The railway sector plays a crucial role in sustainable transportation by reducing greenhouse gas emissions while supporting an increasing volume of freight and passenger transport. Rail pads, essential components in railway infrastructure, mitigate vibrations and distribute loads; however, their long-term performance is influenced by environmental and mechanical degradation, affecting track durability and maintenance costs. Despite their significance, the degradation mechanisms impacting the dynamic stiffness of EPDM (Ethylene Propylene Diene Monomer) and EVA (Ethylene Vinyl Acetate) rail pads remain insufficiently characterized. This study examines the effects of mechanical and chemical aging on the stiffness of these materials through 864 dynamic stiffness tests, analyzing three types of rail pads under mechanical cycling (up to 2,000,000 cycles), UV (ultraviolet light) exposure (100–500 h), and hydrocarbon exposure (100–500 h). Mechanical aging increases stiffness across all pads, with Pad C (EVA) exhibiting the most pronounced increase (27%). The effects of UV exposure vary by material, leading to a stiffness reduction of up to 11.5% in Pad B (EPDM), whereas Pad C (EVA) experiences a 9.5% increase under prolonged exposure. Hydrocarbon exposure also presents material-dependent behavior, with Pad A (EPDM) experiencing an 11.5% stiffness reduction at low exposure but partial recovery at higher exposure, while Pad C (EVA) shows a 5% increase in stiffness under prolonged exposure. These findings offer valuable insights into the aging mechanisms of rail pads and underscore the importance of considering degradation effects in track maintenance strategies.

El presente estudio se centra en el análisis de la propagación de fisuras en materiales sometidos a ensayos de fatiga por resonancia magnética. Este tipo de ensayos resulta una alternativa eficiente frente a las máquinas servohidráulicas, ya que permiten alcanzar frecuencias de ensayo más altas con un consumo energético significativamente menor. Sin embargo, presentan la desventaja de no mantener una frecuencia constante a lo largo del ensayo. El estudio utiliza barras de acero de 16 mm de diámetro y sigue las disposiciones establecidas en la BS 4449. El objetivo es analizar la evolución de la frecuencia de resonancia durante los ensayos de fatiga y su relación con el crecimiento de las fisuras. Se observa que la fisura se origina en concentradores de tensión, y su geometría se aproxima a una circunferencia. El análisis demuestra dos etapas en el comportamiento de la frecuencia de resonancia: una primera etapa de endurecimiento cíclico donde la frecuencia crece, seguida de una caída acelerada asociada a la propagación de la fisura. Este cambio permite identificar el punto de inflexión en el proceso de propagación de la fisura. Finalmente, se establece una relación entre el tamaño de la fisura y la variación de la frecuencia de resonancia, lo que permite ajustar una "ley de Paris" modificada, que describe la velocidad de crecimiento de la fisura en función de la frecuencia de resonancia, aportando un enfoque novedoso para la monitorización del daño en tiempo real.

The vertical stiffness of railway tracks is crucial for ensuring safe and efficient rail transport. Rail-pad dynamic stiffness is a key component influencing track performance. Determining the dynamic stiffness of rail pads poses a challenge because it depends not only on the material and geometry of the rail pad but also on the testing conditions, due to the non-linear material response. To address this issue, a methodology is proposed in this paper to estimate dynamic stiffness using static stiffness measurements. This approach enables the prediction of dynamic stiffness for different situations from a single laboratory test. This study further examines whether this correlation remains valid for different types of rail pads, even when their mechanical behavior has been degraded by temperature, wear, or chemical agents. Experiments were conducted under varying temperatures and on rail pads that underwent mechanical and chemical degradation. The analysis assesses the validity of the static-to-dynamic stiffness correlation under degraded conditions and investigates the influence of each testing condition on the ability to estimate dynamic stiffness from static stiffness and operational parameters. The findings provide insights into the reliability of this predictive model and highlight the impact of degradation mechanisms on the dynamic behavior of rail pads. This research enhances the understanding of rail pad performance and offers a practical approach for evaluating dynamic stiffness. By considering all of the variables used in the analysis, the approach achieves R2 values of up to 0.99, which carries significant implications for track design and maintenance.

