Vulnerability assessment of existing bridges to scour: an indirect monitoring approach

Detecting scour in railway bridges is possible by locating accelerometers and GPS on carriages of passing trains and processing the resulting signals. This research aims to detect scour based on these drive-by measurements, obtained from an instrumented passing vehicle. Signals from multiple train passages will be collected before and after cour repair to determine the change in bridge behavior. Measurements from a train in the UK passing over the Carlisle Bridge will be provided through In2Track3, an ongoing Horizon 2020 project. In the first stage of the numerical approach, off-bridge conditions are considered. The carriage vibrational responses to track with different ground conditions – represented by altering the stiffnesses in a Winkler spring model – are calculated. In second stage, the bridge ‘apparent profile’(AP), which is made up of the true profile on the bridge plus components of bridge/track deflection, will be computed. The Moving Reference Influence Line, i.e., deflection per unit load at a moving reference point, is found from the measured deflections. Bridge support stiffnesses will be modified to represent the loss of stiffness due to scour. Then, signals from the instrumented in-service train carriage i.e., measured AP, will be processed. Finally, an optimization algorithm will find foundation stiffnesses by minimizing the sum of squared differences between the calculated AP and the corresponding measured AP. The presence of scour will be determined by the difference between the stiffness values in the scoured and repaired cases. The results will help to optimize retrofits or develop mitigation measures to scour.This work was partly financed by FCT / MCTES through national funds (PIDDAC) under the R&D Unit Institute for Sustainability and Innovation in Structural Engineering (ISISE), under reference UIDB / 04029/2020. This work has also been partly financed within the European Horizon 2020 Joint Technology Initiative Shift2Rail through contract no. 101012456 (IN2TRACK3)

Meta-heuristic Optimization Algorithms for Predicting the Scouring Depth Around Bridge Piers

An accurate estimation of bridge pier scour has been considered as one of the important parameters in designing of bridges. However, due to the numerous involved parameters and convolution of this phenomenon, many existing approaches cannot predict scour depth with an acceptable accuracy. Obtained results from the empirical relationships show that these relationships have low accuracy in determining the maximum scour depth and they need a high safety factor for many cases, which leads to uneconomic designs of bridges. To cover these disadvantages, three new models are provided to estimate the bridge pier scour using an adaptive network-based fuzzy inference system. The parameters of the system are optimized by using the colliding bodies optimization, enhanced colliding bodies optimization and vibrating particles system methods. To evaluate the efficiency of the proposed methods, their results were compared with those of simple adaptive network-based fuzzy inference system and its improved versions by using the particle swarm optimization and genetic algorithm as well as the empirical equations. Comparison of results showed that the new vibrating particles system based algorithm could find better results than other two ones. In addition, comparison of the results obtained by the proposed methods with those of the empirical relations confirmed the high performance of the new methods