Early identification of wheel defects can prevent serious damage to railways, considerably lowering maintenance costs for both railway administrations and rolling stock operators. Within this context, an unsupervised methodology based on artificial intelligence techniques is presented, which allows the detection and classification of out-of-roundness damage wheels, such as wheel flats and polygonal wheels, based on dynamic responses induced on the track by crossing freight railway vehicles. The methodology involves the following steps: (i) data collection and pre-processing, (ii) feature extraction (iii) data fusion and (iv) feature discrimination. In the first phase, an FFT algorithm is applied to the acceleration track responses. Then, features are extracted after training a Stacked Sparse Autoencoder, in which the main features of the responses are obtained after a compression stage using an encoder network. This lower dimensional layer forces the model to learn a compression of the input data. Then, these extracted features are merged using the Mahalanobis distance, which enhances the sensitivity to the damage recognition. Posteriorly, an outlier analysis is performed to distinguish a healthy wheel from a defective one and a cluster analysis to discriminate the two types of out-of-roundness (OOR) damage and classify the severity of each type of damage.

This article presents the validation of a non-linear FE numerical model of a multi-span stone arch railway bridge based on experimental tests and under in-service freight trains. Static loading tests allow evaluating the bridge response in terms of vertical displacements in the arches, opening/closure deformations on specific block joints of the arches, and vertical compressive stress variations in the piers. The bridge FE model is developed by combining the potentialities of a global continuous homogeneous model, based on FEM and Drucker-Prager model, and a local modelling approach based on a dedicated non-linear contact model. The freight vehicle modelling is based on a flexible FE approach, and the validation of the dynamic behaviour of the train-bridge system involved the comparison between numerical and experimental responses. All the numerical responses are in very good agreement with the experimental responses. Finally, a simulation of the dynamic behaviour of the train-bridge system is performed for realistic scenarios of freight traffic considering speeds between 40 and 140 km/h.

Railway bridges are structures were the dynamic effects due to the passage of traffic could reach significant values, especially for speeds higher than 200 km/h, essentially due to the resonance phenomena originated by the periodic loading associated with the passage of regularly spaced axle groups of the trains.In resonance situations, the amplitude of the dynamic response of the bridges is very dependent of the damping of the train-track-bridge system including its interfaces.In framework of the In2Track2-WP5 project, experimental data of field tests performed in four filler-beam bridges of the Portuguese Railway Network were used.To evaluate the damping coefficients, the logarithmic decrement method and the Prony method were applied using the free vibration responses of the acceleration records due to passing trains.Ambient vibration tests allowed the identification of the modal parameters of the bridges, namely, the natural frequencies and modal configurations.The results of this work may contribute to future revisions of EN1991-2.

The identification of instability problems in freight trains circulation such as unbalanced loads is of particular importance for railways management companies and operators. The early detection of unbalanced loads prevents significant damages that may cause service interruptions or derailments with high financial costs. This study aims to develop a methodology capable of automatically identifying unbalanced vertical loads considering the limits proposed by the reference guidelines. The research relies on a 3D numerical simulation of the train–track dynamic response to the presence of longitudinal and transverse scenarios of unbalanced vertical loads and resorting to a virtual wayside monitoring system. This methodology is based on measured data from accelerometers and strain gauges installed on the rail and involves the following steps: (i) feature extraction, (ii) features normalization based on a latent variable method, (iii) data fusion, and (iv) feature discrimination based on an outlier and a cluster analysis. Regarding feature extraction, the performance of ARX and PCA models is compared. The results prove that the methodology is able to accurately detect and classify longitudinal and transverse unbalanced loads with a reduced number of sensors.

Currently, digital technologies provide engineers with the ability to improve quality control in civil engineering and construction. In this industry, structural quality control aims to prevent deviations that may occur in the assembly of the structure, to guarantee compliance with the essential requirements for its operation. In this context, an attempt was made to learn whether the application of digital technologies, particularly use of the laser scanners, is a resource that facilitates quality control procedures, making them digital and more reliable. In the presented case study, the methodology involved the use of a laser scanner in the quality control of the assembly of a steel structure of an industrial building. Thus, after the study on technologies associated with the automation of geometric object checks, with the use of the computational tools and digital laser survey technologies, the structural elements assembled in situ were analyzed. The main results this study allow us to conclude that the use of the digital laser scanner survey technologies contributes favorably to the automation of geometric quality control procedures in the assembly of steel structures.

