Search Results (115)

Alpine permafrost and seasonally frozen ground threaten the long-term safe operation of highway infrastructures. Aiming at the structural performance optimization of prefabricated corrugated steel plate retaining walls in alpine permafrost regions, this study adopted finite element numerical simulation combined with field test validation to systematically explore the influences of wall height, plate thickness, corrugation geometry, and tie reinforcement layout on structural deformation and internal force, and carried out targeted parameter optimization. The core innovations include the following: (1) Structural lateral displacement and internal force rise nonlinearly with the increase in wall height, and high retaining walls exhibit an accelerated growth trend of deformation and stress. (2) Increasing plate thickness can effectively reduce structural displacement and stress, while the improvement effect gradually weakens after exceeding a critical thickness. Specifically, when the thickness increases from 4 mm to 5 mm, the displacement decreases by 33.13%. (3) Appropriately increasing corrugation pitch and height improves structural equivalent stiffness and optimizes stress distribution. Increasing the corrugation pitch from 75 mm to 400 mm and corrugation height from 25 mm to 150 mm reduces the maximum horizontal displacement by 52.6%. This demonstrates that larger corrugation profiles significantly improve structural stiffness. For walls higher than 6 m, the spacing should be reduced to 0.8 m × 1.0 m to provide additional lateral restraint. (4) Furthermore, seasonal freeze–thaw cycles and a non-uniform temperature field significantly amplify structural displacement and stress. After 12 months of freeze–thaw cycles, the maximum horizontal displacement increases by 49.7% and the maximum equivalent stress increases by 56.9% compared to the initial state. This study clarifies the parameter control mechanism and temperature coupling effect and provides a reliable theoretical basis and design reference for the engineering application of prefabricated corrugated steel plate retaining walls in alpine permafrost areas. Full article

Railway transportation networks increasingly share constrained corridors with transmission lines, buried pipelines, and other linear infrastructure. Electromagnetic interference in these corridors is important for safe railway planning and operation, particularly when nearby high-voltage lines cross oil and gas pipelines. This paper investigates transmission-line-induced pipeline potential under crossing conditions in the Zhangbei region. The CDEGS moment-method framework is applied with locally refined segmentation in the crossing regions, and an electromagnetic coupling model for multiple-crossing transmission line-oil and gas pipeline systems is established. The qualitative effects of crossing angle and parallel length on pipeline potential were obtained under both normal operating conditions and single-phase ground fault transient conditions. The results show that induced voltage decreases nonlinearly as the crossing angle increases and rises markedly with crossing length. The contribution of ground potential rise during transient processes to pipeline potential is significantly greater than that during steady-state processes. Installing zinc ribbons as a drainage measure can reduce the pipeline-to-ground voltage. However, supplementary mitigation measures may still be required under severe interference conditions. These findings are relevant to railway transportation because railway corridors often coexist with transmission lines and buried pipelines, making coordinated electromagnetic compatibility assessment essential for infrastructure safety and operational reliability. The proposed framework supports corridor planning, risk assessment, and protective design for railway-related infrastructure in complex shared corridors. Full article

The safety and stability of onboard Liquid Hydrogen (LH₂) storage systems depend strongly on gas–liquid two-phase flow, heat transfer, and phase change under sloshing; however, the coupled influence of filling ratio and sloshing on thermo-hydrodynamic behavior remains underexplored. We develop a Volume of Fluid (VOF)-based two-phase Computational Fluid Dynamics (CFD) model in ANSYS Fluent to quantify interfacial dynamics, pressure response, and temperature-field evolution in LH₂ tanks subjected to sinusoidal acceleration for filling ratios from 10% to 90%. Increasing the filling ratio strengthens net condensation in the ullage and thus intensifies depressurization. As the filling ratio increases from 10% to 90%, the pressure reduction over the 2.0 s sloshing process increases from 0.418 kPa to 2.410 kPa, and the corresponding initial depressurization rate rises from 0.209 to 1.205 kPa s⁻¹. Free-surface motion decreases with filling ratio: at 10%, large interface excursions can induce gas-cavity formation and splashing, increasing the risk of intermittent propellant supply, whereas at 90% the interface is constrained and oscillations are suppressed. Higher filling ratios lead to faster ullage cooling and larger temperature oscillations. The liquid warms modestly, and its warming rate decreases nonlinearly with filling ratio, consistent with the larger effective thermal mass at higher fillings. Overall, the obtained mechanistic understanding can support the engineering design of onboard LH₂ tanks, including filling-ratio selection and thermal-management optimization under sloshing conditions. Full article

