As the span increases, the flexibility and aerodynamic sensitivity of bridge structures significantly increase. Current research on the aerodynamic impacts on bridges often focuses on single factors, such as wind loads. Due to the complex mechanisms of wind-snow two-phase flow, studies on the aerodynamic safety and durability of bridge structures under wind-snow coupled loads in extreme weather conditions are still in the early stages. This study focuses on a large-span highway box girder bridge located in a cold region, conducting wind tunnel tests and numerical simulations to investigate its aerodynamic effects under wind-snow coupled loads. The wind tunnel tests include comparative studies at different wind speeds and initial particle heights. The results indicate significant differences at varying wind speeds, particularly at low speeds, with average relative change rates in aerodynamic coefficients (drag coefficient, lift coefficient, and moment coefficient) reaching 65%, 37%, and 86%, respectively, compared to the snow-free bridge surface. These differences gradually decrease and stabilize as wind speed increases. The impact of different initial particle heights is also significant, with average relative change rates exceeding 65%, 32%, and 77%, but no clear pattern was observed. Additionally, using the CFD-DEM bidirectional coupling method, numerical simulations are conducted to analyze the aerodynamic effects of the box girder bridge under different wind fairing angles and aspect ratios. The results show variations in aerodynamic coefficients for different aerodynamic shapes, with the aspect ratio having a more pronounced effect than the wind fairing angle. These findings emphasize the importance of studying the aerodynamic effects on bridge structures under windsnow coupled loads and provide valuable data and methodological references, highlighting their significant implications for practical engineering.

The accurate prediction of extreme dynamic amplification factor (DAF) values is significantly important to ensure a long-term safety assessment of bridges under stochastic vehicular loading. However, predicting extreme DAFs is challenging due to traffic randomness, road roughness variability, and nonlinear vehicle–bridge interaction (VBI) effects. This study presents an integrated framework for extreme DAF prediction for simply supported bridges by combining stochastic traffic–bridge interaction simulations with Bayesian updating and a Peaks-Over-Threshold–Generalized Pareto Distribution (POT–GPD) model. A coupled VBI model is developed, incorporating cellular automaton-based traffic flow, multi-axle nonlinear vehicle dynamics, finite-element bridge modeling, and stochastic road roughness profiles. A new DAF definition based on dynamic displacement difference is proposed to better represent dynamic effects. DAF samples obtained from VBI simulations under different road roughness levels are analyzed using the POT method, with GPD parameters estimated through maximum likelihood and Bayesian inference. Extreme DAFs corresponding to different return periods are then determined. The results indicate that extreme DAF values increase with worsening road roughness and longer return periods and that the Bayesian POT–GPD approach effectively captures tail behavior while providing reliable uncertainty quantification for extreme DAF prediction.

Despite its practical significance, existing studies have not adequately addressed the random vibration problem of the train-bridge system (TBS) under non-stationary irregularity excitations resulting from variable vehicle speeds. This study aims to fill this gap by employing the spectral decomposition-based explicit integration method recently proposed by the authors. First, a time-variant dynamic equation of the TBS is established by coupling the governing equations of the bridge and train under the wheel-rail contact assumption. Next, the track irregularity process is expressed in the time domain in terms of two orthogonal stochastic vectors (OSVs), achieved by first decomposing the excitation process in the spatial domain using the spectral representation approach and then applying a space-time transformation. Then, the Duhamel integral is used to derive explicit relationships between the TBS response and the two OSVs, with coefficient matrices efficiently computed using a recursive formulation derived from the Newmark integration scheme. The obtained response-OSV expressions enable direct evaluation of the response statistics and probability densities of the TBS, without extensive numerical integrations or repeated transient analyses typically required in conventional non-stationary random vibration methods. The proposed method is validated through the Monte Carlo simulation of a simply supported beam coupled with a two-dimensional variable-speed train model. Additionally, a comprehensive parametric study is conducted to investigate the effect of different train accelerations on bridge stochastic responses.

