Railway noise barriers are an essential piece of infrastructure for reducing noise propagation. However, these barriers experience aerodynamic loads generated by high-speed trains, leading to dynamic effects that may compromise their fatigue capacity. The most common structural design for railway noise barriers consists of vertical configurations of posts and panels. However, there have been few dynamic analyses of steel post/wood panel noise barriers under train-induced aerodynamic loads. This study used dynamic finite element analysis to assess the dynamic behavior of such noise barriers. Analysis of a 40-m-long noise barrier model and a triangular simplified load model, the latter of which effectively represented the detailed aerodynamic load, were first used to establish the model and input of the moving load during dynamic simulation. Then, the effects of different parameters on the dynamic response of the noise barrier were evaluated, including the damping ratio, the profile of the steel post, the span length of the panel, the barrier height, and the train speed. Gray relational analysis indicated that barrier height exhibited the highest correlations with the dynamic responses, followed by train speed, post profile, span length, and damping ratio. A reduction in the natural frequency and an increase in the train speed result in a higher peak response and more pronounced fluctuations between the nose and tail waves. The dynamic amplification factor (DAF) was found to be related to both the natural frequency and train speed. A model was proposed showing that the DAF significantly increases as the square of the natural frequency decreases and the cube of the train speed rises.

Train passages generate aerodynamic loads that induces dynamic responses in railway noise barriers and may compromise structural integrity. However, field measurements capturing both aerodynamic pressure and structural response, especially for wooden barriers, remain scarce. This study presents a field investigation of train-induced aerodynamic excitation and response in a noise barrier. A monitoring campaign collected pressure and response signals from four train types operating at speeds of 150–200 km/h. The pressure signals exhibited characteristic positive and negative transitions at the train nose and tail, with tail-wave amplitudes approximately 35%–55% of the nose-wave. Steeper noses produced higher pressure amplitudes and shorter nose-wave peak intervals, while train length showed limited influence on nose-wave pressure. Shape coefficients identified for Swedish trains ranged from 0.87 to 1.28, indicating that Eurocode models underestimate pressure by 2.75%–25.44%, however, its vertical distribution model agreed well with measurements. Spectral analysis revealed that nose-wave energy is concentrated in the 2–4 Hz range, with additional contributions in the 4–6 Hz. While pressure signals showed intermediate peaks associated with inter-car gaps, the response signals exhibited more complex fluctuations between nose and tail waves. Stress range and displacement exhibited higher sensitivity to train speed than pressure, increasing approximately with the cube of speed. Increasing speed from 155 to 200 km/h raised the load frequency by about 24%, shifting load energy to higher frequencies and enhancing dynamic amplification. Steeper noses induced larger responses due to higher load frequency contents, whereas train length had a negligible effect on dynamic response.

The passage of trains by railway noise barriers induces vibrations that may affect their fatigue performance and reduce their service life. However, long-term field monitoring of noise barriers under complex environmental and operation conditions remains rare. This study develops an interpretable machine learning (ML) framework to investigate the aerodynamic pressure and dynamic behaviors of noise barriers based on a nine-month long-term field monitoring campaign, yielding 12810 train runs over 105 valid days. Input variables include train type, speed, temperature, wind speed and direction, relative humidity, and air pressure, while the target responses cover train-induced aerodynamic pressure, stress near the base of the steel post, and displacement at the post top. Eight ML models, including four traditional and four ensemble algorithms, were used and systematically compared to evaluate their predictive capabilities and robustness. Ensemble models, particularly Gradient Boosting Decision Tree (GBDT), Light Gradient Boosting Machine (LGBM), and Extreme Gradient Boosting (XGBoost), achieved the best predictive performance, with R2 values exceeding 0.935 for stress and displacement, and 0.895 for pressure. XGBoost, offering a strong balance of predictive accuracy and computational efficiency, was selected for SHapley Additive exPlanations (SHAP)-based interpretability analysis to uncover the physical relationships behind the data-driven predictions. Results reveal that aerodynamic pressure was the most challenging response to predict, given its higher sensitivity to turbulent airflow and environmental fluctuations, whereas stress and displacement exhibited more stable and predictable patterns. SHAP analysis identified train speed and type as the most influential factors across all responses. While environmental factors had comparatively lower influence, temperature and instantaneous wind direction consistently showed higher importance among them. Relative humidity has a moderate effect on aerodynamic pressure but a minor impact on dynamic behavior. Air pressure and wind speed exhibit limited influence on all outputs. These findings highlight the novelty and effectiveness of integrating long-term monitoring data, ML methods, and SHAP-based interpretability, offering new insights into the dynamic behavior of railway noise barriers.

