Search Results (377)

Train-induced vibrations pose a significant threat to foundation pit slopes adjacent to railways during parallel construction or line renovation projects. To address this issue, this paper proposes a periodic steel-sheet pile barrier for vibration mitigation in narrow construction sites. Firstly, field tests were conducted along the Qinbei Railway in China. The acceleration time history and dominant frequency (27.6 Hz) of ground vibrations were obtained. Secondly, based on periodic structure theory, the dispersion relations and band-gap characteristics of periodic steel-sheet piles were analyzed using the finite element method. Parametric studies were then performed to investigate the effects of key factors, including periodic constants, pile spacing and pile count per unit cell, and construction deviations, on the band-gap boundaries and width. Subsequently, frequency-domain, time-domain, and slope stability analyses were carried out to evaluate the isolation performance. The results show that the optimized barrier, with parameters of a = 1.6 m, D = 0.1 m, n₁ = n₂ = 4, and L = 2S, reduced the peak acceleration by 70% and achieved a vibration reduction of up to 88% at the dominant frequency. Furthermore, slope stability analysis revealed that the barrier increased the factor of safety from 1.16 to 1.46, exceeding the code-required minimum of 1.2–1.3. This study provides a potentially cost-effective and construction-friendly solution for protecting temporary foundation pit slopes from train-induced vibrations in railway-adjacent areas. Full article

Train-induced ground vibrations can propagate into pile foundations, potentially causing undesirable vibration in nearby buildings, laboratories housing vibration-sensitive equipment, and manufacturing facilities for high-precision processes. This paper presents an innovative method for predicting building vibration from free-field ground vibration measurements at locations away from the tracks during train pass-bys. The proposed method accounts for site-specific soil profiles and train-soil-pile-structure interactions and is implemented in four steps. In Step 1, train-induced vibration transmission into the ground is estimated using an axisymmetric finite element model that simulates wave propagation through layered soils from the tracks to free-field ground locations. Step 2 estimates free pile head vibration using a three-dimensional finite-element model that captures the ground-borne transmission of track inputs through soil layers to the pile. Step 3 estimates vibration at the junction of the pile head and depot column base using a finite-element model to estimate the pile head impedance and an analytical impedance model for the depot structures supported by the pile. In Step 4, estimates of column-base vibration that transmits into over-track buildings are compared to measured column-base vibration levels obtained during train pass-bys. The method was applied at a metro depot in China, where tracks were in close proximity to columns supporting over-track buildings. Ground and column base vibration levels were measured during multiple train pass-bys. The estimated vibration levels at the base of depot columns closely agreed with the measured vibration levels at the columns during six-car train pass-bys. It demonstrated the potential effectiveness of this hybrid method for assessing vibration transmission into structures atop existing railway tracks. By integrating field measurements, finite element simulations, and analytical impedance models, the proposed hybrid method provides a framework for evaluating the transmission of the train-induced vibration to nearby building structures. Full article

With the integrated development of high-speed railways and urban underground rail transit, large high-speed railway station buildings are often seamlessly connected or even co-constructed with subway structures, forming a complex structural system that integrates high-speed rail, subway, and station buildings. To investigate the dynamic performance of such “ integrated station-bridge” station buildings constructed with subways, this paper takes Yichang North Station as an engineering case study and examines its vertical dynamic characteristics under multi-source train-induced loads. The station adopts a structural configuration where the station tracks are fully integrated with the station building, while the main lines are separated from it. To accurately simulate the entire process of train operation, this study established a refined “train-track-station” spatially coupled dynamics model that incorporates high-speed and subway trains, tracks, and the station structure. Based on this model, various operational scenarios were systematically analyzed, including high-speed trains passing at different speeds, parallel operation of multiple train lines, and combined operation of high-speed and subway trains. The results demonstrate that, when single or multiple high-speed train lines pass through the station at the design entry speed of 80 km/h, the vertical vibration acceleration of the elevated waiting level meets human comfort standards. The train-induced vibration response is transmitted and superimposed along the “column–beam–slab” path, resulting in localized acceleration peaks at the mid-span regions of beams and slabs directly above the tracks. Second, the impact of subway train operation alone on the vibration of the elevated level is significantly weaker than that of high-speed trains. Furthermore, under combined high-speed and subway train operations, the additional vibration contribution from subway trains shows a decreasing trend as the number of simultaneously operating high-speed train lines increases. The findings of this study validate the effectiveness of the structural design of Yichang North Station in terms of train operational safety and passenger waiting comfort. The revealed patterns of multi-source vibration transmission and superposition can provide important theoretical and numerical references for the dynamic optimization design and vibration control of similar integrated transportation hub structures. Full article

