Abstract Summary
In numerous regions of the world, buildings and structures are exposed to environmental influences and are thus prone to damage or failure. To design safe structures or assess the reliability of existing structures, the modelling of environmental processes is crucial in engineering, particularly in stochastic dynamics. Examples of such environmental processes that can be described by stochastic processes include earthquake ground motions and wind loads. The power spectral density (PSD) function characterises such processes in the frequency domain and thus determines the signal's dominant frequencies and corresponding amplitudes. When generating a load model described by a PSD function, the uncertainties present in the time signals must be taken into account, which complicates the reliable estimation of such a PSD function. Particularly when only a limited amount of data is available, it is not possible to obtain accurate statistics from this data to derive probabilistic models. In such a case, the specification of bounds representing the limited data set will be practical. In this work, a radial basis function network is utilised to produce basis functions with corresponding weights that are optimised to produce data-enclosing bounds, thus an interval-valued PSD function results. Instead of relying on a discrete or probabilistic representation, the spectral densities at each frequency are described by means of optimised intervals. The proposed method utilises real data records and involves optimising such bounds for the evolutionary PSD function to provide a more realistic description of an environmental process in frequency domain. Advanced interval propagation schemes are linked to the imprecise PSD in a realistic simulation to efficiently determine the reliability of buildings and structures. Thus, the response behaviour and failure probabilities of the structure can be evaluated under consideration of present uncertainties.
