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• Review of the most important Rail Expansion Devices used in railways bridges in Spain. • Theoretical and field analysis of some of these Rail Expansion Devices (REDs). • Analysis of the existing REDs of forty-four bridges and viaducts on the Spanish High-Speed Railway. • Recommendations for design, construction and maintenance of REDs. The use of rail expansion devices (RED) interrupts the continuity of the welded rail, causing an irregularity with the consequent reduction in comfort, but with the benefit of a significant stress reduction in the rails. Currently the Spanish Railway Administrator uses three types of RED according to their range: 300 (small RED), 600 (medium RED) and 1200 mm (large RED) for all railway structures in the network. Their use has led to the construction of railway bridges and viaducts of maximum free expansion length of just over 1100 m. Development of new high-speed railway lines increasingly demands longer structures. Thus, the maximum allowed free expansion length should be increased. This problem can be faced from two fronts: (1) the improvement of the performance of REDs by developing new designs and (2) the enhanced prediction of the bridges and viaducts real movements caused by retraction, creep and temperature. It is precisely this last approach that has been developed by analysing and reviewing some existing structures. Forty-four bridges and viaducts on the Spanish High-Speed Railway network. Specifically, the railway Lines Madrid-Barcelona, Cordoba-Malaga and Madrid-Valladolid, have been analysed to know the degree of strain of the existing REDs. With these measurements, the possibility of increasing the length of maximum free expansion of the deck has been examined and it has been proven to be possible as predicted strains were higher than the measured ones. Recommendations given have served to developed longer railway structures in Spain. [ABSTRACT FROM AUTHOR]

This work proposes a supervised Deep Learning approach for damage identification in bridge structures. We employ a hybrid methodology that incorporates Finite Element simulations to enrich the training phase of a Deep Neural Network with synthetic damage scenarios. The neural network is based on autoencoders and its particular architecture allows to activate or deactivate nonlinear connections under need. The methodology intends to contribute to the progress towards the applicability of Structural Health Monitoring practices in full-scale bridge structures. The ultimate goal is to estimate the location and severity of damage from measurements of the dynamic response of the structure. The damages we seek to detect correspond to material degradations that affect wide areas of the structure by reducing its stiffness properties. Our method allows a feasible adaptation to large systems with complex parametrizations and structural particularities. We investigate the performance of the proposed method on two full-scale instrumented bridges, obtaining adequate results for the testing datasets even in presence of measurement uncertainty. Besides, the method successfully predicts the damage condition for two real damage scenarios of increasing severity available in one of the bridges. • Combination of model-based and data-driven approaches for SHM. • Use of autoencoder-based Deep Neural Networks to map the relationship between dynamic response and structural damage. • Orientation of the methodology to complex full-scale bridge structures. • Application of the methodology to two real bridges. [ABSTRACT FROM AUTHOR]