The wheel–rail impact effect is prominent in the low vibration track (LVT) in the curved sections of heavy-haul railways, where fastener failure and the support block hanging void are prone to occurring. To investigate the impact of these issues on the dynamic characteristics of the vehicle–track coupled system, this study establishes a coupled dynamics model of a heavy-haul train and LVT, taking into account the topological relationships of vehicle components, multipoint wheel–rail contact, and track irregularities. Comparative analyses are conducted to evaluate the effects of the location, quantity, and failure degree of fastener failure and support block hanging voids on running safety and stability. The results show that (1) compared to the normal condition, fastener failure and support block hanging voids lead to varying degrees of increases in response indicators, thereby intensifying the wheel–rail impact; (2) bilateral failure exhibits more pronounced dynamic responses than unilateral failure, and when the number of failed fasteners or hanging voids exceeds one, the maximum wheel load reduction rate increases significantly; (3) as the gap of the hanging void increases, the dynamic response also increases, and when the gap reaches approximately 3 mm, the support block can be considered fully suspended; and (4) comprehensive analysis indicates that fastener failure poses a greater threat to running safety than support block hanging voids and thus warrants greater attention in practical engineering applications. This study provides theoretical support for the maintenance and repair of heavy-haul railways. Full article

The railway track system is a complex assembly of rails, sleepers, and fastenings designed to ensure operational stability and safety. Within this framework, rail pads play a critical role in load transfer, vibration attenuation, and noise control. This study provides a comprehensive bibliometric analysis of research on railway components published between 2015 and 2024, based on 288 documents retrieved from Scopus, Elicit, and Web of Science. Publication trends reveal a steady increase in research output over the study period, primarily driven by Spain and China. Keyword co-occurrence analysis yielded 51 keywords organized into seven thematic clusters, with the highest frequency terms being “rail pad”, “noise”, “dynamic property”, and “MTHDRP”. The analysis highlights a significant focus on materials such as TPEs, EPDM, and EVA, with static preload and stiffness identified as the most scrutinized performance factors. Findings indicate a clear thematic shift from traditional field testing toward advanced material science and sensor-integrated monitoring technologies. Ultimately, this review outlines future research trajectories emphasizing sustainability, smart sensor integration, and predictive maintenance. By synthesizing a decade of academic contributions, this study serves as a strategic roadmap for optimizing the long-term durability and efficiency of modern railway infrastructure. Full article

The gap dimension of a railway switch machine is a critical physical quantity for determining the locking status of railway turnouts. Under operating conditions characterized by heavy oil contamination, complex illumination, and equipment vibration, existing visual measurement methods often struggle to maintain stability and achieve sub-pixel precision. To address this issue, this paper proposes a gap measurement method based on the fusion of vision and geometric features (G-VFM). The method first utilizes a confidence-aware optimized YOLOv8 model to achieve robust localization of the gap region. Subsequently, an improved multi-channel U-Net is employed to extract soft-edge probability maps, based on which a 20-dimensional structured geometric descriptor is constructed. Finally, visual semantic features and geometric priors are fused for regression through an R34-Fusion two-stream residual network, and systematic errors are corrected using a weighted Huber loss combined with a piecewise linear calibration strategy. Test results on a constructed field dataset show that the proposed method achieves a Mean Absolute Error (MAE) of 0.0076 mm and a maximum error of 0.0193 mm. It achieves a 100% pass rate under an industrial tolerance of 0.02 mm, with an end-to-end inference time of 52.23 ms (~19.15 FPS), balancing both precision and efficiency. Further tests on illumination degradation, noise interference, and cross-batch evaluations indicate that the method maintains relatively stable performance across various complex scenarios. However, performance decreases significantly under extremely low-light conditions, suggesting that actual deployment may require integration with active lighting or multi-sensor fusion to ensure system reliability across all working conditions. Overall, this method achieves high-precision gap measurement under current experimental conditions and provides a feasible solution for vision-based switch machine status monitoring. Full article

