Browsing by Author "Diego, S."
Now showing 1 - 2 of 2
Results Per Page
Sort Options
- Dynamic calibration of slab track models for railway applications using full-scale testingPublication . Sainz-Aja, Jose Adolfo; Pombo, João; Tholken, D.; Carrascal, I.; Polanco, J.; Ferreno, D.; Casado, J.; Diego, S.; Perez, A.; Abdalla Filho, João Elias; Esen, A.; Čebašek, Tina Marolt; Laghrouche, Omar; Woodward, P.Research and development of technology for railways has found new impetus as society continues to search for cost effective and sustainable means of transport. This tasks engineers with using the state-of-the-art science and engineering for rolling stock development and advanced technologies for building high performance, reliable and cost-effective rail infrastructures. The main goal of this work is to develop detailed and validated three-dimensional slab track models using a finite element formulation, which include all components of the infrastructure. For this purpose, the parameters of the computational models are identified by performing full-scale tests of the fastening system and of the slab track, including all its material layers. The computational model proposed here is calibrated using this approach and a good agreement is obtained between experimental and numerical results. This work opens good perspectives to use this reliable track model to study the interaction with railway vehicles in realistic operation scenarios in order to assess the dynamic behaviour of the trains and to predict the long-term performance of the infrastructure and of its components.
- Machine learning algorithms for the prediction of the mechanical properties of railways’ rail padsPublication . Ferreño, D.; Sainz-Aja, J. A.; Carrascal, I. A.; Cuartas, M.; Pombo, João; Casado, J. A.; Diego, S.Train operations generate high impact and fatigue loads that degrade the rail infrastructure and vehicle components. Rail pads are installed between the rails and the sleepers to damp the transmission of vibrations and noise and to provide flexibility to the track. These components play a crucial role to maximize the durability of railway assets and to minimize the maintenance costs. The non-linear mechanical response of this type of materials make it extremely difficult to estimate their mechanical properties, such as the dynamic stiffness. In this work, several machine learning algorithms were used to determine the dynamic stiffness of pads depending on their in-service conditions (temperature, frequency, axle-load and toe-load). 720 experimental tests were performed under different realistic operating conditions; this information was used for the training, validation and testing of the algorithms. It was observed that the optimal algorithm was gradient boosting for EPDM (R2 of 0.995 and mean absolute percentage error of 5.08% in test dataset), TPE (0.994 and 2.32%) and EVA (0.968 and 4.91%) pads. This algorithm was implemented in an application, developed on Microsoft .Net platform, that provides the dynamic stiffness of the pads characterized in this study as function of material, temperature, frequency, axle-load and toe-load.