By introducing the spectral decomposition-based explicit time-domain method, this paper presents an innovative computational framework for analyzing the non-stationary random vibration problem in a three-dimensional train-bridge system involving multi-variate random track irregularities. First, the time-dependent train-bridge model is formulated by coupling the train and bridge dynamics via a spatial wheel-rail interaction model. Next, the random track irregularities are decomposed through the spectral representation technique, enabling their time-domain discrete characterization in terms of three orthogonal random vectors. Then, an explicit mapping between the system responses and the orthogonal random vectors is constructed by integrating the precise integration method with a finite difference approach, leading to a recursive formulation that facilitates efficient computation of the response coefficient matrices. The obtained explicit response formulation allows straightforward computation of time-frequency response statistics of the train-bridge system, eliminating the need for repetitive time-domain simulations or extensive numerical integrations commonly associated with conventional non-stationary random vibration techniques. Lastly, the pseudo-excitation method and Monte Carlo simulation are adopted to verify the applicability of the proposed method, a comprehensive parametric investigation is also conducted to examine the individual contributions from different track irregularity components on the stochastic dynamic behavior of the train-bridge system.

The narrow magnetic gaps inherent to electromagnetic suspension (EMS) high-speed maglev systems critically constrain operational safety, rendering maglev train-track collision a primary failure mode under intense crosswinds. Accurately assessing this low-probability, high-consequence hazard is challenging because high-dimensional coupled modelling must be combined with tail-region probability estimation. This study establishes a unified cross-platform modelling framework for the wind-maglev train-track-bridge (WMTTB) system. The framework integrates the dimension-reduced probability density evolution equation (DR-PDEE) with an absorbing boundary process (ABP) to express physical clearance limits as a first-passage probability (FPP) metric. Quantitative analyses reveal that crosswinds induce both mean drift and broadening in magnetic gap fluctuations. Within the examined wind velocity range of 0-30 m/s, the guide gap provides the dominant contribution to the computed collision risk. Electromagnets at the windward-side ends are most vulnerable. Neither reducing running speed nor increasing girder depth independently mitigates collision risk substantially; deeper girders yield only marginal FPP reductions in suspension gap fluctuations. By coupling WMTTB co-simulation with DR-PDEE-ABP-based first-passage analysis, the framework provides an efficient tool for quantifying maglev train-track collision risk under stochastic crosswinds.

For high-speed maglev trains, significant research has been dedicated to ensuring their vertical stability and the corresponding vertical electromagnetic control. Their transverse dynamics and stability also present formidable challenges. These challenges are particularly pronounced under the random track irregularities, which can be critically exacerbated by stochastic crosswinds. To address this problem, an optimized Proportional-Derivative (PD) control scheme, which refines the error signal associated with the derivative term, is proposed to enhance the lateral performance. A thorough stochastic dynamic analysis is performed to determine the requisite steady-state current. Furthermore, the influence of the electromagnetic control parameters on the transverse stochastic responses is systematically examined. The results demonstrate that the proposed electromagnetic control scheme effectively mitigates the transverse stochastic responses. The permissible operational range of the control parameters narrows considerably as the mean wind velocity increases, highlighting a critical constraint for the control design in high-wind conditions.

The extreme responses of maglev train-guideway systems (MTGS) subjected to random irregularities are important in evaluating operation safety and ride comfort. However, further improvements are still required in high-dimensional track irregularity simulation and small probability response prediction. This study presents an efficient analysis framework to predict the extreme response of MTGS at small probability levels. First, a spectral energy-driven dimensional stratification method, combined with an optimized sample selection strategy, is proposed to address the high-dimensional challenges in track irregularity simulation effectively. Then, a tail prediction method, which uses the Support Vector Regression (SVR) surrogate model to predict the probability of exceedance (POE) of extreme response, is introduced to enhance the analysis efficiency of small probability events. Finally, the proposed framework is validated by a two-degree-of-freedom (DOF) maglev train and a complex MTGS. The result shows that the spectral energy-driven stratification method reduces the dimensionality of representative point selection by approximately an order of magnitude. The surrogate model method exhibits significant advantages in predicting small-probability extreme responses. Compared to the Monte Carlo method (MCS), the proposed analysis method significantly enhances computational efficiency in obtaining extreme responses at a 10-5 probability level.

