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- Impact of the train-track-bridge system characteristics in the runnability of high-speed trains against crosswinds - Part II: Riding comfortPublication . Montenegro, P.A.; Ribeiro, Diogo; Ortega, M.; Millanes, F.; Goicolea, J.M.; Zhai, W.; Calçada, R.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.
- Detection of exposed steel rebars based on deep-learning techniques and unmanned aerial vehiclesPublication . Santos, R.; Ribeiro, Diogo; Lopes, PatrĂcia; Cabral, R.; Calçada, R.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.
- Calibration of the numerical model of a freight railway vehicle based on experimental modal parametersPublication . Ribeiro, Diogo; Bragança, C.; Costa, C.; Jorge, P.; Silva, R.; ArĂȘde, A.; Calçada, R.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 suspensionâs 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.
- Calibration of the numerical model of a freight railway vehicle based on experimental modal parametersPublication . Ribeiro, Diogo; Bragança, C.; Costa, C.; Jorge, P.; Silva, R.; ArĂȘde, A.; Calçada, R.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 suspensionâs 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.