Structural Health Monitoring (SHM) is a technique that involves gathering information to ensure that a structure is safe and behaving as expected. Within SHM, vibration-based monitoring is generally seen as one of the more cost-effective types of monitoring. However, vibration-based monitoring has mostly been undertaken on long-span bridges using data collected with a dense network of sensors. Historically, the logistical difficulty of collecting data on short- and medium-span bridges has meant that the usefulness of vibration-based methods on these bridges is largely unknown. Therefore, this study proposes Minimal Information Data-modelling (MID). MID is an approach that utilises low-cost, easily implementable sensors that are potentially feasible for operators to purchase and operate across a network. This approach will be investigated to determine whether MID is a feasible approach for monitoring short- and medium- span bridges. The results from MID were assessed to determine whether they could detect a suitably small shift in frequency, which is indicative of damage. It was determined that the data models could reliably detect frequency shifts as low as 0.01 Hz. This magnitude of frequency shift is similar to the level of frequency shift reported for a range of bridge damage cases found by others and validated with FE models. The accuracy achieved by the data models indicates that MID could potentially be used as a damage detection method. The cost of the equipment used to collect the data was approximately £370, demonstrating that it is feasible to use MID to monitor bridges across an entire network.
One structural health monitoring method used to detect the occurrence of structural damage is the tracking of a structure’s natural frequencies. However, for bridges, this is complicated by the changing environmental and operational conditions, which also have an effect on the natural frequencies. Consequently, much of the research effort has been on trying to develop data-modelling approaches that correct for, or remove, environmental effects so that changes in structural behaviour can be revealed. However, the fact that the process of extracting frequencies from bridge response data sets has in itself some inherent uncertainties that have been largely ignored forms the major interest of this study. In this paper, various methods for extracting frequency data from time-domain signals are reviewed, and their suitability for use in automated approaches is discussed. A selection of these methods was then used to obtain frequencies from continuous acceleration data from a bridge over a 20 d period. Comparisons were then made between the obtained frequencies, and any observed differences are highlighted between the methods.
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