Few-shot fault diagnosis of intelligent high-speed train components remains challenging because fault samples are scarce and highly imbalanced. To address this issue, this paper proposes MR-DDIM, a class-conditional diffusion-based data augmentation framework for generating high-fidelity fault vibration signals from limited labeled data. A WT-UNet denoising backbone is developed by combining one-dimensional wavelet convolution with Feature-Wise Linear Modulation (FiLM) to capture multiscale time–frequency structures and enable class-controllable generation. To improve training stability and spectral fidelity, log-σ regularization and a multi-resolution STFT consistency loss are introduced into the optimization process. In addition, this paper proposed the multi-resolution spectral correlation coefficient (MR-SCC) and class-intrinsic maximum mean discrepancy (cMMD) to evaluate generation quality from spectral and distributional perspectives. Experiments on the BJTU-RAO datasets show that the proposed method can generate fault samples with high spectral consistency and reasonable intra-class diversity, thereby improving the robustness of downstream few-shot fault diagnosis. The results indicate that MR-DDIM provides an effective data augmentation solution for intelligent fault diagnosis in high-speed railway systems. Full article

Traditional rolling stock dynamics studies often rely on simplified 2D models, limiting stability predictions for innovative designs at high speeds. This work proposes a refined spatial multi-mass mathematical model that accounts for nonlinear interrelationships and the superposition of deterministic and random disturbances. This approach enables a detailed reproduction of components with variable stiffness and diagonal connections, identifying critical dependencies inaccessible to standard analytical methods. The model describes spatial vibrations using linear differential equations, considering vertical and horizontal perturbations to simulate real-world operational conditions. To ensure accuracy, the simulation results were validated against field test data, showing high correspondence in force levels and displacements. The study optimizes spring suspension parameters for speeds of 40–140 km/h. Key findings include: Relative friction coefficients (${\phi }_0$) should be adjusted: reduced to 6% for new bogie designs, but increased to 12% for model 18-9996 equipped with diagonal braces. Dynamic stability improves significantly with increased horizontal coupling stiffness. This is achieved through the integration of diagonal braces with side frames and the use of elastic-roller side bearers. This methodology provides a robust framework for evaluating the stability and performance of innovative railway vehicle designs. Full article

The occurrence of short-wave corrugation with wavelengths of 32–44 mm on curved sections of urban express railway lines is particularly pronounced, yet the underlying initiation mechanisms have remained insufficiently understood. Furthermore, conventional mitigation strategies—including the installation of rail dampers and passive grinding—entail substantial maintenance expenditures, thereby hindering their large-scale application. To elucidate the initiation mechanisms of rail corrugation and to formulate effective control measures, the characteristic corrugation parameters under various track structure configurations across an entire alignment were first measured and systematically analyzed. Dynamic interaction models between vehicles and three distinct track typologies were subsequently developed, together with a comprehensive analytical framework for corrugation evolution. The wheel–rail dynamic response characteristics and corrugation growth rates corresponding to each track type were examined, and the wheel–rail coupled vibration modes that exacerbate corrugation propagation in urban express lines were identified. The instantaneous wear behavior of the rail under differing creep regimes was also investigated, leading to the proposal of a novel self-mitigating approach for rail corrugation. The results demonstrate that the excitation frequency of rail corrugation is predominantly confined to the 600–700 Hz range, exhibiting a fixed-frequency characteristic that remains invariant with respect to curve radius, track structure type, and operational speed. An interesting finding is that, although the intrinsic vibration properties of different track structures diverge significantly, the third-order bending resonance of the rail segment situated between bogie wheels is largely unaffected by track-borne vibrations and manifests as a localized wheel–rail resonance within the vehicle–track coupled system. This particular resonance markedly accelerates corrugation development and is identified as the critical governing factor for corrugation initiation in urban express lines, regardless of the underlying track configuration. Furthermore, rail instantaneous wear displays a substantial phase shift under varying creep conditions, with the wear profiles under creep saturation (full sliding) and low creep (rolling–sliding) exhibiting a distinct anti-phase relationship. This insight underpins a novel self-wear suppression strategy: by intentionally mixing rolling–sliding and full-sliding operational regimes, destructive interference between the out-of-phase wear contributions is achieved, resulting in a considerably attenuated corrugation growth rate compared with exclusive rolling–sliding operation. This methodology thus offers a promising and fundamentally new alternative for the long-term management of rail corrugation through intrinsic wheel–rail interaction. Full article