Recent measurement campaigns under the Shift2Rail In2Track2 and In2Track3 projects have shown that damping levels in existing railway bridges often exceed the conservative values prescribed in EN 1991–2. These studies also revealed significant scatter among similar structures, reflecting the complex influence of soil–structure interaction, vibration amplitude, and estimation methods. Given the critical role of damping in bridge dynamics, particularly near resonance, and in light of the challenges outlined above, an update of the Eurocode provisions is needed. In this context, the European Union Agency for Railways (ERA) and Europe’s Rail Joint Undertaking (EU-Rail) launched the InBridge4EU project to improve the representation of damping in the Eurocode framework. This paper presents its main outcomes based on over 1150 traffic-induced acceleration records from nearly 90 bridges across five European countries. The analysis resulted in the first proposal since the 1990s for revised damping curves and an updated bridge typology classification that better reflects real-world variability. These results provide a more robust, data-driven basis for the forthcoming revision of EN 1991–2, enabling less conservative yet safe design approaches and contributing to more efficient and reliable dynamic assessment of railway bridges in the European network. 

High-speed railway is expanding drastically in Sweden, necessitating new technology, and improve-ments of existing structures. End-shield bridges are a common and under-tested bridge type inSweden. Their dynamic performance is significantly impacted by their boundary conditions due to thesoil–structure interaction (SSI) and their large masses cantilevering beyond the footings. A specificend-shield bridge was tested under low (5 kN) and high (20kN) amplitude-forced hydraulic excitationfor a wide range of frequencies. Several train passages for typical passenger trains,‘X62’, were meas-ured with the same experimental setup. The results were analysed to isolate the significant modes ofthe system and the natural frequencies. A full 3D numerical model was calibrated and updated inAbaqus, along with a brief sensitivity study to determine the most influential parameters. Finally, theresponse to passing trains and Eurocode design HSLM trains was calculated. The experimental studyshowed that higher loading amplitudes resulted in higher damping and lower natural frequencies. Thenumerical analysis showed that for this bridge type the SSI cannot be neglected and can be success-fully introduced in the model.

In this paper, the performance of a single-span railway bridge with integrated retaining walls during high-speed train passage is investigated by considering different uncertainties originating from modeling assumptions and measurement processes. For this purpose, a single-span railway bridge is equipped with numerous accelerometers and is excited using a hydraulic actuator across different frequency ranges. A comprehensive 3D model of the bridge and the surrounding soils is created in Abaqus. Different sets of material properties for concrete and soil components are derived by converging the frequencies and damping ratios of the first three structural modes, using both the Error-Domain Model Falsification (EDMF) and Residual Minimization (RM) methods. These material properties are subsequently utilized in high-speed train passage analysis, and the results are compared.

Increasing the operating speed of trains in modern railway networks can induce greater actions on the infrastructure than was previously the case. This is due, in particular, to the occurrence of the resonance phenomenon in railway bridges, which is the focus of this paper and was not traditionally considered as a concern. In this context, the vibrations experienced by bridges, both vertical accelerations and displacements, are limited by design regulations to ensure that the safety of train passages over bridges and the comfort of passengers are guaranteed. However, previous studies have shown that the conventional dynamic design methods do not always result in conservative designs, nor is the achieved safety always consistent. Therefore, a probabilistic approach is adopted in this study to optimize the cross-section properties of various railway bridges in a wide design range including section types, span lengths, and number of spans. For this purpose, an iterative line search-based optimization problem is formulated to minimize the thickness of the cross-sections under consideration and consequently the linear mass of the bridges. Meanwhile, the associated failure probabilities of the above dynamic limit states are constrained to be less than the desired level of safety by incorporating them into the optimization constraint. In this regard, First-Order Reliability Method (FORM) is adopted to perform reliability analyses. Thus, the obtained results are presented in the form of design curves that may assist designers to select minimum cross-section dimensions satisfying the desired level of safety in terms of dynamic limit states. This objective can be achieved using the proposed design curves without the need to construct associated complex computational models and perform computationally expensive dynamic analyses.