This study investigates the fatigue behavior of railway rails with lubrication holes through a finite element modeling approach validated against full-scale laboratory tests. Fatigue tests were conducted on rail coupons subjected to three-point bending with the rail positioned upside-down, replicating the most critical loading configuration. Two finite element models were developed using ANSYS 2024 R2: a reduced model reproducing the laboratory setup, and a more comprehensive model representing a real rail track segment with multiple spans. The first model was calibrated against experimental S–N curve data to ensure consistency with the mechanical behavior observed in tests. The second model was used to evaluate the effect of wheel position, hole diameter, and hole location on the fatigue life of the rail. Simulation results highlight the influence of geometric and load parameters on crack initiation near the hole, providing valuable insights for optimizing hole design and placement in operational conditions.

The increasing utilization of short fiber-reinforced thermoplastics, due to their advantageous mechanical properties and manufacturing convenience, has led to their application in areas traditionally dominated by metals. This shift underscores the importance of understanding the fatigue behavior of these materials. This study evaluates the fatigue behavior of short fiber-reinforced thermoplastics through three characterization methods: continuous fatigue, interrupted fatigue, and the Locati method, with the latter serving as a novel approach for estimating critical fatigue conditions from a single specimen. Continuous fatigue testing provides the baseline for comparison. The effect of load interruption is explored through the interrupted fatigue method. The Locati method, characterized by incrementally increasing load steps until failure, offers a significant benefit by enabling the estimation of critical fatigue conditions efficiently. This research aims to provide a comprehensive understanding of the fatigue behavior of short fiber-reinforced thermoplastics, contributing to the optimization of their use in engineering applications.

This study investigates the effect of hydrocarbon exposure on the mechanical behaviour of seat pads used as a part of the fastening system in rail transport. Three different types of rail pads are examined, two made of EPDM (Ethylene Propylene Diene Monomer) and one made of EVA (Ethylene Vinyl Acetate). The results indicate that hydrocarbon exposure has a significant effect on the dynamic stiffness and mass stability of the pads, particularly in the EPDM ones. More specifically, it is observed that EPDM exposure to hydrocarbons increases the flexibility of the pads. Furthermore, for prolonged exposure periods (500 hours), this flexibility can become so pronounced that exhausts the pad's ability to deform under the loads applied during track assembly, which results in a more rigid behaviour than aging. Such findings are important as they suggest that hydrocarbons can negatively impact the performance and safety of rail transport due to the changes induced in critical mechanical properties of pads. Consequently, measures should be taken by contractors and infrastructure managers to reduce the adverse influence of hydrocarbons on rail pads, which contributes to increase the safety and efficiency of rail transport.

Classification of cast iron alloys based on graphite morphology plays a crucial role in materials science and engineering. Traditionally, this classification has relied on visual analysis, a method that is not only time‐consuming but also suffers from subjectivity, leading to inconsistencies. This study introduces a novel approach utilizing convolutional neural networks—MobileNet for image classification and U‐Net for semantic segmentation—to automate the classification process of cast iron alloys. A significant challenge in this domain is the limited availability of diverse and comprehensive datasets necessary for training effective machine learning models. This is addressed by generating a synthetic dataset, creating a rich collection of 2400 pure and 1500 mixed images based on the ISO 945‐1:2019 standard. This ensures a robust training process, enhancing the model's ability to generalize across various morphologies of graphite particles. The findings showcase a remarkable accuracy in classifying cast iron alloys (achieving an overall accuracy of 98.9 ± 0.4%—and exceeding 97% for all six classes—for classification of pure images and ranging between 84% and 93% for semantic segmentation of mixed images) and also demonstrate the model's ability to consistently identify and graphite morphology with a level of precision and speed unattainable through manual methods.