Railway switch machines are key devices in railway signal systems and have a critical impact on train operation safety. However, in real operating conditions, fault samples are scarce because field data collection is cumbersome and often constrained by safety requirements, which limits the diagnostic accuracy and generalization capability of traditional fault diagnosis methods in few-shot scenarios. To address the challenge posed by insufficient accuracy in railway switch machine state recognition using sensors under few-shot conditions, we propose a regularized supervised contrastive meta-learning (RSCML) fault diagnosis method for switch machines. First, the tri-axial vibration signals acquired from the throwing rod and the reducer are transformed into axis-wise STFT spectrograms and organized as a unified three-channel time-frequency representation for subsequent cross-channel feature learning. Second, channel expansion and attention enhancement are employed to obtain more informative feature representations among similar fault types under limited samples. Finally, the feature extractor is integrated into the regularized supervised contrastive ANIL framework, while multi-loss optimization and stability regularization jointly constrain the meta-learning training process. Experimental results show that the proposed method achieves a maximum accuracy of 99.73% on 3-way and 5-way few-shot tasks, together with an F1-score of up to 99.72%. In the cross-category generalization experiment, it achieves a 93.08% accuracy and a 92.84% F1-score, indicating improved robustness when the fault categories at test time differ from those used during meta-training. The proposed method shows superior classification performance and stronger generalization to unseen fault categories under the current dataset setting, which suggests promising potential for switch machine fault diagnosis under limited sample conditions. Full article

Continuous monitoring of railway bridges is essential for ensuring safety and operational reliability, considering aging mechanisms, rising traffic, and elevated speeds of railway vehicles. Frequently, traditional vibration-based approaches, including modal identification and data-driven diagnostic strategies, are strongly influenced by environmental and operational variability, requiring labeled damaged datasets or numerical simulations to provide reliable outcomes. However, the acquisition of complete and representative datasets for training neural networks in structural health monitoring remains a challenging task, particularly for large-scale civil structures such as bridges. In these cases, unsupervised learning approaches represent promising solutions. An unsupervised anomaly detection methodology for railway bridge monitoring based on a long short-term memory (LSTM) autoencoder (AE) trained exclusively on bridge accelerations under healthy structural conditions is proposed in the present work. Specifically, the acceleration responses are obtained from simulations made on a calibrated finite element model of the bridge, reproducing realistic train–bridge interaction scenarios. The multi-channel acceleration signals are reconstructed by the proposed LSTM AE to produce the Root Mean Square Error (RMSE) between measured and reconstructed acceleration responses as indicators of potential structural anomalies. A dual-threshold strategy is adopted for damage detection purposes, including a global threshold for identifying anomalies in the overall dynamic response and per-sensor thresholds derived from the healthy-condition RMSE distribution for detecting localized damages. Only healthy-condition data are required for employing the proposed technique, avoiding labeled damaged data for training purposes. The LSTM AE constitutes an effective and computationally efficient tool for anomaly detection and continuous structural health monitoring of railway bridges, as demonstrated by the obtained results, representing a promising alternative to classical modal-based approaches and existing deep learning-based methods. Full article

The present paper addresses the experimental measurement of vibration frequencies using an earth pressure sensor embedded in a full-scale (1:1) test structure. The vibration frequencies within the tested structure were induced by static load tests carried out at different elevation levels (corresponding to varying thicknesses of the crushed aggregate layer) in accordance with the methodology applied on German railways (DIN 18 134). The aim of the research was to verify the stress state at individual partial levels of the tested structure on the basis of the measured vibration frequencies, and to determine the depth of influence and the load dispersion angle generated by the static load test (SLT). The measured parameters also serve as input data for parallel research focused on the assessment of transition zones between railway embankments and artificial structures along railway lines. The results presented in this paper indicate that the stress induced by the SLT decreases with increasing structural thickness of the tested construction. For a structural layer thickness of 150 mm, the resulting stress corresponds to approximately 63% of the stress value (force effect) induced on a rigid circular plate (σ = 0.50 MPa), whereas for a layer thickness of 900 mm, the stress corresponds to approximately 12% of that value. The force (stress) effects of the SLT cease to act at a depth between 900 and 950 mm (only stress due to the self-weight of the overlying material was recorded), and the load dispersion angle is approximately 40°. Full article

