Jianfeng Mao

High-speed railway track–bridge systems (HSRTBSs) in near-fault high-seismicity regions face combined threats from pulse-type seismic excitations, vertical earthquake components and track defects, which may trigger structural damage and deterioration of track regularity. This paper establishes refined OpenSEES coupled numerical models for a typical 32 m simply supported girder bridge equipped with CRTS II slab ballastless track, considering both conventional spherical steel bearings and friction pendulum bearings (FPBs). Nonlinear time-history analyses are performed with near-fault pulse-like and far-field non-pulse ground motions to explore the influences of peak ground acceleration (PGA), vertical-to-horizontal acceleration ratio (αVH), and CA mortar void length. The results demonstrate hierarchical controlling effects of these parameters. PGA dominates the overall seismic response; sliding layer damage follows the sensitivity sequence PGA > αVH > CA mortar void, whereas post-earthquake traffic capacity degradation obeys PGA > CA mortar void > αVH. Near-fault pulse-like ground motions produce more severe structural damage compared with far-field inputs. FPB isolation yields a maximum pier-top seismic reduction ratio of 86.73% and effectively mitigates structural deformation, but cannot eliminate track irregularity originating from CA mortar void defects. Conditional on the 0.2 g seismic level and the given structural configuration adopted in this study, αVH = 0.65 and the 1.95 m critical CA mortar void length for longitudinal track constraint failure can serve as reference values, though they are not universally applicable for all track–bridge systems. This work provides insights for seismic design, CA mortar defect remediation and post-earthquake traffic assessment of near-fault isolated HSRTBSs.

Crosswind is one of the key factors threatening the safety and stability of high-speed train operations. Due to the highly stochastic nature of natural wind fluctuations, traditional analysis methods struggle to accurately capture the full probabilistic information of system responses. This paper introduces the probability density evolution method (PDEM) to conduct an in-depth study on the stochastic vibration response of a high-speed vehicle–bridge coupled system under crosswind. First, a dynamic analysis model for the vehicle–bridge coupling system considering the randomness of aerodynamic loads is established. The stochastic wind load acting on the vehicle–bridge system is simulated using a non-stationary assumption, where the wind speed process is described as a spatially varying stationary Gaussian random field. Based on this, the probability density evolution method is applied by introducing random variables that characterize the essential randomness of the wind field, and a generalized probability density evolution equation is constructed to relate the system state quantities to these random variables. Using the multiple-distribution point selection method, a representative set of points is selected, ultimately obtaining the probability density function (PDF) and its evolution process of the stochastic dynamic response of the system. Taking a long-span cable-stayed bridge as an example, numerical simulations accurately reveal the complete evolution of the PDF of the system response in the time domain under crosswind. This method provides a more comprehensive and accurate theoretical tool and analytical approach for assessing the operational safety risks of high-speed trains crossing long-span bridges in crosswind environments.

The growing demand for high-capacity rail transit has led to the adoption of lighter and faster trains operating on long-span bridges, where strong crosswind-induced train–bridge interactions present considerable risks to operational safety. This study develops a stochastic analysis framework to assess the aerodynamic performance of train–bridge systems under crosswinds by integrating computational fluid dynamics with a coupled vibration approach. A three-dimensional interaction model is established using the k-omega shear-stress transport turbulence model to resolve complex flow fields and unsteady aerodynamic loads under high-speed conditions. The probability density evolution method is employed to solve the stochastic dynamic equations, quantifying system vibrations and evaluating running safety indices. Results reveal that the lateral response of the train–bridge system increases exponentially with wind speed. Owing to aerodynamic coupling between the moving train and crosswinds, both vehicle and bridge aerodynamic coefficients show significant sensitivity to increasing Reynolds numbers. Changes in cross-sectional parameters can cause force coefficient amplitudes to vary by over 40% compared to baseline values. Large bridge pylons induce notable aerodynamic shielding effects, where spatial pressure gradients between windward and leeward sides reduce the resultant aerodynamic forces across the coupled system by up to 50%. These findings offer valuable insight for the aerodynamic design and safe operation of long-span rail bridges in wind-prone regions.

China’s high-speed railway (HSR) network relies heavily on bridge structures to ensure track regularity, with many lines crossing seismically active near-fault zones. Near-fault ground motions are characterized by significant vertical components (VGMs), which challenge conventional seismic design practices. Although seismic isolation techniques are widely adopted, the effects of VGMs on the dynamic response and damage mechanisms of HSR track–bridge systems remain insufficiently studied. To address this gap, this study develops a refined finite element model (FEM) in OpenSEES that integrates CRTS II slab ballastless tracks, bridge structures, and friction pendulum bearing (FPB). Using nonlinear time-history analyses, the research systematically investigates structural responses and damage degrees under different ratios of vertical-to-horizontal peak ground acceleration (α_VH) and multiple seismic intensity levels (frequent, design, and rare earthquakes). Key findings reveal that α_VH values in near-fault regions frequently range between 0.5 and 1.5, often exceeding current design code specifications. The impact of VGMs intensifies with seismic intensity: negligible under frequent earthquakes but significantly amplifying damage to piers, bearings, and track interlayer components (e.g., sliding layers and CA mortar layers) during design and rare earthquakes. While seismic isolation effectively mitigates structural responses through energy dissipation by bearings, it may increase sliding layer displacements and lead to bearing failure under rare earthquakes. Based on these insights, tiered α_VH values are recommended for seismic design: 0.65 for frequent, 0.9 for design, and 1.2 for rare earthquakes. These findings provide critical references for the seismic design of HSR infrastructure in near-fault regions.

