analysis of the displacement deformation and crack progression of the tunnel lining structure under varying dislocation momentum conditions, unveiling the underlying mechanisms. The findings indicated that as the dislocation increased, the extent of damage to the vault intensified, and the particle contact force within the tunnel lining shifted from compression to tension, significantly contributing to the crack formation. Fault dislocation influenced the gradual expansion of cracks from the vault to the spandrel and arch waist, with the crack width increasing alongside the rising dislocation momentum. In particular, under substantial dislocation momentum, the overall stability of the tunnel lining was markedly diminished. The safety factor at the tunnel section declined progressively as the dislocation momentum escalated, with values of 2.53, 2.49, 2.43, 2.39, and 2.32 corresponding to dislocation momenta of 0.01 m, 0.05 m, 0.1 m, 0.15 m, and 0.2 m, respectively. This research offers valuable insights and a reference framework for investigating the stability of tunnel lining structures in proximity to fault dislocations, pinpointing potential failure points, and bolstering the structural integrity of tunnels.

More recently, it is strongly required that road management involves a wide range of topics over the world and it must be considered from a perspective of customer-oriented levels of service and road administrators are requested to present a visual map of road network in terms of road network monitoring among others. To fulfill these requirements, firstly, this paper introduces new topics, which is related to two Compact Road Profiler (CRP) to monitor the road roughness conditions including localized distresses. In addition, we analyze road profile data considering two wheel paths by the survey vehicle, known as inner and outer wheel paths, and using a sophisticated driving simulator (DS) to evaluate ride quality conditions based on Power Spectrum Density (PSD) function to identify the sensitive range of ride quality. Finally, we calculate the Root Mean Square (R.M.S) and Crest Factor (CF) using vertical acceleration data of the passenger seat in DS. Consequently, it is shown that advanced evaluation of the ride quality under two wheel paths is performed. As regards standard for ride evaluation, ISO 2631 standard is adopted.

In this thorough examination, we dive deep into the long-lasting mechanical characteristics of fine-grained saline soil subgrades, aspiring to establish a precise and reliable collection of predictive models. Our objective is to provide a solid scientific footing for the design and ongoing upkeep of road networks within saline soil environments. Analyzing prolonged monitoring data across diverse highway subgrades within a prototypical saline soil locale, we unveil the intricate temporal fluctuations and environmental sensitivities of the soil’s mechanical properties under continuous load. Precisely, the subgrade’s compressive modulus dwindled by 15%, while shear strength declined by 8% over a five-year period. These trends intensify during rainy and scorching seasons, with drops surpassing 20% and 12% respectively. Leveraging this intricate data, we deploy nonlinear regression analysis and sophisticated machine learning algorithms to construct a predictive model tailored for the long-term mechanical properties of fine-grained saline soil roadbeds. This model integrates a multitude of factors, including load duration, temperature, humidity, and more, delivering accurate forecasts of key subgrade indicators like compressive modulus, shear strength, and beyond. In the verification stage, compared with the measured data, the error rate of the model prediction results is controlled within 5%, showing high prediction accuracy and stability. In addition, we also carried on the sensitivity analysis to the model, found that the load size and the duration of the impact on the mechanical properties of the roadbed is the most significant. Therefore, in the design of road engineering in saline soil areas, the influence of these factors should be fully considered, and reasonable engineering measures should be taken to ensure the safety and durability of roads. This study not only provides effective data support for the long-term mechanical performance evaluation of fine-grained saline soil roadbed, but also provides an important theoretical reference for engineering practice in related fields.

Heavy rainfall infiltration is a key disaster-inducing factor that triggers the softening of surrounding rock and deformation of support structures in tuff gully tunnels. Based on the gully section of the left line of the Dabao Tunnel of the Leigongshan–Rongjiang Expressway in Guizhou Province, this study systematically reveals the synergistic disaster-inducing mechanism of “topography-seepage-softening” in tuff gully tunnels under heavy rainfall infiltration through laboratory tests and FLAC3D 3D numerical simulations. The main innovative conclusions are as follows: (1) The “phased” attenuation law of tuff mechanical parameters was quantified, and the critical water content for significant strength deterioration was determined to be 2.5%, with a saturated softening coefficient of 0.59. These results provide key data for early warning and evaluation of similar projects. (2) A “convergence-disorder” distribution pattern of pore water pressure controlled by gully topography was revealed. It was found that the rock mass directly below the aqueduct exhibits a disordered zone with downward-extending pore water pressure due to fluid convergence, with the maximum pore water pressure reaching 0.55 MPa. This clarifies the essence that tunnel stability is controlled by the coupling of topography and seepage field. (3) The key sensitive areas for tunnel stability—namely the gully bottom, arch haunches, and the area below the aqueduct—were accurately identified. The significant increase in displacement of these areas after rock stratum softening was quantified (e.g., the displacement at the crown of the secondary lining increased from 3 mm to 4 mm, and the influence range expanded to the arch haunches). This study clarifies the deformation characteristics and instability mechanism of tuff gully tunnels under heavy rainfall from two aspects: the “internal mechanism of rock mass softening” and the “external condition of topographic seepage control.” It can provide a theoretical basis and key technical pathway for disaster prevention and control as well as stability design of similar tunnels.

The cumulative displacement of landslides in alpine regions is jointly affected by rainfall, temperature variation, freeze–thaw cycles, and other factors, and usually exhibits nonlinear, non-stationary, and multi-scale fluctuation characteristics. To improve the accuracy of landslide displacement prediction under complex environmental conditions, this study takes the Taker Tubek Village landslide in Gongliu County, Xinjiang, China, as the study object. Cumulative displacement data from GNSS02 and GNSS03, together with daily rainfall and daily mean temperature, were used to construct a SOO-VMD-CNN-TimeXer hybrid prediction model. First, SOO was employed to adaptively optimize the VMD parameters, and the cumulative displacement series were decomposed into multiple IMF components. Then, CNN was used to extract local fluctuation features, while TimeXer was applied to model long-term temporal dependencies and the effects of exogenous variables. Finally, the predicted results of all components were reconstructed to obtain the cumulative displacement prediction. The results show that the proposed model achieved high prediction accuracy at both GNSS02 and GNSS03. The MSE, MAE, MAPE, and R² values were 0.0158, 0.0960, 0.0112, and 0.9464 for GNSS02, and 0.0483, 0.1590, 0.0203, and 0.9946 for GNSS03, respectively, outperforming LSTM, Informer, iTransformer, Crossformer, and other models. The results indicate that the SOO-VMD-CNN-TimeXer model can effectively characterize the cumulative displacement evolution of landslides in alpine regions and provide technical support for landslide deformation trend forecasting and disaster early warning.