This paper presents a full-scale case study on real-time corrosion monitoring in an underground reinforced-concrete potable water tank built in 1968. The study aims to demonstrate how continuous electrochemical monitoring can support durability assessment and predictive maintenance in ageing water-retaining infrastructure, where direct inspection is often limited and exposure conditions are spatially variable. Fourteen monitoring points were installed in beams, columns and domes subjected to different exposure conditions. Corrosion potential, concrete resistivity, corrosion current density and temperature were recorded every 3 h and used to assess the corrosion state of the reinforcement. The monitored durability indicators were reinforcement section loss, estimated from corrosion current density using Faraday’s law, and corrosion-induced crack-width evolution, used as a serviceability-related indicator for maintenance planning. The results show that beams remained predominantly passive, with corrosion current densities below 0.1 µA/cm² and incremental sectional losses below approximately 2 µm during the monitoring period. Columns showed the highest vulnerability, particularly at lower elevations subjected to prolonged immersion, with estimated incremental section losses reaching approximately 4–6 µm and a clear correlation between submerged time and corrosion progression. Domes exhibited intermediate behaviour, with occasional activation events associated with environmental fluctuations. A multivariable model combining resistivity and temperature was used to interpret corrosion kinetics, while Faraday-based section-loss estimates were coupled with empirical crack-width models to forecast serviceability indicators up to 2045. These forecasts are presented as scenario-based maintenance-support indicators rather than deterministic predictions of future damage, since corrosion propagation and crack development may evolve nonlinearly under changing exposure conditions. The proposed approach demonstrates how continuous corrosion monitoring can be linked to durability limit-state assessment, enabling risk-informed and performance-based maintenance of critical water infrastructure. Full article

This work studies a finite-time mean-field type control problem arising from optimal investment under uncertainty with risk management. The problem leads to a nonlinearly coupled system of parabolic equations with temporal and nonlocal interactions. An explicit characterization of the solution to the system is obtained, and a generalized finite difference method (GFDM) combined with an iterative scheme is developed to ensure global temporal consistency of the mean-field feedback during backward computation. Numerical experiments illustrate the accuracy and effectiveness of the proposed approach. In addition, sensitivity studies with respect to the volatility and risk-aversion parameters demonstrate the robustness of the proposed numerical framework under parameter perturbations. Full article

Short-term precipitation forecasting is essential for disaster prevention, urban management, and weather-sensitive decision making, yet radar-based nowcasting remains challenging because precipitation systems evolve nonlinearly and high-frequency echo structures are easily over-smoothed by deterministic sequence models. This paper proposes a ViT-modulated diffusion spatiotemporal prediction network (VSTPN) that cascades a spatiotemporal prediction module with a ViT-conditioned diffusion refinement module. The spatiotemporal module models the temporal evolution of radar echoes, whereas the ViT-Diffusion module uses global contextual features as conditional guidance during iterative denoising to refine spatial structures. Experiments on the HKO-7 benchmark show that VSTPN achieves lower MSE and higher SSIM than the tested baselines and improves CSI, HSS, and POD at the evaluated reflectivity thresholds. At the 40 dBZ threshold, the model improves CSI, HSS, and POD, while its FAR is slightly higher than that of ETCJ-PredNet, indicating a recall–false alarm trade-off for intense echoes. Additional post-hoc diagnostic analyses of relative gains, metric consistency, threshold sensitivity, and component effect sizes further support the stability of the reported improvements under the current experimental protocol. The results suggest that coupling spatiotemporal sequence modeling with diffusion-based radar echo refinement is a feasible direction for short-term precipitation forecasting; nevertheless, probabilistic uncertainty evaluation, multi-domain validation, and additional generative-quality metrics remain important directions for future work. Full article