China's high-speed railway (HSR) network extensively utilizes bridge structures to maintain track regularity, with numerous lines extending across active fault zones that present substantial seismic hazards. Near-fault ground motions exhibit distinctive characteristics, particularly strong vertical components, that differ significantly from far-field earthquakes. Seismic isolation design is essential for improving the seismic performance of near-fault HSR track-bridge systems, but research on the influence of vertical ground motions (VGMs) on their dynamic response and damage mechanisms remains inadequate. To address this, this study developed a refined finite element model (FEM) in OpenSEES incorporating CRTS II slab ballastless tracks, bridges, and friction pendulum bearings. Nonlinear time-history analyses were conducted to investigate structural responses and damage degrees under varying ratios of vertical-to-horizontal peak ground acceleration (α_VH) and seismic intensities (frequent, design, and rare earthquakes). The results demonstrate that α_VH values in near-fault regions are densely distributed between 0.5–1.5, exceeding the values specified in the design codes. The impact of VGMs intensifies with seismic intensity: negligible under frequent earthquakes but significantly amplifying damage to piers, bearings, and track interlayer components (e.g., sliding layers and CA mortar layers) during design and rare earthquakes. While seismic isolation effectively mitigates structural responses through energy dissipation by bearings, it may increase sliding layer displacements and lead to bearing failure under rare earthquakes. Based on damage control and economic considerations, tiered α_VH recommendations are proposed: 0.67 for frequent, 0.9 for design, and 1.2 for rare earthquakes. These findings provide critical references for the seismic design of HSR infrastructure in near-fault regions.

The operational safety of high-speed maglev train–bridge stochastic systems is inherently governed by the critical parameters of the coupled train–bridge interaction. To quantify the impact of these parameters on stochastic vibration responses, this study presents an analytical framework based on the probability density evolution method (PDEM) for systematically evaluating key system parameters and assessing safety performance. The primary contribution of this work is the development of a stochastic vibration model for the maglev train–bridge coupling system, explicitly capturing dynamic interactions under random excitation, including track irregularities and structural variability. By leveraging PDEM, the proposed methodology characterizes the transient evolution of the system’s probability density function, enabling high-fidelity statistical analysis of critical dynamic responses for both the train and bridge. A rigorous parametric investigation reveals the sensitivity of system performance to key design variables, such as suspension stiffness, bridge span configurations, and track irregularity parameters. Through stochastic simulations across diverse operational scenarios, the framework quantifies safety thresholds and evaluates compliance with dynamic stability requirements under uncertainty. The results demonstrate that the PDEM-based approach not only identifies dominant parameters influencing vibration mitigation but also provides a robust quantitative foundation for operational safety assessment. This study advances the understanding of the stochastic dynamics of maglev systems, bridging theoretical innovation with practical engineering applications.

The safety and smoothness of high-speed train operations, particularly through bridge zones, are crucial for ensuring operational stability and comfort. Regions such as large-span continuous and simply supported girder bridges, specifically the beam joint regions and beam ends, pose significant challenges due to the dynamic interaction between trains, tracks, and bridges. This study develops a refined coupled model of the train-track-sleeper lifting device-bridge (TTSB) system to simulate the dynamic responses of trains passing through large-displacement sleeper-lifting device (LSD) zones. The model incorporates the effect of axial displacement changes caused by temperature variations at the bridge girder ends, which influence the spacing of fasteners. The refined modeling approach improves both the accuracy and computational efficiency of dynamic simulations. The methodology employs a numerical model to simulate train dynamics at various speeds (250–425 km/h) and fastener spacing ranges (0.35–0.85 m). The study examines key parameters, including vertical and lateral displacements, accelerations, wheel load reduction rates, and derailment coefficients. The results show that increased fastener spacing leads to significant changes in vertical and lateral displacement and acceleration (up to 45.8% for Fastener A and 43.4% for Fastener B). Additionally, the wheel load reduction rate and derailment coefficient exhibit fluctuations with varying fastener spacing, highlighting safety implications. These findings validate the model’s effectiveness and offer insights for optimizing track structure design in high-speed railways, improving safety and operational stability.