Institute of R&D in Structures and Construction

Funder

Organizational Unit

Publications

Wind-induced fatigue analysis of high-rise guyed lattice steel towers

Publication . Ribeiro, Diogo; Bragança, C.; Montenegro, P.A.; Carvalho, H.; Costa, B.; Marques, F.

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.

2022Journal article

Restricted access

Experimental Validation of a Double-Deck Track-Bridge System under Railway Traffic

Publication . Saramago, Gabriel; Montenegro, Pedro Aires; Ribeiro, Diogo; Silva, Artur; Santos, Sergio; Calçada, Rui

This article describes the experimental and numerical evaluation of the dynamic behaviour of the Cascalheira bridge, located on the Northern Line of the Portuguese railway network. The bridge has a short span formed by two filler-beam half-decks, each one accommodating a railway track. The study includes the development of a finite element numerical model in ANSYS® software, as well as in situ dynamic characterization tests of the structure, namely ambient vibration tests, for the estimation of natural frequencies, modes shapes and damping coefficients, and a dynamic test under railway traffic, particularly for the passage of the Alfa Pendular train. The damping coefficients’ estimation was performed based on the Prony method, which proved effective in situations where the classical methods (e.g., decrement logarithm) tend to fail, particularly in the case of mode shapes with closed natural frequencies, as typically happens with the first vertical bending and torsion modes. The updating of the numerical model of the bridge was carried out using an iterative methodology based on a genetic algorithm, allowing an upgrade of the agreement between the numerical and experimental modal parameters. Particular attention was given to the characterization of the ballast degradation over the longitudinal joint between the two half-decks, given its influence in the global dynamic behavior of this type of double-deck bridges. Finally, the validation of the numerical model was performed by comparing the acceleration response of the structure under traffic actions, by means of numerical dynamic analyses considering vehicle-bridge interaction and including track irregularities, with the ones obtained by the dynamic test under traffic actions. The results of the calibrated numerical model showed a better agreement with the experimental results based on the accelerations evaluated in several measurement points located in both half-decks. In the validation process the vertical stiffness of the supports, as well as the degradation of the ballast located over the longitudinal joint between half-decks, was demonstrated to be relevant for the accuracy and effectiveness of the numerical models.

2022Journal article

Open access

Impact of the train-track-bridge system characteristics in the runnability of high-speed trains against crosswinds - Part II: Riding comfort

Publication . 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.

2022Journal article

Restricted access

Calibration of the numerical model of a freight railway vehicle based on experimental modal parameters

Publication . Ribeiro, Diogo; Bragança, C.; Costa, C.; Jorge, P.; Silva, R.; Arêde, 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 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.

2022Journal article

Open access

Detection of exposed steel rebars based on deep-learning techniques and unmanned aerial vehicles

Publication . Santos, R.; Ribeiro, Diogo; Lopes, Patrícia; 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.

2022Journal article

Open access