Rain-on-snow (ROS) is a hydrometeorological phenomenon in which liquid precipitation falls onto an existing snowpack, augmenting runoff through the combined effects of rainfall and accelerated snowmelt. Anthropogenic climate change is progressively shifting the rain-to-snow partitioning of precipitation and altering land-surface conditions across mid- to high-latitude mountainous regions, thereby heightening flood potential. Most previous work, however, has addressed ROS at regional scales and over historical periods; hemispheric-scale assessments of future ROS dynamics and their implications for flood hazard and societal exposure remain scarce. Here we apply 10 bias-corrected CMIP6 models together with ERA5-Land reanalysis data to project changes in ROS days across the Northern Hemisphere under four Shared Socioeconomic Pathway (SSP) scenarios. ROS days are coupled with flood frequency analysis to quantify changes in ROS flood occurrence, and gridded population and Gross Domestic Product (GDP) data are integrated to evaluate future population-socioeconomic exposure. Under low-to-medium emission scenarios, ROS days increase substantially over historical hotspots, whereas under high-emission scenarios they decline at mid- to high latitudes yet expand into previously unaffected high-latitude and inland cold regions. ROS flood days respond nonlinearly to ROS frequency because progressive snow water equivalent loss limits runoff generation, causing ROS floods to decrease in some mountainous areas even as ROS events become more frequent. Population-socioeconomic exposure exhibits a corresponding polarization: it declines in mid-latitude regions where snow cover is disappearing but rises sharply at high latitudes, with high-emission pathways accelerating the northward migration of disaster risk. These findings bridge critical gaps in large-scale ROS climatology and shed light on future changes in ROS-induced hydrological extremes. Besides, the findings facilitate the creation of regionally focused adaptation strategies and provide useful references for integrating climate model projections with remote sensing observations to improve future monitoring and risk assessment of ROS-related floods. Full article

Given the escalating impacts of global climate change and extreme weather events, the accurate stability assessment of rainfall-induced landslides necessitates a comprehensive consideration of both seepage processes and the inherent spatial variability of soils. Traditional deterministic and two-dimensional (2D) analyses often fail to capture the multi-dimensional kinematic features of slope failures and the stochastic nature of soil heterogeneity, thereby leading to inaccurate risk assessments. This study proposes a three-dimensional (3D) slope reliability analysis framework. Within this framework, a 3D slope geometric model is constructed using GeoStudio 2025.1.0 software, and seepage analysis is conducted by the SEEP3D module. To account for soil spatial variability, the Karhunen–Loève (K-L) expansion method is employed to discretize key shear strength parameters (effective cohesion and effective angle of internal friction). The factor of safety (F_s) is evaluated using the 3D simplified Bishop method, which is then coupled with Monte Carlo simulations to determine the probability of failure (P_f). The results show that rainfall infiltration causes progressive dissipation of shallow matric suction and a significant rise in the groundwater table near the slope toe, resulting in reduced effective stress in the critical resistance zone. As rainfall intensity increases, the F_s decreases approximately linearly from 1.14 to 0.90, whereas the P_f increases nonlinearly from nearly 0 to 98.36%. Under the rainstorm condition, although the F_s remains above unity at 1.063, the corresponding P_f reaches 23%, indicating that deterministic evaluation based only on the F_s may underestimate the actual failure risk. The proposed framework provides a quantitative tool for evaluating rainfall-induced slope instability by integrating transient hydraulic response, three-dimensional spatial variability, and probabilistic reliability assessment. Full article

A recently proposed tensor–scalar extension of gravity coupled to extended Aharonov–Bohm electrodynamics admits one-variable traveling reductions in which a longitudinal electromagnetic scalar mode $S={\partial }_{\mu }{A}^{\mu }$ couples nonlinearly to gravitational scalars. In the weak-field regime outside sources, a one-dimensional traveling ansatz depending on $\xi =x-vt$ reduces the field equations to a coupled autonomous ODE system. The mathematical core of the reduction is a singular Newton-type equation whose classical mechanics counterpart is known; the novelty here lies in its derivation from the scalar–tensor/Aharonov–Bohm field system, in the physically motivated normalization of the traveling-wave families, and in the resulting phase–space interpretation for source-generated pulse selection. We provide a systematic classification of all admissible initial data and of the corresponding maximal solutions, distinguishing repulsive/attractive regimes and subcritical/critical/supercritical behaviors through a normalized parameter map. In particular, attractive branches may reach the singularity in finite time with a universal collision exponent $2/3$, while escaping branches exhibit asymptotically uniform motion with a computable logarithmic correction. Finally, we construct a numerical atlas of representative trajectories and validate the computations by cross-checking direct time integration against numerical inversion of the implicit quadrature, together with energy-defect diagnostics. Full article

