A Bayesian inverse dynamic approach for impulsive wave loading reconstruction: Theory, laboratory and field application

The measurement of wave forces acting on marine structures is a complicated task, both during physical experiments and, even more so, in the field. Force transducers adopted in laboratory experiments require a minimum level of structural movement, thus violating the main assumption of fully rigid structure and introducing a dynamic response of the system. Sometimes the induced vibrations are so intense that they completely nullify the reliability of the experiments. On-site, it is even more complex, since there are no force transducers of the size and capacity able to measure such massive force intensity acting over the very large domain of a marine structure. To this end, this investigation proposes a Bayesian methodology aimed to remove the undesired effects from the directly (laboratory applications) or indirectly (field applications) measured wave forces. The paper presents three applications of the method: i) a theoretical application on a synthetic signal for which MATLAB® procedures are provided, ii) an experimental application on laboratory data collected during experiments aimed to model broken wave loading on a cylinder upon a shoal and iii) a field application designed to reconstruct the wave force that generated recorded vibrations on the Wolf Rock lighthouse during Hurricane Ophelia. The proposed methodology allows the inclusion of existing information on breaking and broken wave forces through the process-based informative prior distributions, while it also provides the formal framework for uncertainty quantification of the results through the posterior distribution. Notable findings are that the broken wave loading shows similar features for both laboratory and field data. The load time series is characterised by an initial impulsive component constituted by two peaks and followed by a delayed smoother one. The first two peaks are due to the initial impact of the aerated front and to the sudden deceleration of the falling water mass previously upward accelerated by the initial impact. The third, less intense peak, is due to the interaction between the cylinder and remaining water mass carried by the individual wave. Finally, the method allows to properly identify the length of the impulsive loading component. The implications of this length on the use of the impulse theory for the assessment or design of marine structures are discussed

Development and field testing of a vision-based displacement system using a low cost wireless action camera

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this recordThis paper describes development of a contactless, low cost vision-based system for displacement measurement of civil structures. Displacement measurements provide a valuable insight into the structural condition and service behaviour of bridges under live loading. Conventional displacement gauges or GPS based systems have limitations in terms of access to the infrastructure and accuracy. The system introduced in this paper provides a low cost durable alternative which is rapidly deployable in the field and does not require direct contact or access to the infrastructure or its vicinity. A commercial action camera was modified to facilitate the use of a telescopic lens and paired with the development of robust displacement identification algorithms based on pattern matching. Performance was evaluated first in a series of controlled laboratory tests and validated against displacement measurements obtained using a fibre optic displacement gauge. The efficiency of the system for field applications was then demonstrated by capturing the validated bridge response of two structures under live loading including the iconic peace bridge. Located in the City of Derry, Northern Ireland, the Peace Bridge is a 310 m curved self-anchored suspension pedestrian bridge structure. The vision-based results of the field experiment were confirmed against displacements calculated from measured accelerations during a dynamic assessment of the structure under crowd loading. In field applications the developed system can achieve a root mean square error (RMSE) of 0.03 mm against verified measurements

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