Indirect structural health monitoring (iSHM) of transport infrastructure in the digital age

Workshop reportCopyright © Joint Research Centre (European Commission). The existing European motorway infrastructure network is prone to ageing and subject to natural events (e.g. climate change) and hazards (e.g. earthquakes), necessitating immediate actions for its maintenance and safety. Within this context, the structural health monitoring (SHM) framework allows a quantitative assessment of the structural integrity, serviceability and performance, facilitating better-informed decisions for the management of the existing infrastructure. The European Commission Joint Research Centre (JRC) established the exploratory research project MITICA (Monitoring Transport Infrastructures with Connected and Automated vehicles) to investigate the opportunity to use novel methods for infrastructure motoring, aiming at the efficient maintenance of the European aging road infrastructure. This report summarizes the discussion and the outcomes of a workshop held at the JRC in Ispra (Italy) on June 6-7 2022, as part of the MITICA project. Considering the EU priority “A Europe fit for the digital age”, the workshop was dedicated to SHM and its application to civil infrastructure, focusing on innovative indirect structural health monitoring (iSHM) approaches that rely on the vehicle-bridge interaction and the deployment of sensor-equipped vehicles for the monitoring of the existing bridge infrastructure. The report aims to become a reference document in the area of iSHM using passing vehicles, for both scholars and policy makers

Drive-by Bridge Health Monitoring Using Multiple Passes and Machine Learning

This paper studies a machine learning algorithm for bridge damage detection using the responses measured on a passing vehicle. A finite element (FE) model of vehicle bridge interaction (VBI) is employed for simulating the vehicle responses. Several vehicle passes are simulated over a healthy bridge using random vehicle speeds. An artificial neural network (ANN) is trained using the frequency spectrum of the responses measured on multiple vehicle passes over a healthy bridge where the vehicle speed is available. The ANN can predict the frequency spectrum of any passes using the vehicle speed. The prediction error is then calculated using the differences between the predicated and measured spectrums for each passage. Finally, a damage indicator is defined using the changes in the distribution of the prediction errors versus vehicle speeds. It is shown that the distribution of the prediction errors is low when the bridge condition is healthy. However, in presence of a damage on the bridge, a recognisable change in the distribution will be observed. Several data sets are generated using the healthy and damaged bridges to evaluate the performance of the algorithm in presence of road roughness profile and measurement noise. In addition, the impacts of the training set size and frequency range to the performance of the algorithm are investigated

The calibration challenge when inferring longitudinal track profile from the inertial response of an in-service train

An Irish Rail intercity train was instrumented for a period of one month with inertial sensors. In this paper, a novel calibration algorithm is proposed to determine, with reasonable accuracy, vehicle model parameters from the measured vehicle response data. Frequency domain decomposition (FDD) is used to find the dominant frequencies in the captured data. Randomly chosen 2 km data segments are chosen from a number of datasets, thereby averaging out the effects of variations in track longitudinal profile, track stiffness, signal noise and other unknowns. The remaining dominant peaks are taken to be vehicle frequencies. An optimisation technique known as Cross Entropy is used to find vehicle mass and stiffness properties that best match modal vehicle eigenfrequencies identified in the frequency analysis. Finally, the calibrated vehicle is run over a measured track profile and the resulting model output is compared to measured data to validate the results

Drive-by scour monitoring of railway bridges using a wavelet-based approach

This paper numerically investigates the feasibility of using bogie acceleration measurements from a passing train to detect the presence of bridge scour. The Continuous Wavelet Transform is used to process the simulated acceleration measurements for a number of train passages over a scoured bridge, with scour represented as a local reduction in stiffness at a given pier. Average Wavelet coefficients are calculated for a batch of train runs passing over the same bridge. A scour indicator is developed as the difference in average coefficients between batches from the healthy bridge and when the bridge is damaged by scour. The method is assessed using a blind test, whereby one author simulated trains passing over a bridge in various states of health. The remaining authors were provided only with the train accelerations and had to predict the state of scour without any prior knowledge. This scour indicator performed quite well in the blind test for normal vehicle operating conditions

Railway track loss-of-stiffness detection using bogie filtered displacement data measured on a passing train

This paper presents an innovative numerical framework for railway track monitoring using acceleration measurements from sensors installed on a passenger train. A numerical model including a 10 degrees of freedom train passing over a three-layer track is employed. The bogie filtered displacement (BFD) is obtained from the bogie vertical acceleration using a numerical integration method and a band-pass filter. The BFD is compared to the filtered track longitudinal profile and can be seen to contain the main features of the track profile. This is also experimentally confirmed using field measurements where an in-service Irish Rail train was instrumented using inertial sensors. The proposed algorithm is employed to find the BFDs from the bogie accelerations. A track level survey was also undertaken to validate the measurements. It is shown that the BFDs from several passes are in good agreement with the surveyed profile. Finally, the BFDs are numerically used to find track defects such as hanging sleepers. The mean of the BFDs obtained from two populations of train passes over a healthy and a damaged track are employed to detect the loss of stiffness at the subgrade layer. The effect of the train forward speed variation and measurement noise are also investigated