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Classification of novel events for structural health monitoring systems

This article reports on results obtained when applying neural networks to the problem of vehicle classification from SHM measurement data. It builds upon previous work which addressed the issue of reducing vast amounts of data collected during an SHM process by storing only those events regarded as being "interesting," thus decreasing the stored data to a manageable size. This capability is extended here by providing a means to group and classify these novel events using artificial neural network (ANN) techniques. Two types of neural systems are investigated, the first one consists of two neural layers employing both supervised and unsupervised learning. The second, which is an extension of the first, has a data pre-processing stage. In this later system, input data presented to the system is first pre-scaled before being presented to the first network layer. The scaling value is retained and later passed to the second layer as an extra input. The results obtained for vehicle classification using these two methods showed a success rate of 60% and 90% for the first and second ANN systems respectively.