Road surface deterioration can significantly amplify bridge vibration responses under traffic loading; however, the evolution of bridge dynamics under progressively deteriorating road conditions remains insufficiently understood. This study aims to investigate the statistical characteristics and evolution of the dynamic amplification factor (DAF) of a tied-arch bridge under realistic traffic conditions over its service life. A three-dimensional finite element (FE) model of the bridge is developed in ANSYS and coupled with a stochastic traffic flow model based on the cellular automaton (CA) method. A time-dependent road surface deterioration model is incorporated to simulate the evolution of road roughness. Extensive numerical simulations are performed, and the statistical distribution of DAF is analyzed using the generalized extreme value (GEV) model. The results show that the GEV distribution provides a more accurate representation of DAF than the conventional normal distribution. The DAF exhibits significant spatial variability across structural components and increases in both magnitude and dispersion with progressive road surface deterioration. Conventional design values are found to underestimate vehicular impact effects under deteriorated conditions. A predictive relationship between road surface roughness and the statistical parameters of DAF is established. The proposed framework enables a more realistic evaluation of bridge dynamic performance under stochastic traffic loading and progressive road degradation, providing valuable insights for long-term bridge design and maintenance.

In this study, nine supercritical utility boilers were selected to measure different forms of ammonia slip at the outlets of various flue gas treatment units. The occurrence patterns, distribution characteristics, variation trends, migration and transformation mechanisms of ammonia slip, as well as the removal and conversion effects of each treatment unit, were systematically analyzed. The results showed that the total ammonia concentration in the flue gas at the stack outlets of all units was generally less than 3 ppm.Significant differences were observed in the speciation distribution of ammonia at the outlets of different units: gaseous ammonia dominated at the SCR outlet, accounting for 94.3%; granular ammonia was predominant at the air preheater outlet, with a proportion of 74.2%. The proportions of gaseous ammonia at the outlets of the dust collector, wet electrostatic precipitator and stack were 68.0%, 61.0% and 61.0% respectively, while granular ammonia accounted for 39.0% at the stack.Total ammonia concentration gradually decreased downstream of the air preheater, with average removal efficiencies of 76.5%, 23.9% and 13.5% achieved by the dry dust collector, desulfurization system and wet electrostatic precipitator, respectively. Approximately 59.5% of gaseous ammonia was converted into granular ammonia in the air preheater, and similar transformation from gaseous to granular ammonia also occurred in the dust collector and desulfurization unit. granular ammonia decreased gradually after the air preheater, and the dust collector exhibited a high removal efficiency of 91.6% for granular ammonia.The removed ammonia was discharged along with fly ash, desulfurization wastewater and gypsum. Therefore, ammonia slip should be controlled at the source by reducing excessive ammonia injection.

The dynamic amplification factor (DAF) is a key parameter for evaluating the dynamic performance of bridges under vehicular loading. Although it has been extensively studied, accurately determining DAF remains challenging due to the combined influence of multiple parameters and the absence of a unified evaluation methodology. In this study, the concept of DAF is revisited, and a new definition based on dynamic displacement difference is proposed to better capture the physical mechanisms governing bridge dynamic responses. A review of existing DAF definitions, calculation methods and design code provisions is first presented. Subsequently, a vehicle-bridge interaction framework is adopted to investigate the dynamic responses of a multi-span simply supported bridge subjected to different vehicle models. Numerical simulations are conducted to evaluate the sensitivity of bridge responses and DAF values to vehicle modelling assumptions and road roughness effects. The results indicate that conventional DAF definitions may overestimate or underestimate dynamic effects under certain conditions, whereas the proposed DAF formulation provides a more consistent and physically meaningful representation of dynamic amplification. This study clarifies the effects of vehicle modelling assumptions and road roughness on DAF evaluation and provides a rational basis for improving DAF assessment in bridge design and analysis.

