Data-driven reliability assessment of dynamic structures based on power spectrum classification

The power spectral density function is a widely used tool to determine the frequency components and amplitudes of environmental processes, such as earthquakes or wind loads. It is an important technique especially in the engineering field of vibration analysis and in determining the response of structures. When using a large amount of data, a load model can be generated, which describes the characteristics of the underlying stochastic process. This load model enables artificially generated excitations to be created within the framework of Monte Carlo simulations. If multiple data records are utilised, a problem that can occur is that the individual records have a high variance in the frequency domain and are therefore too dissimilar from each other, even though they appear to be similar in the time domain. A load model derived from this data does not represent the entire data set, because not the whole spectral range is covered. Therefore, every attempt must be made to group the records according to their characteristics and thus combine similar data to derive two or more load models accordingly. In this work, an approach is proposed to classify real earthquake ground motion records using the k-means algorithm based on similarities within the data ensemble as determined by the Bhattacharyya distance. The silhouette method enables the identification of the optimal number of groups for the classification. The classified data thus form a subset of the entire data set from which load models can be generated and can be applied separately to the structure under investigation, leading to more accurate simulation results. The advantages of this classification approach are illustrated by means of an academic example and a seismic-isolated bridge pier model as a non-linear dynamic system