This work exploits unsupervised data-driven AI-based structural health monitoring (SHM) in order to propose a continuous online procedure for damage detection based on train-induced dynamic bridge responses, taking advantage of the large-magnitude loading for enhancing sensitivity to small-scale structural changes.While such large responses induced by trains might create more damage-sensitive information in the measured response, it also amplifies the effects on those measurements from the environment.Thus, one of the biggest contributions herein is a methodology that exploits the large bridge responses induced by train passage while rejecting the confounding influences of the environment in such a way that false positive detections are mitigated.Furthermore, this research work introduces an adaptable confidence decision threshold that further improves damage detection over time.To ensure an online continuous assessment, a hybrid combination of autoregressive exogenous input (ARX) models, principal components analysis (PCA), and clustering algorithms was sequentially applied to the monitoring data, in a moving window process.Since

This article presents an efficient methodology for the calibration and validation of a numerical model of a freight wagon based on a dynamic test under real operation conditions. The dynamic test takes place during a regular journey of the train and involved the installation of on-board accelerometers and LVDTs, whose number and location was conditioned by the space occupied by the goods and constraints associated with loading and unloading the wagon. The data derived from the dynamic test was used for the identification of carbody's modal parameters, namely the frequencies, mode shapes and damping coefficients and to extract accelerations and displacements time-histories. A three-dimensional (3D) FE numerical model of the freight wagon was developed and calibrated using a genetic algorithm. The methodology proves efficiency and robustness in precisely estimating three numerical parameters, besides a significant upgrade in relation to the model before calibration. Model validation involved the comparison between numerically simulated results, based on a vehicle-track dynamic interaction analysis, and the experimental observations. An excellent agreement between experimental and numerical after updating time-histories was obtained, especially for the carbody responses, whose behaviour is governed by lower frequencies (below 3.5 Hz), in which the calibration process was focused.

Wind-induced fatigue is a major issue for the design of slender high-rise structures. However, there are still few studies focused on this topic, resulting in a lack of practical design procedures for this type of structures. This paper aims to fill this gap by presenting a complete and practical methodology for the wind-induced fatigue life assessment of high-rise towers and its application to a 120 m cable-stayed steel tower composed by a modular lattice. The wind actions were considered as the sum of the quasi-static component according to international codes and a numerically generated, trough an ergodic stochastic process, turbulent component which is based on the Kaimal wind spectrum. Real wind measurements were also taken for a period of 15 months on a nearby MET station which, when compared with the normative scenario, proved to be much less conservative and were not used for the safety analysis. The wind velocities were used as inputs for a nonlinear dynamic analysis from which stress time histories were derived for 10 potentially critical structural details. The damage in each detail was computed through the application of the Rainflow counting algorithm and Palmgren-Miner's damage accumulation law, indicating the connection region between the modules as the critical detail with respect to fatigue damage.

Abstract Background Aortic stenosis (AS) is one of the main valvular heart diseases in developed countries. Degenerative fibrocalcific aortic stenosis is a progressive disease of the valve and ultimately of the myocardium, which can be fatal when symptomatic. There is no medical treatment that can halt or delay its progression. AS does not evolve linearly over time, and not every patient has the same progression rate. Aims The aim of this study is to 1) compare different mathematical models of aortic stenosis progression, 2) cluster patients into rapid and slow progressors and explore possible predictors, 4) evaluate the impact of different progression rates on cardiac structure and function, and 5) evaluate survival and optimal timing for follow-up and treatment. Methods We retrospectively studied consecutive patients with aortic peak velocities from 2012 to 2020. Follow-up echocardiograms, seriated biomarker assessment, and clinical records were consulted, providing a multiparametric data frame for longitudinal and dynamic modeling of aortic stenosis progression and its consequences. Results This study included 9583 studies from 752 patients with a median total follow-up of 4.26 years (interquartile range: 1.28 to 7.24 years). A logistic model was selected with the best accuracy to predict the rate of AS progression. Patients were categorized into slow and rapid progressors in a ratio of 5:1. Multiparametric analysis showed no association between these profiles and clinical variables. However, anti-hypertensive drugs before and after adjustment for blood pressure control (Calcium Channel Blockers, p=0.013, OR 0.50) were associated with slower progression. Meanwhile, elevated inflammatory markers (erythrocyte sedimentation rate, p=0.01) were associated with faster AS progression. Despite no survival difference between these groups, higher rates of valvular intervention were registered in rapid progressors (p<0.001). Moreover, faster progressors were associated with earlier cardiac damage (as demonstrated by early onset of moderate mitral and tricuspid valve regurgitation, left auricle dilation, and left ventricle hypertrophy, p<0.05). Conclusions These results can potentially modify follow-up times and deliver more personalized and individualized health care to different AS patients, thereby optimizing resources. Funding Acknowledgement Type of funding sources: None.