The structural components inside power transformers are commonly made of high-density pressboard, due to its suitable mechanical and dielectric properties. Among these components are the spacers used in the windings of transformers, which are subjected to compressive loading during operation. The spacers are immersed in dielectric liquid and subjected to high temperatures and chemical reactions during the lifespan of the transformer, which result in the degradation of their dielectric and mechanical properties. The performance and reliability of the power transformer greatly depends on its mechanical integrity, so it is necessary to understand how ageing degrades the mechanical response of the high-density pressboard. In this study, spacers made of high-density pressboard and pieces of copper conductor were immersed in uninhibited paraffinic oil and aged at 150 °C for different periods of time, trying to realistically represent the process suffered by a power transformer during its whole lifespan. The evolution caused by the thermal ageing over some chemical parameters (acidity and moisture content) and dielectric properties (AC breakdown voltage, dielectric dissipation factor, resistivity and degree of polymerisation) of the oil and the pressboard was studied experimentally. Compressive mechanical tests were performed on samples of the aged high-density pressboard, and the compressive stiffness during the ageing process was related with other chemical and dielectric parameters.

Radial spacers made of high-density pressboard play a critical role in power transformers, providing support and maintaining distance between winding disks for efficient dielectric oil flow through cooling ducts. However, prolonged exposure to high temperatures leads to chemical reactions and degradation of mechanical and dielectric properties, potentially compromising transformer integrity. In this study, high-density pressboard spacers immersed in paraffinic oil underwent thermal aging to simulate the transformer lifespan. Various chemical, dielectric, and mechanical parameters were measured, including AC breakdown voltage, dielectric dissipation factor (tan $\delta$), permittivity, resistivity, moisture content, and oil acidity. Moreover, the degree of polymerisation and compressive stiffness of the pressboard were determined. By establishing mathematical relationships, it was possible to estimate the compressive stiffness indirectly and non-destructively, from the degree of polymerisation and oil acidity measurements. These findings offer valuable insights for evaluating the condition of radial spacers and ensuring the reliable operation of power transformers.

Rigorous and efficient management of the railway infrastructure is crucial to avoid accidents and reduce operation and maintenance costs. This requires in-depth knowledge of the assets, the interaction among them and the effect that each track parameter has on the overall infrastructure performance. In this study, a large set of studies are carried out, on a previously calibrated finite element slab track model, where the relevant track parameters are varied within their usual ranges. The results are then used to train and validate a series of predictive models based on Machine Learning algorithms. This methodology provides greater understanding and enhanced prediction of the behaviour of tracks, which are composed of multiple variables such as the soil/subgrade, supporting layers, sleepers, pads and rails. The study also considers train axle loads and service speeds, which are other key elements that influence the track performance. The results show that the parameters that have greatest influence on the railway infrastructure are the properties of the soil, characteristics of the rail pads and the axle loads. This work can support the implementation of predictive maintenance procedures for railway tracks and the development of innovative technological solutions, providing responses to the industrial needs of reducing costs and contributing to improve the competitiveness of railway transport.

The structural elements within power transformers are commonly constructed from high-density pressboard, chosen for its favourable mechanical and dielectric properties. Among these elements are the spacers employed in the windings of transformers, which endure compressive loading during operation. The spacers are immersed in dielectric fluid and exposed to high temperatures and chemical reactions over the transformer's lifespan, resulting in the degradation of their dielectric and mechanical properties. The mechanical integrity of the power transformer significantly relies on these factors; hence, it is imperative to comprehend how ageing deteriorates the mechanical response of the high-density pressboard. The present article presents experimental data on the compressive mechanical properties of a commercially available high-density pressboard, commonly employed in power transformer spacers, under various ageing conditions (induced through accelerated thermal ageing and assessed by the degree of polymerisation). These data hold potential for diverse applications. They can enhance the existing comprehension of the mechanical behaviour and degradation mechanisms of cellulosic insulation in power transformers and provide reference benchmarks for comparison with factory-obtained values by manufacturers. In the realm of engineering failure analysis, these values can be utilised to evaluate the mechanical failures of paper-based materials utilised as structural components in power transformers.