Accurate prediction of the dynamic load-bearing characteristics of suction anchors is critical for the safety and reliability of floating offshore wind turbines. This study bridges the gap between mooring approaches and anchor foundation response assessment by systematically quantifying how the choice of mooring analysis method (dynamic or quasi-static) affects the predicted displacement response of suction anchors. Using OpenFAST coupled with a validated suction anchor dynamic response model (SADR), the motion responses of the suction anchor foundation for the OC4 semi-submersible platform are computed under regular waves of varying heights and periods, as well as irregular sea states representing operational and extreme conditions. The results reveal that the ratio of the anchor displacement predicted by dynamic mooring analysis to that predicted by quasi-static mooring analysis grows nonlinearly with increasing wave height and rises substantially as wave period lengthens, indicating that mooring dynamic effects become progressively more pronounced under large wave heights and long-period swell conditions. Statistical analysis under irregular waves further reveals that under moderate operational conditions, the response variability predicted by the two methods remains comparable; however, under extreme sea states, dynamic analysis yields not only larger peak displacements but also substantially greater response variability, with standard deviations significantly exceeding those obtained from quasi-static predictions. These findings provide quantitative evidence that the application of quasi-static mooring analysis to anchor foundation design carries a substantial risk of underestimating true responses, and that this underestimation becomes increasingly severe under high wave heights, long periods, and extreme conditions. The work establishes that dynamic mooring analysis is essential for reliable suction anchor foundations design and long-term serviceability assessment. Full article

Extreme hydrological events fundamentally alter sediment transport dynamics across grain, reach, and watershed scales, rendering classical equilibrium-based transport formulations inadequate. This review synthesizes recent advances in multiscale sediment transport modeling under highly unsteady and high-magnitude forcing conditions. At the grain scale, particle-resolved simulations demonstrate that sediment entrainment is governed by turbulence intermittency and transient force exceedance rather than mean bed shear stress thresholds, particularly when the hydrograph rise timescale (T_h) becomes comparable to particle response times (T_p). At the reach scale, non-equilibrium transport emerges when the unsteadiness ratio T_h/T_a∼O(1), where T_a is the sediment adaptation timescale representing the time required for sediment flux to adjust toward transport capacity. Under these conditions, pronounced hysteresis between discharge and sediment flux is observed, requiring relaxation-based transport formulations instead of instantaneous equilibrium laws. At the watershed scale, the sediment delivery ratio (SDR), defined as the ratio of sediment yield at the basin outlet to total hillslope erosion, becomes highly time-dependent. Extreme precipitation events can activate hillslope-channel connectivity, increasing SDR by orders of magnitude relative to baseline conditions. A unified dimensionless scaling framework is presented based on mobility intensity (θ/θ_c, where θ is the Shields parameter and θ_c is its critical value for incipient motion), unsteadiness ratio (T_h/T_a), and morphodynamic coupling (T_f/T_m, where T_f is the hydraulic advection timescale and T_m is the morphodynamic adjustment timescale). This framework enables classification of sediment transport regimes ranging from quasi-equilibrium to cascade-dominated states. The synthesis demonstrates that predictive uncertainty increases nonlinearly across scales due to timescale compression, threshold activation, and feedback between flow hydraulics and evolving morphology. Recent developments in hybrid physics-AI approaches show promise in improving predictive capability by enabling dynamic transport closures, surrogate modeling of computationally expensive microscale processes, and data assimilation for real-time forecasting. However, these approaches remain limited by extrapolation uncertainty and the need to enforce physical constraints. Overall, this review concludes that regime-aware multiscale coupling, combined with uncertainty quantification and adaptive modeling strategies, is essential for robust sediment hazard prediction and climate-resilient infrastructure design under intensifying hydrological extremes. Full article

The Zhuanghe coast in the northern part of the Yellow Sea is one of China’s important fishing and ocean engineering areas. Frequent storm surge events pose a significant threat to residents’ safety and properties. This study used the coupled Finite Volume Coastal Ocean Model (FVCOM) and the Surface Wave Model (FVCOM-SWAVE) to investigate storm surges and wave heights during Typhoons Muifa (1109) and Lekima (1909) in the northern parts of the Yellow Sea and analyze the impact of the typhoon parameters on flood inundation on the Zhuanghe coast. The wind stress comparison in the coupled wave–current model uses synthetic wind field data formed by superimposing ERA5 wind fields with a parameterized typhoon model. The results showed that the simulated and measured tide levels, wave heights, and storm surges were in good agreement, indicating that the coupled model accurately reproduced the dynamics of the storm surges and wave heights during the two typhoons. The maximum significant wave height (H_s) exhibited a right-skewed distribution in the two typhoons’ paths, with extreme values consistently located to the right of the typhoon’s center. The decrease in atmospheric pressure at the center of Typhoon Muifa was significantly, nonlinearly, and positively correlated with the severity of storm surge disasters. A significant correlation was observed between the path of Typhoon Muifa and the disaster intensity. Full article