The simulation of the dynamic behavior of the train-track system is strongly dependent on the accuracy of the numerical models of the train and track subsystems. The use of calibrated numerical models of the railway vehicles, based on experimental data, enhances their ability to correctly reproduce the dynamic responses of the train under operational conditions. In this scope, studies involving the experimental calibration of freight wagon models are still scarce. This article aims to fill this gap by presenting an efficient methodology for the calibration of a numerical model of a freight railway wagon based on experimental modal parameters. A dynamic test was performed during the unloading operation of the train, adopting a dedicated approach which does not interfere with its tight operational schedule. From data collected during the dynamic test, five natural frequencies and mode shapes associated with rigid-body and flexural movements of the wagon platform were identified through the Enhanced Frequency-Domain Decomposition (EFDD) method. A detailed 3D finite-element (FE) model of the loaded freight wagon was developed, requiring precise knowledge of the vehicle design details which, in most situations, are difficult to obtain due to confidentiality reasons of the manufacturers. The model calibration was performed through an iterative method based on a genetic algorithm and allowed to obtain optimal values for seven numerical parameters related to the suspension's stiffnesses and mass distribution. The stability of the parameters considering different initial populations demonstrated the robustness of the optimization algorithm. The average error of the natural frequencies decreased from 8.5% before calibration to 3.2% after calibration, and the average MAC values improved from 0.911 to 0.950, revealing a significant improvement of the initial numerical model.

The aim of this study was to investigate the relation between endothelial dysfunction and aspirin response in a young healthy population (102 men aged 18–40). Initial concentrations of the NO pathway metabolites (ADMA, l-arginine, SDMA), cardiovascular risk markers, oxidative stress markers (MDA, thiol index), sICAM1, sVCAM1, PAI-1, sE-selectin, sP-selectin, VEGF, thromboxane B2, 6-keto-PGF1α and arachidonate-induced platelet aggregation (to separate aspirin resistant from sensitive group) were measured. Flow-mediated-vasodilation (FMD) was measured before and after intravenous infusion of 16.0 g of l-arginine. Measurements were repeated following aspirin administration (75 mg/24 h) for 4 days. Both groups were homogenous regarding demographic and biochemical characteristics reflecting cardiovascular risk. Aspirin resistant subjects were characterized by lower baseline FMD and higher FMD following aspirin and l-arginine treatment, as compared to aspirin sensitive control. MDA and nitrotyrosine were greater, whereas thiol index was lower in aspirin resistant men. The sICAM1, sVCAM1, PAI-1, sE-selectin, sP-selectin and VEGF levels were similar in the analyzed groups. Thromboxane in aspirin resistant subjects was greater both at baseline and following aspirin therapy. However, a significant decrease following aspirin treatment was present in both groups. Aspirin resistance in young men is associated with endothelial dysfunction, which could be due to oxidative stress resulting from lipid peroxidation.

In recent years deep-learning techniques have been developed and applied to inspect cracks in RC structures. The accuracy of these techniques leads to believe that they may also be applied to the identification of other pathologies. This article proposes a technique for automated detection of exposed steel rebars. The tools developed rely on convolutional neural networks (CNNs) based on transfer-learning using AlexNet. Experiments were conducted in large-scale structures to assess the efficiency of the method. To circumvent limitations on the proximity access to structures as large as the ones used in the experiments, as well as increase cost efficiency, the image capture was performed using an unmanned aerial system (UAS). The final goal of the proposed methodology is to generate orthomosaic maps of the pathologies or structure 3D models with superimposed pathologies. The results obtained are promising, confirming the high adaptability of CNN based methodologies for structural inspection.

Passenger riding comfort is a major concern in railways, particularly in high-speed (HS) networks due its strict requirements. Both the track and vehicle conditions may influence the comfort experienced by the passengers, but other external factors may also do it. Among these factors, the effects caused by crosswinds stand out due to the high levels of vibrations that may cause to the vehicle. However, almost no studies in this regard can be found in the literature, since most of the works do not consider external loads and do not analyse this phenomenon on bridges. Thus, the present work aims to fill this gap, by evaluating the passenger comfort on bridges subjected to crosswinds with different lateral structural behaviours and track conditions. Based on the vehicle's accelerations computed with an in-house dynamic train-track-bridge interaction tool, the Mean and Continuous comfort indexes defined by the European norm EN 12299, as well as the Sperling index, have been assessed for distinct scenarios. The bridge's lateral behaviour shows a negligible effect in the riding comfort, as well as the track quality since the wind load is much more determinant for the carbody vibrations than the track irregularities considered in this work.