Large-span bridges with bluff body girders are susceptible to vortex-induced vibration (VIV) due to their low frequency, light mass, and relatively low damping ratio, affecting fatigue life and serviceability. While research progress has been made on VIV mechanisms and control measures, systematic investigations on the application of vortex generators (VGs) to narrow Π-shaped railway girders remain scarce, and the potential synergistic effect of combining VGs with conventional aerodynamic measures has not been explored. To address this gap, wind tunnel tests were conducted on a 1:50 scale sectional model of a narrow Π-shaped steel girder for a railway cable-stayed bridge. The experimental program systematically investigated the VIV response of the original girder and evaluated the suppression effectiveness of conventional aerodynamic measures (vertical stabilizers, deflectors, modified fairings) and spanwise control using VGs. Parametric optimization of VG height (0.1 H–0.2 H, where H is the girder height), spacing (2/3 L₀ and L₀, where L₀ = 12.5 m is the standard segment length), and installation position (upper fairing, lower fairing, girder bottom) was performed. Results show that under wind angles of attack from −5° to +5° and a damping ratio of 0.36%, the original girder exhibits pronounced vertical VIV with a maximum RMS amplitude of 0.025 m, approximately 3.15 times the code limit. Conventional measures alone fail to adequately suppress VIV. However, the optimal combination of VGs (height 0.2 H, spacing L₀, installed on the lower fairing) with a 0.5 m wide, 15° inclined deflector effectively suppresses VIV under wind AOAs of 0°, ±3°, and –5°, achieving suppression below the measurable threshold. This study contributes the first comprehensive parametric investigation of VGs for narrow Π-shaped railway girders, reveals a synergistic effect when combining VGs with deflectors, and incorporates practical engineering constraints (such as aesthetic requirements) into the optimization process. Full article

The use of Machine Learning tools for studying, among others, vibrating signals that enable a comprehensive analysis of the state of the elements under study through Machine Learning techniques has become widespread. Considering the main traditional classification methods of these tools and their associated use of artificial intelligence, this paper thoroughly analyses both current approaches and trends in their use, as well as examining intelligent means for diagnosing faults and monitoring the condition of mechanical systems. These methodologies are becoming increasingly common in Industry 4.0. The objective of this paper is to systematically review the latest trends in research and development for the diagnosis of faults and monitoring the condition of rotating equipment using Artificial Intelligence tools. Therefore, this paper studies Machine Learning techniques applied to the analysis of signals from rotating mechanical elements, particularly bearings and shafts, with a special focus on the classification of the condition of railway rolling stock. Full article

Railway traffic monitoring requires robust detection technologies capable of operating reliably under real-world vibration and environmental conditions. In this work, we present the design and validation of an optical vibration sensor based on a Single-mode–Multimode–Single-mode (SMS) fiber structure for Light Rail Vehicle (LRV) detection. The sensing mechanism relies on multimodal interference in the multimode fiber (MMF), where rail-induced vibrations modify the guided mode distribution and, consequently, the transmitted optical intensity. The optical signal is converted to voltage and processed through an embedded acquisition system. Additionally, we conducted tests with freight trains and maintenance trains in order to evaluate the applicability of the sensor in other types of trains besides the LRV. We conducted laboratory experiments to assess mechanical stability, sensibility, and packaging strategies, followed by supervised field tests on an operational LRV line. The recorded time-domain signal exhibited clear modulation during train passage, and first-derivative and sliding-window variance analyses were applied to reliably identify vibration events, even in the presence of slow baseline drift. In addition, frequency-domain analysis was performed by applying the Fast Fourier Transform (FFT) to the measured signal, enabling the identification of characteristic low-frequency spectral components induced by train passage. A quantitative sensitivity assessment was further carried out by correlating the integrated spectral energy (0–12 Hz) with vehicle weight, yielding a linear response with a sensitivity of 0.0017 a.u./t and coefficient of determination $R^2=0.933$. The proposed solution demonstrated stable operation using commercially available low-cost components, confirming the feasibility of SMS-based optical sensing for railway monitoring. These results indicate strong potential for future deployment in traffic safety systems and distributed sensing networks. Full article