[This corrects the article DOI: 10.1016/j.dib.2023.109471.].

The superstructure of modern railway lines uses tons of technical polymeric material spread along the track with mechanical, insulating and damping functions. Many of these parts are rejected because they do not pass the quality controls, generating a large accumulation of plastic waste of high economic value. Therefore, this study is aimed at determining the optimum degree of recyclability by mechanical crushing of geometrically defective (and so rejected) railway fastenings flanged plates injected with short fiberglass-reinforced polyamide. After recycling, the material must guarantee its physical and mechanical properties required to ensure the future in-service conditions of the highly responsible components that guarantee the maintenance of the railway gauge. Viscosity, mechanical properties (tensile test), Charpy and fracture toughness as well as fatigue performance were determined for ten successive recyclings. It has been found that the drop of viscosity is the most restrictive limitation, allowing three recyclings of the material. All the properties measured have experienced a noticeable reduction after 10 recyclings. Specifically, viscosity is reduced by 15%, ultimate strength by 70%, yield stress by 41% strain under maximum load lost by 70%, Young’s modulus lost by 38%, Charpy impact strength by 70%, fatigue resistance by 69% and fracture toughness lost by 80%. With the development of this study and taking into account that the market price of the flanged plates is valued at approximately 8 k€/km, of which around 5 k€/km corresponds to the raw material, the recovery of this material not only represents a great environmental benefit but also an economic one.

The most recommendable frequency range for concrete fatigue tests is between 1 and 15 Hz. It has been clearly established that performing the tests at low frequency (<1 Hz) reduces the fatigue limit because it increases test time and therefore, creep damage. On the other hand, there are not enough studies analysing tests above 15 Hz, which could greatly reduce test time. In this work, three recycled concretes were characterized, starting with the comparison between tests at moderate frequency (10 Hz) with tests at very high frequency (90 Hz). From these tests it was observed that, in all cases, the fatigue limit or fatigue life was notably lower in the case of performing the tests at high frequency. The test results show that, in the case of tests performed over the fatigue limit, the temperature of the specimens rises up to approximately 100 degrees C, while, in the case of tests performed with strength lower the fatigue limit, the temperature stabilises at around 65 degrees C. In order to analyse whether the temperature was the cause of the reduction in the fatigue limit, creep tests were carried out at 3 temperatures: 20, 65 and 100 degrees C, and it was possible to verify that creep damage became significantly greater as temperature was increased, and that this effect was emphasized in those cases where the concretes were made with recycled crushed concrete aggregates.

Machine Learning algorithms are aimed at building generalizable models to provide accurate predictions or to find patterns from noisy data. These characteristics are potentially beneficial for the fabrication of steel products. In this research, 529 rotating bending fatigue tests (R =-1 and sigma a = 400 MPa) were carried out on steel suspension spring bars fabricated using different combinations of manufacturing parameters. A reliable regression model (R-2 = 0.877 on the test dataset) based on the Gradient Boosting algorithm was obtained. The interpretation of the model was carried out through the Permutation Importance algorithm, revealing the relevance of the temperature in the tempering treatment applied after quenching on the fatigue lifespan. This pattern was quantitatively described by means of the Partial Dependence Plot of this feature. Besides, a specific study was carried out to obtain a reliable interpretation of the results derived from the Machine Learning analysis. In this sense, it has been observed that specimens subjected to high temperature tempering display a lower surface hardness that provokes a higher surface roughness after shot peening; this, in turn, facilitates the initiation of surface cracks during the fatigue tests reducing the fatigue lifespan. This study provides a reliable framework to optimize the suspension spring manufacturing conditions to increase their fatigue lifespan as well as an example, generalizable to other manufacturing processes, of the potential benefits of Machine Learning.

