Abstract This paper investigates the fatigue reliability of the interfacial bonding in the concrete–concrete composites of CRTS III slab ballastless track structure under material uncertainty, using combined parametric experiments and numerical simulations. First, based on the fatigue constitutive model of interfacial bonding, parametric experiments are conducted to identify basic variables. Then, by integrating existing data, uncertainty quantification is performed for both the bonding and concrete to obtain the probability distributions of random variables. Moreover, the probability density evolution method is adopted to assess the interfacial fatigue reliability, based on finite element simulations of CRTS III slab ballastless track under fatigue temperature loading. The experimental results confirm the presence of significant material uncertainties, which underscores the necessity of shifting from a deterministic to a stochastic perspective for addressing the interfacial fatigue. The simulation results reveal that the interfacial fatigue reliability drops rapidly with service time. When the formulated level-II damage limit is employed as the maintenance criterion, the reliability decreases to 0.80, 0.48, and 0.17 after 10, 30, and 60 years, respectively. These findings indicate relatively weak durability of the interfacial bonding in concrete–concrete composites of CRTS III slab ballastless track and highlight the need of significant efforts from maintenance departments.

Abstract Lateral resistance, principally dictated by the interaction between sleepers (ties) and ballast, is a fundamental property of ballasted railway track which is critical to overall track stability and safety. This study investigates the influence of sleeper spacing on lateral resistance to support overall track system design and track safety management. A novel multi-sleeper push test (MSPT) methodology, utilising both a kinematic restraint to better replicate track conditions and a state-of-the-art digital image correlation (DIC) system to measure surface ballast movement, has been used to evaluate the lateral resistance behaviour of two different sleeper types (concrete and steel) under seven different sleeper spacing conditions (550–850 mm). This is the first direct comparison of modern concrete and steel sleeper variants under these conditions. It is found that both sleeper types exhibit similar lateral resistance behaviour at small displacements (less than 3 mm) but, regardless of the spacing used, steel sleepers have significantly greater lateral resistance beyond this point. Although it depends on the assessment method used, concrete sleepers demonstrated an optimum spacing of approximately 675 mm, whilst steel sleepers showed highest resistance at approximately 706 mm. However, concrete sleepers showed less sensitivity to changes in spacing compared to steel sleepers, which retained high resistance over a narrower range of spacings. These findings contribute to track safety management by improving buckling mitigation strategies and component selection, whilst unlocking the ability to assess system-level impacts to aid the pursuit of more sustainable railway infrastructure design.

Abstract The growing demand for sustainable transport has led to increasing interest in developing rail transit networks for both intra-city and inter-city travel. However, train-induced vibrations may cause significant negative environmental impacts on nearby buildings, sensitive equipment, and residents, thereby garnering considerable attention from researchers and engineers. An efficient prediction model is essential for assessing train-induced vibrations and for designing appropriate vibration mitigation measures. The complex dynamics of the train, track, infrastructure, soils, and buildings, along with their interactions, make the modeling of train-induced environmental vibrations a challenging task. This paper provides a comprehensive review of the current state-of-the-art methods for modeling train-induced vibrations from surface and underground railway traffic. It begins by addressing wave propagation in natural soils, followed by an in-depth examination of analytical, numerical, and empirical approaches for predicting train-induced vibrations in the ground and buildings. Finally, this paper identifies unresolved issues in the field and outlines areas that require further investigation.

Abstract Imbalanced loads in freight railway vehicles pose significant risks to vehicle running safety as well as track integrity, increasing the likelihood of derailments and increasing track wear rate. This study presents a robust machine learning-based methodology designed to detect and classify transverse imbalances in freight vehicles using dynamic rail responses. The proposed approach employs wayside monitoring systems with accelerometers and strain gauges, integrating advanced feature extraction methods, including principal component analysis, log-mel spectrograms, and multi-feature-based techniques. The methodology enhances detection accuracy by normalizing features to eliminate environmental and operational variations and employing data fusion for sensitive index creation. It is capable of distinguishing between different severity levels of imbalanced loads across various wagon types. By simulating scenarios with typical European freight wagons, the study demonstrates the effectiveness of the approach, offering a valuable tool for railway infrastructure managers to mitigate risks associated with imbalanced loads. This research contributes to the field by providing a scalable, non-invasive solution for real-time monitoring and safety enhancement in freight rail operations.

Abstract Drive-by techniques for bridge health monitoring have drawn increasing attention from researchers and practitioners, in the attempt to make bridge condition-based monitoring more cost-efficient. In this work, the authors propose a drive-by approach that takes advantage from bogie vertical accelerations to assess bridge health status. To do so, continuous wavelet transform is combined with multiple sparse autoencoders that allow for damage detection and localization across bridge span. According to authors’ best knowledge, this is the first case in which an unsupervised technique, which relies on the use of sparse autoencoders, is used to localize damages. The bridge considered in this work is a Warren steel truss bridge, whose finite element model is referred to an actual structure, belonging to the Italian railway line. To investigate damage detection and localization performances, different operational variables are accounted for: train weight, forward speed and track irregularity evolution in time. Two configurations for the virtual measuring channels were investigated: as a result, better performances were obtained by exploiting the vertical accelerations of both the bogies of the leading coach instead of using only one single acceleration signal.