High-speed maglev trains have no direct contact with the track and rely on the modulation of the electromagnetic force to maintain their posture, which poses significant challenges to safety and comfort in crosswinds compared with the conventional wheel-rail trains. Regarding a high-speed maglev train on a common simply supported girder bridge, the aerodynamic forces of the high-speed maglev train and the bridge girder under different yaw angles are measured in the wind tunnel, with 1:20 scaled models and force balances. The aerodynamic admittances of the high-speed maglev train and the bridge girder are also tested and identified. Effects of the location of the maglev train, the suspension gap and the shape of the head car on the aerodynamic forces are explored. The results show that the aerodynamic lift coefficient of the maglev train increases as the suspension gap increases, with an increment of 0.302 from 2mm gap to 12mm gap. The turbulence affects the trend of the lift coefficient of the maglev train as the yaw angle is larger than 60 degrees. The drag coefficients of the bridge girder in turbulent flow are larger than those in uniform flow, about 142.3% at 90 degrees yaw angle. It is found that the aerodynamic admittances of the bridge girder are larger at lower reduced frequencies at high yaw angles, while they are larger at higher reduced frequencies at lower yaw angles. The side force and lift admittance of the maglev train are approximately the same to a specific reduced frequency as the yaw angle is larger than 60 degrees. The aerodynamic admittances of the maglev train and bridge girder at different yaw angles are influenced by the maglev train location.

The high-speed maglev train is regarded as one of the primary directions for the future advancement of highspeed transportation systems due to its rapid velocity and environmentally friendly characteristics. Nevertheless, the transverse vibration of a high-speed maglev train is significantly challenged by the dual stochastic excitations of the crosswinds and the maglev track irregularities. In this study, a simplified numerical model for the transverse dynamics of an EMS-type high-speed maglev train is established, employing a traditional proportional-derivative (PD) controller. The improved fast stochastic analysis method based on pseudoexcitation method is utilized to solve the stochastic transverse dynamical responses. The damping matrix of the high-speed maglev train exhibits strong non-proportional characteristics as the derivative factor is relatively large. The improved fast stochastic analysis method is capable to effectively enhance the computational efficiency of the stochastic responses. The results demonstrate that the correlation between track irregularities and transverse vibrations is strong. With the increase in mean wind velocity, the stochastic response of the high-speed maglev train, which is relatively low-frequency (below the natural frequency), also increases notably. Crosswinds exceeding 15 m/s and maglev track irregularities have significant impacts on the running smoothness of the high-speed maglev train.

In order to study the impact on the shock effect when a high-speed train passes over a concrete-filled steel tube (CFST) tied-arch bridge, a dynamic load test was carried out in the background of the Qinjiang River Bridge in Qinzhou, Guangxi Province, to test the bridge displacements, accelerations, and dynamic stresses. The bridge finite element model was coupled with a CRH2 train model developed in SIMPACK to perform ANSYS–SIMPACK co-simulation of vehicle–bridge interactions. Model reliability was verified by comparing simulated results with field measurements under matched operating conditions. On this basis, a parametric study was conducted for single-line operation with a mainline spacing of 4.2–5.4 m (0.4 m increments) and train speeds of 80–270 km/h (10 km/h increments), yielding 80 working conditions to evaluate hanger impact responses. The results indicate that the ANSYS–SIMPACK co-simulation provides reliable predictions. Compared with long hangers, short hangers exhibit larger stress impact coefficients. As train speed increases, the hanger impact effect shows a wavelike increasing trend. When the speed approaches 180–200 km/h, the excitation nears the bridge’s dominant natural frequency, and impact effects on bridge components peak, identifying a critical speed range that is more prone to inducing vehicle–bridge resonance; the impact coefficient of the shock effect on both sides of the train is different: the coefficient on the far side of the bridge is about 2 times of that on the near side of the bridge, so when the impact coefficient is regulated, the unevenness of the impact of the shock effect on both sides can be taken into account. Single-line operation can introduce a lateral load bias on the train, and the distance of the train from the center line is positively correlated with the impact size of the shock effect, with the stress impact coefficient of the shock effect on both sides of the bridge and span deflection increasing as the spacing of the main line increases.

This study presents a flood risk assessment of five rural bridges along the monsoon-prone Khar–Mohmand Gat corridor in Northwestern Pakistan using an integrated hydrologic and hydraulic modeling framework. Hydrologic simulations for 50- and 100-year design storms were performed using the Hydrologic Engineering Center’s Hydrologic Modeling System (HEC-HMS), with watershed delineation conducted via Geographic Information Systems (GIS). Calibration was based on regional rainfall data from the Peshawar station using a Soil Conservation Service Curve Number (SCS-CN) of 86 and time of concentration calculated using Kirpich’s method. The resulting hydrographs were used in two-dimensional hydraulic simulations using the Hydrologic Engineering Center’s River Analysis System (HEC-RAS) to evaluate water surface elevations, flow velocities, and Froude numbers at each bridge site. The findings reveal that all bridges can convey peak flows without overtopping under current climatic conditions. However, Bridges 3 to 5 experience near-critical to supercritical flow conditions, with velocities ranging from 3.43 to 4.75 m/s and Froude numbers between 0.92 and 1.04, indicating high vulnerability to local scour. Bridge 2 shows moderate risk, while Bridge 1 faces the least hydraulic stress. The applied modeling framework effectively identifies structures requiring priority intervention and demonstrates a practical methodology for assessing flood risk in ungauged, data-scarce, and semi-arid regions.

