Digital coupling of multiscale analyses in modelling and monitoring

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A safe operation of built infrastructure requires robust prognosis models for the prediction of system or structural properties. These prognosis models have continuously to be updated with respect to the current state of the considered existing structures especially if the expected lifetime should cover several decades. Deviations of the modelling assumptions from real situation and uncertain or scattering parameters can only be taken into consideration by such a procedure. This requires, beside a respective structural monitoring system, a suitable numerical model that describes possibly occurring damage scenarios with sufficient accuracy. In this project, a multiscale approach is applied to describe structural damage. Starting point is a numerical model of the complete structure on macroscale. Critical zones that were identified based on monitored data are modelled with a higher resolution on the mesoscale using a hybrid multiscale modelling strategy. The information related to the current structural state that is necessary for local modelling on the mesoscale is obtained by the application of non-destructive testing methods.For the information flow between monitoring system and numerical model, digital coupling has to be established. Therefore the method of theory-driven machine learning is applied in this context. Models describing relations between input and output data can be created from experimental data by means of neuronal networks. However, these data-based models are not necessarily related to physical phenomena. And therefore, can not directly be put into context with the structural behaviour of the considered system.The approach intended to be developed within the project, feeds the meta models not only with information about input and output parameters derived from measured data but also with physics-based relations that are used for the numerical models. In this way, the coupling between experimental investigations and the numerical structural model of a digital twin is established. This coupling does not only allow for the identification of model parameters from measured data. It allows also an information flow in reverse direction to control mobile non-destructive testing equipment based on simulation results obtained by means of the model.The methodology to be developed requires the creation of numerical multiscale models, tools for the numerical coupling as well as experimental analyses of the structure on different scales. In this context, the components of this information chain are first validated with structural elements that can be tested under controlled conditions in the laboratory. In a second step, a validation will be carried out on the reference structure of the DFG priority programme 2388.