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This paper introduces new contributions for construction procedures designed to enhance the robustness and precision of stress control in active anchorage and short presetressing units for long-span bridges, particularly addressing potential technical risks. The primary focus is on optimizing stress management for bridge stays, suspension cables, and short prestressing units by emphasizing a unified parameter: stress. The contributions of this research encompass (1) the introduction of advanced load cells for stress control in active anchorages and (2) the implementation of a novel synchronized multi-strain gage load cell network for short prestressing units, crucial in situations where prestressing losses can attain significant magnitudes. To validate these advancements, the authors present (3) a practical experience and results obtained from applying these methodologies in monitoring the structural response during the construction of the Tajo Bridge using the cable-stayed cantilever technique. [ABSTRACT FROM AUTHOR]

A correct modal analysis of girder bridge decks requires a correct characterisation of the deformation of their cross-section, governed by the longitudinal bending of the girders and the transverse bending of the slab. This paper presents a novel method that allows the modal analysis of girder bridge decks by applying a matrix formulation that reduces the structural problem to one degree of freedom for each girder: the deflection at the centre of the beam span. A parametric study is presented that analyses the structural response of 64 girder bridge decks. The study compares the dynamic structural response obtained by the proposed method with that obtained by traditional grillage calculation methods. The method is experimentally contrasted by a dynamic load test of a full-scale girder bridge. As a result of the analysis, the proposed method reflects adequate convergence with the experimental dynamic structural response. The use of the proposed novel analysis method contributes to the intelligent modelling process for the analysis of the dynamic behaviour of bridges opening the way to easily feed a Digital Twin accelerating the demands of the Decision Support System in real time. [ABSTRACT FROM AUTHOR]

• 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 novel supervised learning approach to identify damage in operating bridge structures. We propose a method to introduce the effect of environmental and operational conditions into the synthetic damage scenarios employed for training a Deep Neural Network, which is applicable to large-scale complex structures. We apply a clustering technique based on Gaussian Mixtures to effectively select Q representative measurements from a long-term monitoring dataset. We employ these measurements as the target response to solve various Finite Element Model Updating problems before generating different damage scenarios. The synthetic and experimental measurements feed two Deep Neural Networks that assess the structural health condition in terms of damage severity and location. We demonstrate the applicability of the proposed method with a real full-scale case study: the Infante Dom Henrique bridge in Porto. A comparative study reveals that neglecting different environmental and operational conditions during training detracts the damage identification task. By contrast, our method provides successful results during a synthetic validation. [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]

Nanoelectromechanical resonators have potential applications in sensing, cooling, and mechanical signal processing. An important parameter in these systems is the strength of coupling the resonator I motion to charge transport through the device. We investigated the mechanical oscillations of a i suspended single-walled carbon nanotube that also acts as a single-electron transistor. The coupling of the mechanical and the charge degrees of freedom is strikingly strong as well as widely tunable (the associated damping rate is ∼3 x 106 Hz). In particular, the coupling is strong enough to drive the oscillations in the nonlinear regime. [ABSTRACT FROM AUTHOR]