The random vibration analysis of beams subjected to train loads is an interesting research subject in the field of civil engineering. Two critical problems in this subject deserving further study are how to reasonably model the random wheel-rail forces and efficiently evaluate the response statistics of beams. This paper aims to contribute to addressing these two problems. First, an appropriate wheel-rail force model that can accurately represent the statistical characteristics of train loads is established, where the wheel-rail forces are modelled as a series of stationary stochastic processes with fixed time delays, and their inherent relation with the track irregularity is established based on the frequency-domain random vibration theory. Next, an approach combining the spectral decomposition and modal superposition techniques is proposed to derive a closed-form response expression for the Euler beams with general boundary conditions, which can be further used to accurately and efficiently evaluate the time-frequency response statistics of beams. In the numerical examples, the evolutionary spectral method and Monte Carlo simulation are used to demonstrate the performance of the proposed method, and the effects of several parameters of the wheel-rail force model on the stochastic responses of the beams are investigated.

Due to the inherent randomness of track irregularity excitation, the dynamic analysis of the train-bridge system (TBS) is essentially a random vibration problem. This paper presents a new computational framework for addressing this problem based on a spectral decomposition-based explicit time-domain method (SD-ETDM) recently proposed by the authors. In this framework, an orthogonal stochastic vector (OSV) obtained via the spectral decomposition approach is used to represent the random track irregularity. Then, an explicit expression is derived for the stochastic responses of the TBS in terms of the OSV by numerical discretization and integration of the coupled TBS equation using the Newmark-beta method. The time-dependent coefficient matrices for these response-OSV expressions are obtained recursively, making the computational framework very convenient for numerical implementation. The obtained response-OSV expressions can be used to efficiently evaluate the time- frequency response statistics of the TBS, avoiding tedious integral calculations or repeated time-history analyses required in traditional random vibration methods. Finally, a three-span continuous beam coupled with a twodimensional train model is used for numerical verification, a comparison with other existing methods indicates that the SD-ETDM is an accurate, efficient and practical method for non-stationary random vibration analysis of the TBS.

Bridge structures are typically elevated above the ground, with lower temperatures on the bridge deck. Accumulated snow particles can drift under the influence of strong winds, posing a substantial threat to traffic safety. The study of wind-induced snow hazards on bridge structures is significant for ensuring the safe operation of transportation in high-latitude and cold regions. Due to the complex mechanisms of snow motion, many unresolved issues remain. This study focuses on a large-span highway box girder bridge and investigates wind-induced snow redistribution on the girder surface using a three-dimensional CFD approach. The nondimensional wind-induced redistribution coefficients of the snow particles were obtained. In addition, a detailed analysis of the mechanisms behind the wind-induced snow redistribution was conducted from a flow field perspective. The results indicate that auxiliary components of the bridge model, such as railings and sidewalk pavement layers, directly influence the redistribution of particles on their surfaces. Furthermore, scaled model tests were conducted in a wind tunnel to validate the accuracy of the numerical simulations. Polyethylene particles were used to simulate snow particles. Moreover, to reveal the mechanism of how wind attack angles affect, the redistribution of particles and flow field information were analyzed under different wind attack angles (0 degrees and +/- 3 degrees). The results demonstrate that different wind attack angles have a significant impact, particularly on the windward side of the bridge. The erosion extent of snow particles under the negative wind attack angle is higher than that under the 0 degrees attack angle, and the erosion extents under both the negative and 0 degrees attack angles are higher than that under the positive attack angle. The average and maximum differences in the nondimensional distribution coefficients of particles among the three conditions reach 34.5% and 77.8%, respectively. These findings not only provide data support for practical engineering applications but also offer methods and insights for further research on wind-snow interactions on bridge structures.