Abstract This study explores the feasibility of electromagnetic acoustic transducers (EMATs) for ultrasonic rail inspection, focusing on bulk wave generation from the rail head and on defect detection at the central part of the rail foot. As a contactless method, EMATs could overcome some known limitations of conventional ultrasonic techniques, but require further validation. Different campaigns of experimental tests were performed, evaluating, by means of a probability of detection approach, the response of the technique to several artificial semi-elliptical flaws of increasing size and by considering two sensors characterized by different working frequencies. In contact, static tests allowed to assess the basic feasibility of the inspection technique and showed a linear response to defect size, saturating when defect width exceeded the rail web thickness. Dynamic tests allowed to introduce the effects of lift-off on signal responses. During all tests, the higher-frequency sensor outperformed the lower-frequency one. Finally, full-scale bogie tests on an indoor permanent track installation, comprehensive of defective rails, confirmed the higher flaw detection rates of the higher-frequency sensor, with minimal detection failures despite occasional false alarms. EMATs showed encouraging results for in-motion rail inspection: with further technical development and optimization, this technique could enhance ultrasonic rail inspection by diagnostic trains.

Abstract Combining the improved delayed detached eddy simulation and Ffowcs Williams–Hawkings equation, a numerical study is conducted to explore the potential of base-frame fairings in aerodynamic noise reduction of high-speed pantographs and deepen the understanding of related flow physics. The fairing models for noise control are designed without changing the bottom structures of the pantograph. The aerodynamic and acoustic results indicate that the flow deflection and acceleration effects caused by the fairings when shielding the bottom components of the pantograph as well as the self-noise generated by the interaction between the wake of unshielded components and the fairings may compromise the noise reduction effects. Compared with the solid fairing, the perforated fairing has additional advantages in noise reduction. The presence of through-holes leads to a flow redistribution around the fairing, alleviating the flow deflection and acceleration effects. Besides, the airflow ejected from the holes on leeward side can suppress the formation of vortex structures in the fairing wake and push them downstream, thereby effectively weakening the flow field fluctuation near the fairing tail. The investigation of the aerodynamic drag of the pantograph and lift fluctuation of the strip further confirms the superiority of the perforated fairing over the solid one.

Abstract Nonuniform track support and differential settlements are commonly observed in bridge approaches where the ballast layer can develop gaps at crosstie-ballast interfaces often referred to as a hanging crosstie condition. Hanging crossties usually yield unfavorable dynamic effects such as higher wheel loads, which negatively impact the serviceability and safety of railway operations. Hence, a better understanding of the mechanisms that cause hanging crossties and their effects on the ballast layer load-deformation characteristics is necessary. Since the ballast layer is a particulate medium, the discrete element method (DEM), which simulates ballast particle interactions individually, is ideal to explore the interparticle contact forces and ballast movements under dynamic wheel loading. Accurate representations of the dynamic loads from the train and track superstructure are needed for high-fidelity DEM modeling. This paper introduces an integrated modeling approach, which couples a single-crosstie DEM ballast model with a train–track–bridge (TTB) model using a proportional–integral–derivative control loop. The TTB–DEM model was validated with field measurements, and the coupled model calculates similar crosstie displacements as the TTB model. The TTB–DEM provided new insights into the ballast particle-scale behavior, which the TTB model alone cannot explore. The TTB–DEM coupling approach identified detrimental effects of hanging crossties on adjacent crossties, which were found to experience drastic vibrations and large ballast contact force concentrations.

Abstract In recent years, the issue of structure-borne noise generated by steel–concrete composite (SCC) bridges has become increasingly severe. To control this noise by adjusting the cross section parameters of SCC bridges, this study first established a numerical model based on the hybrid finite element–statistical energy analysis (FE-SEA) method. The overall sound pressure levels calculated by numerical model are compared with field measurements, showing discrepancies of 0.4 dB and 1.1 dB, respectively. The comparison confirms the accuracy of the numerical model. Then, a high-accuracy radial basis function neural network (RBFNN) was trained using samples generated from the numerical model with uniform design. To achieve greater noise reduction with lower costs, the non-dominated sorting genetic algorithm (NSGA-II) was used for multi-objective constrained optimization, resulting in the Pareto frontier for sound power levels (SWLs) and material cost. Finally, the solution set was evaluated using the technique for order preference by similarity to an ideal solution method, and the optimal combination of cross sectional parameters was obtained. This combination resulted in a 5 dB reduction in the SWL of the structure and a 23.9% reduction in material cost.