The increasing speeds of modern trains lead to excessive vibrations on the bridges, which have the potential to destabilize the ballast particles. The occurrence of this phenomenon not only increases the track maintenance cost, but can also disrupt the load path from the rail level to the bridge deck, posing a risk to the train running safety. The design regulations indirectly control this limit-state by restricting the vertical acceleration of the bridge deck. The assessments pertaining to this purpose often neglect the soil-structure interaction (SSI) effects considering that as a conservative assumption. Such effects can positively contribute by increasing the system damping, but they can also increase the bridge flexibility making it more susceptible to vibrations due to reduction on critical speed. Therefore, this study investigates the influence of considering/disregarding SSI effects on the ballast destabilization phenomenon using a probabilistic methodology. The results are classified based on the maximum permissible train speeds and the bridge span length. Due to the high computational costs of the reliability analyses, the associated limit-state is approximated by an ensemble of classification-based surrogate models using the stack-generalization concept. Subsequently, the upper/lower bounds of the failure probability in the presence of SSI effects are compared with those obtained for simply-supported bridges. It is pointed out that neglecting SSI effects for shorter span bridges may lead to an underestimation of system safety. For longer span bridges, however, this may lead to an overestimation of safety, which means that a non-conservative system can be designed.

As far as the authors are aware, the threshold for vertical acceleration of the bridge deck was chosen based on the assumption that the induced dynamic loads would overcome gravity at higher accelerations, resulting in loss of contact between wheels and rail; however, the previous studies do not support this hypothesis. Considering these inconsistencies, a better understanding of the simplified design criteria is essential before conducting further studies suchas the calibration of partial safety factors. Therefore, this study considers a set of representative design scenarios to statistically compare wheel-rail contact loss with other criteria that can bederived from moving load models, such as vertical accelerations and bridge deck deflections. Based on the analyzes performed, deflection seems to be a better criterion than acceleration to control the running safety limit-state; although the results presented do not necessarily show avery strong correlation between these two criteria. Therefore, the k-means clustering approach isused together with 5% lower quantiles of the collected data to propose potential new thresholds. It should be noted that due to the limited number of analyzes, the approach presented in this study can be considered as a possible framework for further updates of the current design method rather than drawing general conclusions.

The assessment of running safety of railway bridges is conditioned by the Eurocode EN 1990 A2 by limiting vertical deck acceleration. On ballastless track bridges, this value is 5 m/s2. The background for this value is not clear, and it is believed that it originates in the application of an arbitrary safety factor of 2 on accelerations around 1 g to avoid loss of wheel–rail contact. However, studies show that the level of acceleration may not be directly related to the occurrence of derailment. In this work, this idea is expanded by assessing both vertical and lateral dynamics, comparing acceleration values with the Unloading and Nadal derailment criteria. The parametric study is comprised of a set of five representative single-track slab bridges with spans between 10 m and 30 m with two levels of track irregularities, corresponding to a well-maintained track and an Alert limit situation. A three-dimensional articulated FE model based on the load properties of the EN 1991-2 High-Speed Load Model A is presented, crossing the bridges at running speeds from 150 km/h to 400 km/h. Despite the complexity of the models, a large amount (1461) of full 3D train–track–bridge interaction dynamic analyses are performed, to produce a data set representative of the phenomenon. Results show a weak correlation between the criteria and deck acceleration (maximum r2 of 0.47 for Unloading and 0.15 for Nadal). Additionally, track quality is shown to be a more conditioning factor for derailment when compared to resonance. This work contributes to discussing the thesis of using deck acceleration as an indicator of running safety, considering lateral dynamics.