The deterioration of the insulation in the windings of power transformers affects their lifespan. A commercial insulated Continuously Transposed Conductor (CTC) was studied experimentally, numerically and analytically. The purpose was to understand the mechanisms governing the mechanical failure of the insulating paper, and to achieve a criterion for predicting failure under different conditions. Samples of that insulated CTC were extracted from a coil and aged at $150^{\circ}{\mathrm{C}}$ for different durations inside vessels filled with naphthenic oil. Then the degree of polymerisation and tensile, compressive and shear mechanical properties of the insulation were measured/estimated. Aged insulated CTC samples were subjected to three-point bending tests, producing deformations compatible with a short circuit, and the fractures in the insulation were analysed. The bending test over a CTC sample was simulated by means of a FEM Program. The Norris failure model, applicable to a lamina, was adapted to the studied insulation materials. The predictions of that failure criterion agreed with experimental observations.

Natural esters have become of interest for the industry in recent years as dielectric liquids for power transformers, and many studies are focused on their dielectric and chemical properties and on their influence in the degradation of the solid insulation due to aging. However, very little is known about their impact on the evolution of the mechanical properties of the paper insulation, which are acknowledged to have a considerable influence in their overall performance and reliability during the operating life of power transformers. This work studies the effects of thermal aging with vegetable oil in some commercial components, which are commonly used in power transformers, such as an insulated continuously transposed conductor (CTC) and samples of thermally upgraded crepe insulation. The changes in the properties of the crepe paper insulation are characterized through the degree of polymerization and tensile testing. Failure initiation and propagation in the insulation of the CTC is analyzed macroscopically. The results are compared with those obtained when using mineral oil, showing that the use of vegetable oil has a protective effect over mechanical properties of the studied types of paper insulation.

This paper is aimed at identifying by means of micro-CT the microstructural differences between normal and degenerative mitral marginal chordae tendineae. The control group is composed of 21 normal chords excised from 14 normal mitral valves from heart transplant recipients. The experimental group comprises 22 degenerative fibroelastic chords obtained at surgery from 11 pathological valves after mitral repair or replacement. In the control group the superficial endothelial cells and spongiosa layer remained intact, covering the wavy core collagen. In contrast, in the experimental group the collagen fibers were arranged as straightened thick bundles in a parallel configuration. 100 cross-sections were examined by micro-CT from each chord. Each image was randomized through the K-means machine learning algorithm and then, the global and local Shannon entropies were obtained. The optimum number of clusters, K, was estimated to maximize the differences between normal and degenerative chords in global and local Shannon entropy; the p-value after a nested ANOVA test was chosen as the parameter to be minimized. Optimum results were obtained with global Shannon entropy and 2 <= K <= 7, providing p < 0.01; for K=3, p = 2.86.10(-3). These findings open the door to novel perioperative diagnostic methods in order to avoid or reduce postoperative mitral valve regurgitation recurrences.

The dielectric paper of power transformers is subjected to ageing caused by high temperatures and chemical reactions in the dielectric oil. The electromagnetic forces that affect the conductor can cause the breakage of the paper and even the end of the useful life of the transformer. To improve the understanding of those phenomena, a continuously transposed conductor with four layers of dielectric paper (plain Kraft and crepe papers) was impregnated in naphthenic oil and aged during different periods, representative of its whole lifespan. Samples of the conductor were subjected to bending deformations compatible with a short circuit, and the cracks generated in the paper were analysed. The aged dielectric paper was mechanically characterised (in tensile, compressive and shear modes) and the bending tests of the conductors were simulated in ANSYS Static Structural, to estimate the strains in the paper. The Norris failure criterion was modified to predict the regions in the dielectric paper susceptible to fail for a particular ageing state and deformation level, obtaining a good agreement with the experimental results and thus validating this new version of the Norris approach.