The present work focuses on the evaluation of the dynamic behavior of a centenary steel arch bridge, located in Portugal, under light railway traffic loads. This works aims to assess the dynamic behavior of the bridge subjected to an alternative type of railway vehicle, more specifically, a typical underground vehicle that is currently in service in the Lisbon Metro. The dynamic response of the system has been evaluated using two distinct methodologies, namely a moving loads model and a vehicle–bridge interaction model. To achieve this goal, finite element (FE) models from both the bridge and the vehicle have been developed and a comprehensive study has been carried to evaluate the influence of distinct factors in the dynamic response of the bridge–train system, namely the methodology used to assess the dynamic response, the location of the response reference point in the deck, the train speed and the vehicle configuration (single or double vehicle). Moreover, both the traffic safety, passenger comfort and pedestrian comfort have also been evaluated using normative criteria based on acceleration responses. The results shown that the normative limits related to traffic safety and passenger comfort were never exceeded in any condition analyzed in the study. However, the pedestrian comfort was jeopardized when the train speed exceeded 20[Formula: see text]km/h.

The aim of the present study consists of evaluating the influence of the most relevant geometric, mechanical and aerodynamic vehicle properties in the risk of derailment caused by crosswinds. To achieve this objective, a vehicle-structure interaction model is used to carry out non-linear dynamic analyses to assess the train-track coupling behaviour in the presence of winds. By computing the wheel-rail contact forces, the derailment risk is evaluated based on the unloading criterion, as suggested by the European Norm EN 14067–6 (2016), for several scenarios with different train and wind speeds. The wind is simulated with a stochastic model that allows the generation of turbulent wind time-histories based on power spectral density functions. The reference vehicle adopted in this work corresponds to the European InterCity Express 3 (ICE-3) train, whose original properties were parameterized in order to evaluate their influence in the vehicle's stability. The parametric study focused on several properties of the vehicle, namely the carbody mass, height of the gravity centre, aerodynamic coefficients, and stiffness and damping of the suspensions. Apart from the suspensions' properties, which prove to have a negligible influence in the vehicle's stability, the remaining parameters have a significant impact in the running safety against crosswinds.

Train running safety is a major concern among railway engineers, since a derailment may cause significant personal and material damages. This problem becomes more important if the derailment occurs on bridges, especially at high-speeds, where the consequences may be even worse. The sudden development of high-speed (HS) railway networks that occurred at the end of the 20th century and beginning of the 21st century demanded the construction of new lines with large curve radii in order to fulfill the design requirements of this type of transport. By adding this fact to the orography constraints and, in some cases, to constraints related with the lack of construction area and with the high costs of expropriation, several HS lines started to be developed with more than 75% of their length built over viaducts and bridges. Naturally, this relatively new reality led to a significant increase in the probability of a train being exposed to natural hazards that might jeopardize its stability when it is running over an elevated structure. Hence, this paper aims to present a comprehensive literature review of the problematic associated with the train running safety assessment on bridges. The existing normative criteria from different regions of the world related to this topic are summarized in a first stage. Then, the paper gives a brief description of the available train-bridge interaction models needed to explicitly assess the traffic stability, followed by a presentation of the running safety indexes used to assess the derailment risk. Finally, the available applications regarding the traffic stability against different sources of excitation are systematically reviewed and guidance to future research work on this topic is provided.

Progressive updating strategies are commonly used for the calibration of numerical models such as those of a track-bridge system. These strategies involve, first, updating the track model based on dedicated dynamic tests, and second, the calibration of the bridge model based on ambient vibration tests considering the updated track parameters. This subsequent procedure is suggested as some track parameters are, in relation to the modal responses of the bridge, generally not sufficiently sensitive for a successful parameter estimation. This work describes the calibration of a specific finite element (FE) model of a track section over a bowstring-arch railway bridge. The model includes the rails, rail pads, sleepers, the ballast layer and the interfaces with the bridge and neighbouring track sections. A track receptance test allowed for the identification of natural frequencies and mode shapes of the track by applying two different techniques, an output-only and an input-output. Several modes of vibration related to the movement of the sleepers on the ballast were identified using the SSI-DATA method, at frequencies between 83.8 Hz and 142.2 Hz. The calibration of the track FE model was performed using an iterative methodology by means of a genetic algorithm, which proves efficiency and robustness in estimating several track numerical parameters, besides a significant upgrade in relation to the numerical model before calibration.