High-geothermal tunnels are subjected to complex thermo–hydro–mechanical (THM) coupling effects, where the interaction of temperature, seepage, and stress significantly influences the stability of surrounding rock. To address the limitations of conventional models assuming uniform initial temperature, a THM-coupled numerical model incorporating an in situ temperature gradient is established based on the Sangzhuling Tunnel. The concept of the thermal influence zone is quantitatively defined by an equivalent-radius method, and its spatiotemporal evolution is systematically investigated. In addition, the distinct roles of temperature and pore water pressure in controlling deformation and plastic-zone evolution are comparatively clarified. The results show that the thermal influence zone expands nonlinearly with increasing initial rock temperature and gradually stabilizes over time. Temperature and pore water pressure both promote the development of the plastic zone, which predominantly propagates along directions approximately 45° to the horizontal. Under the geological and boundary conditions considered in this study, temperature plays a dominant role by inducing thermal stress and degrading mechanical properties, leading to significant expansion of the plastic zone and increased vault deformation. In contrast, pore water pressure mainly reduces effective stress, thereby influencing deformation distribution, especially at the tunnel invert. Overall, THM coupling significantly amplifies surrounding rock failure compared with single-field conditions. The findings provide quantitative insights into the evolution of the thermal influence zone and its coupled control on deformation and plasticity, offering a theoretical basis for support design and stability control in high-geothermal tunnels. Full article

To address the strongly nonlinear hydrodynamic coupling and complex maneuvering challenges encountered by large ships during berthing operations in restricted waters, this paper proposes a high-precision autonomous berthing control system incorporating four-quadrant propeller hydrodynamics. Based on an improved Mathematical Maneuvering Group (MMG) framework, a three-degree-of-freedom (3-DOF) dynamic model is established to accurately capture the transient thrust and torque mappings of the propeller over all four quadrants. A dynamic line-of-sight (LOS) guidance system with a nonlinearly decaying acceptance radius is tightly coupled with PD/PI controllers to coordinate and regulate the rudder angle and propeller rotational speed. The numerical solver was rigorously validated against turning-test data for the S-175 container ship, with the errors of the key parameters all controlled within 15%. Subsequently, under the environmental conditions of Yangshan Port, full-condition path-planning and berthing simulations were conducted for the novel B-573 container ship under steady-current disturbances. These simulations evaluated multiple flow directions, namely due south, due north, due west, and due east defined in the Earth-fixed coordinate system, as well as multiple intensity levels ranging from 0 to 1.5 m/s that were specifically tested under the due north current. Quantitative evaluation shows that, under the highly challenging current condition of 1.0 m/s, the dynamic corrective mechanism effectively drives the global mean absolute error (MAE) to converge to 85.50 m, representing a 62% statistical reduction relative to the transient peak value. In addition, a parameter sensitivity analysis based on the cumulative cross-track error confirms that, when subject to variations in the underlying hydrodynamic parameters, the proposed system can suppress fluctuations in trajectory error to a very low level, thereby demonstrating a certain degree of control robustness. During the terminal berthing stage, the vessel smoothly completed an extreme deceleration from an initial speed of 6.4 m/s to a full stop within 588 s, while constraining the maximum astern rotational speed to −2 rps and seamlessly passing through all four propeller quadrants. The results confirm that the proposed autopilot framework possesses a certain degree of engineering feasibility in complex maritime environments. Full article

Micro-meteorological monitoring systems have been widely deployed in power grids, providing essential data to support the prevention and mitigation of ice- and wind-related disasters. However, understanding of the associated error mechanisms and quantitative evaluations under freezing rain and snow remains limited, particularly in complex field environments. This study presents a field-based quantitative assessment of two key variables, air temperature and wind speed, based on comparative observations collected over multiple winter icing cycles. We analyze the coupled effects of low temperature, ice accretion, and solar radiation on temperature measurements through multi-configuration sensor comparison, and characterize the dynamic response of cup anemometers under icing conditions using cross-correlation lag analysis. Results show that temperature error is dominated by sensor installation configuration and solar radiation. Under weak solar radiation, unshielded sensors tend to record lower temperatures than a standard Stevenson screen, but once radiation exceeds 200 W/m², they warm rapidly and exhibit maximum positive biases of ~8–10 °C. Ice accretion further induces a cold bias of ~1 °C and a response lag of 5–18 min, while suppressing the rapid warming driven by shortwave radiation. For wind measurements, cup anemometers show clear underestimation during ice accretion, with the error increasing nonlinearly with ice thickness to ~20% before freezing-induced failure occurs. These findings provide a basis for improved sensor deployment and interpretation of field monitoring data in cold, humid, and icing-prone environments, although the quantitative results are site-dependent. Full article