Civil engineering structures frequently encounter a variety of non-Gaussian random excitations throughout their service life. Traditional random vibration methods, however, generally assume Gaussian inputs and focus on estimating limited statistical moments rather than complete probability distributions of structural responses. This study proposes a novel computational framework, called Karhunen-Loe`ve decomposition-based explicit integration method (KLD-EIM), for non-stationary non-Gaussian random vibration analysis of multi-degree-offreedom structural systems. First, the proposed method employs an iterative KLD algorithm to generate nonGaussian orthogonal random vectors (ORVs), discretely representing random excitation processes statistically characterized by their time-dependent covariance and non-Gaussian distribution functions. Then, structural responses are explicitly formulated in terms of the non-Gaussian ORVs through employing the precise integration approach incorporating a finite difference formula, where the associated coefficient matrices are efficiently determined through a series of recursive computation. The resulting response-ORVs expression is regarded as an explicit surrogate model, through which a large number of structural response samples can be efficiently generated without repeated time-history integrations, enabling accurate estimation of both higher-order statistical moments and complete probability distributions of structural response quantities through statistical postprocessing. Finally, case studies are conducted on a simply supported beam and a three-span continuous beam under moving non-Gaussian forces with different covariance and distribution functions, the Monte Carlo simulation is used for numerical validation of the proposed KLD-EIM.

In order to study the instability characteristics of interlayer rock strata (IRS) in shallow buried close-distance coal seams under insufficient mining areas, based on the background of interval mining under goaf in Nanliang Coal Mine, this paper studies the instability characteristics of interlayer strata in interval mining under goaf by means of similar simulation, numerical simulation, and field measurement. The results indicated that the first weighting interval of the main roof during mining in the lower coal seam was 49 m, while small and large periodic weightings with intervals of 10–14 m and 15–19 m were identified. During periodic weighting, the support resistance ranged from 6813 to 10,935 kN, with a dynamic load factor of 1.07–1.74, and the peak abutment pressure in front of the working face was 5.85–9.85 MPa. The mining under the interval coal pillar (ICP) was the ‘stress increase zone’, and the mining under the temporary coal pillars (TCPs) and the interval goaf was the ‘stress release zone’. During the working face mining out of the ICP, the support resistance reached 10,934 kN, the dynamic load factor reached 1.74, and the abutment pressure (AP) reached 9.85 MPa, which was 60% higher than the AP mining under the “stress release zone”. Analysis suggests that the cutting instability of the IRS was the root cause of the increased AP in the working face of the lower coal seam. A numerical simulation was performed to verify the instability characteristics of the IRS in the interval goaf. The relationship between support strength and roof subsidence during the period of the working face leaving the coal pillar was established. A dynamic pressure prevention method involving pre-splitting and pressure relief of the ICP was proposed and yields superior field application performance. The findings of the study provide a reference for rock strata control during mining under the subcritical mining area in shallow and closely spaced coal seams.

Among large-span bridges, arch bridges have relatively high stiffness, which may lead to large dynamic amplification factors (DAFs). The DAFs suggested by current codes mostly originate from common simply supported beam bridges. Previous DAF studies on dynamic vehicle–bridge solutions for arch bridges have mainly focused on one or two side-by-side vehicles. However, complex traffic flows with randomness rather than one or two vehicles act on bridges. DAFs that consider the effects of random traffic flows have not previously been reported. In this study, a random traffic–bridge vibration solution was established to explore the DAFs of arch bridge components. The randomness of the traffic parameters and road roughness was explored. The randomness of the external excitation, including traffic flow and road roughness, resulted in random dynamic responses and DAFs of the arch bridge components. Normal distributions could be used to fit the DAF distributions of each arch bridge component under random vehicle spacing, weight, speed, and road roughness. Under random traffic parameters, the coefficients of variation of the DAFs exceeded the 5% accepted level. The shortest suspenders were more sensitive to the randomness of the traffic flow parameters. Both the mean and coefficient of variation of the DAFs increased with worsening of the road conditions. The influence of the randomness of the road roughness on the DAFs of arch bridges must be considered, particularly for the shortest suspender. The 95% upper confidence limits of the DAFs for all components may be greater than the suggested values in the code.